Trending November 2023 # 7 Natural Remedies For Ibs # Suggested December 2023 # Top 12 Popular

You are reading the article 7 Natural Remedies For Ibs updated in November 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested December 2023 7 Natural Remedies For Ibs

IBS is a common illness that involves the gut and intestines, generally known as the gastrointestinal system. Common signs include muscle pain, stomach discomfort, bloating, gases, diarrhea, or constipation. IBS is a persistent ailment that takes comprehensive management.

Only a tiny percentage of IBS patients have severe signs. Many individuals could control these signs by controlling their food, behavior, and anxiety. Medicines and therapy can help with increasingly serious problems.

IBS does not produce gut pathological changes or raise your chance of colon cancer.

Symptoms Includes


Abdominal pain

Changes in the look of bowel movements

Changes in the frequency of your bowel movement


It is usual for persons with IBS to suffer from severe constipation and diarrhea. Signs such as swelling and gas usually subside following a bowel motion.

IBS signs are not usually chronic. They can decide only to return. However, many individuals experience ongoing signs.


The precise etiology of IBS is unknown. Aspects that seem to be important involve −

As food moves through your digestive system, sheets of flexing muscles cover the walls of your intestines. Longer-lasting, more complicated contracts might cause bloating, diarrhea, and gas. Food flow may be hindered by inadequate setups, and stiff, dry feces may develop.

Neurological issues in your gastrointestinal system might irritate you if your stomach expands as a result of gas or excrement. Your body may react to changes in the digestion cycle if brain-gut signals are not properly controlled. Constipation, diarrhea, or pain might result from this.

People who have experienced stress, especially as youngsters, are more prone to develop IBS symptoms.

When to Consult a Doctor

Consult your doctor if you experience prolonged changes in bowel movements or any other IBS signs. They might be symptoms of a potentially severe ailment, like colorectal cancer. The following are more significant symptoms −

Losing weight

Pain that is not alleviated by farting or having a bowel movement





Unknown food intolerance or allergies may contribute to IBS. Rarely can an actual food allergy induce IBS? However, a lot of people find that when they consume specific beverages or foods, their IBS symptoms get worse. Examples include grains, dairy items, grapefruits, beans, vegetables, and fizzy beverages.

At times of high stress, many persons with IBS report severe or more regular complaints; however, anxiety might exacerbate symptoms without causing it.

Natural Remedies Include Lowering Stress Levels

Spending time to chill at home might be an excellent IBS therapy because stress is a major cause of IBS problems. The following relaxation methods might help relieve signs: This approach may quiet the malfunctioning impulses of the stomach by concentrating on one portion of the anatomy at the moment and calming it. This and similar meditative methods are beneficial. Deep inhaling techniques also assist in relaxing nerves that are malfunctioning in IBS. Individuals who do more profound inhaling exercises experience fewer IBS signs than individuals who do not.

Exercise Eating more soluble fiber.

Fiber aids digestion and is more readily absorbed by the body. You must pick fiber that is gentle on digestion and does not impede the passage of digestive fluids. Consume more foods with a high concentration of dietary fiber and less refractory fiber, which requires time to digest. Fiber-rich meals such as carrots, beans, nuts, and peanuts are good choices. You can also fulfill your fiber needs with organic products.

Avoiding gassy food

While incorporating additional fiber, avoid food types and selections that might cause gas, i.e., those you are sensitive to. Many of those most regularly used variations we utilize can operate as terrible carriers for viruses, produce gas, and make you feel bloated and uncomfortable. Onions, garlic, and cabbage contain a significant amount of fermenting sugar that serves as a nesting ground for bacteria to grow and disrupt the gut flora.


Better digestion

The immune function has been improved.

Constipation prevention

Treatment of postmenopausal symptoms

The traveler’s diarrhea has been reduced.

Probiotics may also aid in the treatment of stress, stress, high blood cholesterol, and healthy skin. Food, beverages, and pills can help you raise the number of beneficial bacteria in the body. If you don’t want to give up wheat, sourdough is the most excellent alternative for improving your digestion. The fermenting technique reduces gluten levels while increasing nutritional bioavailability in flour. Fermented foods, such as yogurt and buttermilk, are high in helpful microbes. Fermented beverages like kimchi are also high in probiotics. Probiotics can also be added to the diet via nutritional pills in addition to meals. Before beginning a new drug, speak with your physician.


A low FODMAP diet reduces IBS signs naturally. This diet removes foods that include FODMAPs, which are substances that are inadequately digested in the intestine. FODMAP-containing foods include −

Milk, curd, desserts, and cheeses are examples of foodstuffs.

Onion and garlic are examples of veggies.

Honey and sorbitol are examples of sweets.

Wheat products include loaves of bread and noodles.

Apples and cherries are examples of fruits.

The meal is divided into two stages. For the first fourteen days, foods rich in FODMAPs are avoided. Then, each of the individual meals is restored. If you have a response to a specific meal when it is returned, you understand you should avoid it. When starting the low FODMAP diet, you should talk to a dietician. This will guarantee that you acquire sufficient nutrition despite avoiding several items.

Although the low FODMAP diet could be highly beneficial in controlling IBS signs, most individuals choose a treatment choice that does not restrict the things they may and cannot eat.


There are several organic methods to manage irritated bowel sickness signs at home. This eliminates the requirement to acquire over or prescribed drugs. Natural treatments, dietary modifications, exercises, and relaxing methods, especially hypnotherapy, are all examples of harmless and efficient home treatments. All of these act together to return the digestion tract to regular operation. Therefore, getting medical guidance to examine IBS signs is also essential.

You're reading 7 Natural Remedies For Ibs

Introduction To Natural Language Processing And Tokenization

This article was published as a part of the Data Science Blogathon.

Source: Open Source

is a subfield of Artificial intelligence that allows computers to perceive, interpret, manipulate, and reply to humans using natural language.

In simple words, “NLP is the way computers understand and respond to human language.” 

Humans communicate through “text” in a different language. However, machines understand only numeric form. Therefore, there is a need to covert “text” to “numeric form”, making it understandable and computable by machines. Thus, NLP comes into the picture which uses pre-processing and feature encoding techniques like Label encoding, One Hot encoding, etc., converting text into numerical format also known as vectors.

For example, when a customer buys a product from Amazon, they leave a review for it. Now, the computer is not a human who understands the sentiment behind that review. Then, how can a computer understand the sentiment of a review? Here, NLP plays its role.

NLP has applications in language translation, sentiment analysis, grammatical error detection, fake news detection, etc.

Figure 1 provides a complete roadmap of NLP from text preprocessing to using BERT. We will discuss everything about NLP in detail taking use-case

Source: Open Source. Complete RoadMap to Natural Language Processing

In this article, we will focus on the main step of Pre-processing i.e. Tokenization.


Tokenization is the breaking of text into small chunks. Tokenization splits the text (sentence, paragraph) into words, sentences called tokens. These tokens help in interpreting the meaning of the text by analyzing the sequence of tokens.

If the text is split into sentences using some separation technique it is known as sentence tokenization and the same separation done for words is known as word tokenization.

For instance, A review is given by a customer for a product on the Amazon website: “It is very good”. Tokenizer will break this sentence into ‘It’, ‘is’, ‘very’, ‘good’.

Source: Local

There are different methods and libraries available to perform tokenization. Keras, NLTK, Gensim are some of the libraries that can be used to accomplish the task. We will discuss tokenization in detail using each of these libraries.

Tokenization using NLTK

NLTK is the best library for building programs in Python and working with the human language. It provides easy-to-use interfaces, along with a suite of text processing libraries for tokenization, classification, stemming, parsing, tagging, and many more.

This section will help you tokenize the paragraph using NLTK. It will give you a basic idea about tokenizing which can be used in various use cases such as sentiment analysis, question-answering tasks, etc.

So let’s get started:

Note: It is highly recommended to use google colab to run this code.

#1. Import the required libraries

Import nltk library, as we will use it for tokenization.

import nltk'punkt')

#2. Get the Data

Here, a dummy paragraph is taken to show how tokenization is done. However, code can be applied on any text.

paragraph = """I have three visions for India. In 3000 years of our history, people from all over the world have come and invaded us. From Alexander onwards, the Greeks, the Turks, all of them came and looted us, took over what was ours. Yet we have not done this to any other nation. We have not conquered anyone. We have not grabbed their land, their culture, their history and tried to enforce our way of life on them. """

#2. Tokenize paragraph into sentences

Take the paragraph and split it into sentences.

sentences = nltk.sent_tokenize(paragraph)


#4. Tokenize sentence into words

Rather than splitting paragraph into sentences, here, we are breaking it into words.

words = nltk.word_tokenize(paragraph)


Tokenization using Gensim

Gensim is an open source library which was primarily developed for topic modeling. However, it now supports NLP tasks, text similarity and many more.

#1. Import the required libraries.

from gensim.utils import tokenize from gensim.summarization.textcleaner import split_sentences



#2. Tokenize into words



Tokenization using Keras

The third way of tokenization is using Keras library.

#1. Import the required libraries

from chúng tôi import Tokenizer from chúng tôi import text_to_word_sequence

#2 Tokenize

tokenizer = Tokenizer()


train_sequences = text_to_word_sequence(paragraph)



Challenges with Tokenization

There exists a lot of challenges in tokenization. Here, we have discussed a few of them.

The biggest challenge in tokenization is the boundary of words. For example, when we see a space between two words, say “Ram and Shyam”, here we know that three words are involved as space represents the separation of words in English language. However, in other languages, such as Chinese, Japnese, case is not the same.

Another challenge created is by scientific symbols such as µ, α etc. and other symbols such as £, $, €.

Further, a lot of short forms are involved in English language such as didn’t (did not), etc which causes a lot of problems in the next step of NLP.

A lot of research is going in the field of NLP which requires the proper selection of corpora for the NLP task.


The article started with the definition of Natural Language Processing, discussed its use and applications. Then, the entire pipeline of NLP from tokenization to BERT is shown, majorly focusing on “Tokenization” in this article. NLTK, Keras, and Gensim are three libraries used for tokenization which are discussed in detail. At last, the challenges with Tokenization are briefly described.

Read more articles on AV Blog on NLP.

Connect with me on LinkedIn.

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion. 


Pbl And Steam Education: A Natural Fit

Both project-based learning and STEAM (science, technology, engineering, art, and math) education are growing rapidly in our schools. Some schools are doing STEAM, some are doing PBL, and some are leveraging the strengths of both. Both PBL and STEAM help schools target rigorous learning and problem solving. As many teachers know, STEAM education isn’t just the course content—it’s the process of being scientists, mathematicians, engineers, artists, and technological entrepreneurs. Here are some ways that PBL and STEAM can complement each other in your classroom and school.

STEAM Beyond the Acronym

I think one of the pitfalls of STEAM is in the acronym itself. Some might oversimplify STEAM into mastery of the specific content areas. It’s more than that: Students in high-level STEAM work are actively solving problems, taking ownership of their learning, and applying content in real-world contexts. Does that sound like PBL? That’s because it is. High-level STEAM education is project-based learning.

Project-based learning can target one or more content areas. Many PBL teachers start small in their first implementations and pick only a couple of content areas to target. However, as teachers and students become more PBL-savvy, STEAM can be great opportunity to create a project that hits science, math, technology, and even art content. You could also integrate science, art, and the Chinese language, for example—you’re not limited to the subjects in the STEAM acronym.

Embedding Success Skills

Skills like collaboration, creativity, critical thinking, and problem solving are part of any STEAM PBL, and will be needed for students to be effective. Like the overall project, success skills are part of the glue of STEAM education. In a STEAM PBL project, teachers teach and assess one or more of these skills. This might mean using an effective rubric for formative and summative assessment aligned to collaborating, collecting evidence, and facilitating reflection within the PBL project. Although STEAM design challenges foster this kind of assessment naturally as an organic process, PBL can add the intentionality needed to teach and assess the 21st-century skills embedded in STEAM.

For example, a teacher might choose to target technological literacy for a STEAM PBL project, build a rubric in collaboration with students, and assess both formatively and summatively. In addition, the design process, a key component of STEAM education, can be utilized. Perhaps a teacher has a design process rubric used in the PBL project, or even an empathy rubric that leverages and targets one key component of the design process. When creating STEAM projects, consider scaffolding and assessment of these skills to make the project even more successful.

Students Shaping the Learning

In addition to the integration of disciplines and success skills, voice and choice are critical components to STEAM PBL. There are many ways to have students shape the learning experience. They may bring a challenge they want to solve based on their interests—a passion-based method. And students can choose team members and products to produce to solve authentic challenges. In addition, they may be allowed to pick sub-topics within the overall project or challenge, or questions they want to explore within the overall driving question.

Planning Questions

When teachers design STEAM projects, they need to leverage a backward design framework and begin with the end in mind. Here are some questions to consider in planning:

7 Best Antiviruses For Opera Browser

7 Best Antiviruses for Opera Browser Browse the web freely and safely with one of these antivirus programs




For a safe online activity, without infections or passwords stolen, we recommend you use an antivirus alongside Opera.

Our picks are great choices when you’re surfing the web

for shopping, banking, or work.

Freeware coming from some of the apps will block malicious websites, and prevent phishing attacks.

Even if Opera includes security features, it doesn’t offer virus protection. Thus, use any of our top picks to keep your computer safe.

Opera is a popular web browser with an abundance of features. Although this browser isn’t as popular as Chrome or Firefox, it still has a dedicated user base.

If you’re using Opera and you’re looking for antivirus software, today we’re going to show you the best antivirus tools that work with it.

All antiviruses are fully compatible with all web browsers, and the only difference is that some apps come with a dedicated browser extension to scan the websites you visit.

Even though some applications on our list don’t have a dedicated extension for Opera, they offer great security and reliability, so you should try them out regardless.

Does Opera GX have virus protection?

No, Opera GX doesn’t have its own antivirus. Instead, Opera GX provides some amazing features to protect your privacy. It comes with a free VPN that you can use indefinitely.

It’s important to remember that using your browser’s antivirus alone is not enough to protect against phishing, ransomware, and more. We highly recommend using a third-party antivirus for all tasks you do online to virtually eliminate the chance of infection.

ESET Internet Security is one of the best options when you need actively use the Internet for shopping, banking, work, and communication.

This tool is a cross-platform solution, meaning that it can be used to secure multiple types of devices running Windows, Mac, Android, and even Linux.

Moreover, the ransomware shield blocks malware that tries to lock you out of your personal data and then asks you to pay a ransom to unlock it.

It will constantly monitor all the processes and apps on your PC, to see which ones want to use your webcam. It alerts you when they try to access your webcam, and lets you block them.

Furthermore, ESET protects your privacy and assets against attempts by fraudulent websites to acquire sensitive information such as usernames, passwords, or banking details.

Overview of ESET’s most important features:

Ransomware shield

Monitors computer processes 24/7

Blocks malicious websites from stealing your data

Password manager included

ESET Internet Security

If you are concerned about your online privacy, use one of the best antiviruses in addition to Opera.

Free trial Visit Website

A great antivirus for Opera is Bitdefender Total Security. This tool offers protection against all sorts of malware which makes it a complete data protection solution.

The application uses behavioral detection to monitor your apps, and if any suspicious action is detected, you’ll be alerted immediately.

In addition to standard malware, Bitdefender Total Security offers protection against ransomware, and it also provides anti-phishing and anti-fraud detection.

In order to protect your online credentials, Bitdefender Total Security also has a Secure Browsing feature that will scan all your links and search results ensuring their validity.

The application also has a Rescue Mode that is useful in removing rootkits and other malware that can appear before your Windows starts.

Bitdefender Total Security is optimized to be light on your resources, and it also comes with Game, Movie, and Work modes so you can focus on your work without any interference.

It’s also worth mentioning that Bitdefender Total Security has a Global Protective Network feature and it performs heavy scanning in the cloud without affecting your performance.

As for additional features, Bitdefender Total Security has its own secure browser, social network protection, password manager, and file shredder.

Other great features include:

Removes rootkits and other malware that can appear before Windows starts

Behavioral detection to monitor your apps

Scans your search results and blocks infected websites

Provides anti-phishing and anti-fraud detection

Bitdefender Total Security

Protect all your online activities, using Opera together with a revolutionary antivirus.

Check price Get it now

If you want a lightweight antivirus with some of the best browser protection, chúng tôi is the right choice.

A full-featured antivirus browser extension, chúng tôi keeps every browsing session safe and protects your computer from viruses. It can do full scans of your system, and detect and eliminate anything that might pose a threat.

Automatically blocks malicious web pages before you can access them, checks every downloaded file and executable, keeping guard at every door that a virus might get by.

Data Breach Monitor notifies you when your email account is compromised, so you can quickly respond and fix the problem before it happens.

The main key features of using

This antivirus allows you to remove and be notified of malicious extensions that control your browser settings, invade your privacy, or install malware, spyware, and adware.

chúng tôi

Your online guardian when browsing the web keeps your system safe from viruses.

Free Trial Visit Website

Expert tip:

The application comes with a browser extension that can protect your privacy and prevent phishing attacks. In addition, this extension can also block malicious websites.

The extension can also highlight infected websites in your search results, so you’ll never visit a malicious website.

Another feature of Avira is its built-in password manager so all your online passwords will be stored and protected by a master password.

There’s also a software updater tool that checks if your installed applications are up to date. This feature can be useful in finding security vulnerabilities on your PC.

Other important features include:

In addition, this tool can protect your identity online and stop online fraud, identity theft, and phishing attempts. And it also works as an Android antivirus.

The tool also has an anti-theft feature for Android so you can easily locate, lock or wipe your device remotely.

The Parental Control feature can easily block harmful websites. The Data Shield feature allows you to protect your data from unauthorized access. This feature can be useful since it will protect your PC from ransomware.

The premium version includes an additional password manager, and file shredder features. Plus, the file encryption feature easily encrypts and protects your files.

Overview of the most important features:

Blocks harmful websites

Scans your Wi-Fi connection for vulnerabilities

Compatible with both Mac and Windows

Anti-theft feature

Panda Security Essential

For secure, private, and unlimited Internet browsing, don’t hesitate to use Panda Dome.

Free trial Visit Website

Another great anti-malware to use with Opera Browser is Emsisoft’s Anti-Malware tool. This tool offers protection against over 300 000 daily threats.

This tool is designed to work on low-specs PCs and laptops so even if you have an older PC, it will work without any problems.

The most notorious feature is Behaviour Blocker. This tool monitors all downloaded and modified files and blocks or isolates them as soon as it detects something strange.

Emsisoft also uses a dual-engine scan system that enforces your security. Hourly updates keep all-new threats away and it also gives you past logs so you can analyze all threats that have been detected.

This is a great tool to use with opera, especially when the last test shows that Opera isn’t so well-protected. You will find Emsisoft in the link below at a great price.

Other great features that are included:

Anti-Ransomware and anti-phishing protection

Web Protection

Remove unwanted programs

Email & live chat customer support

Emsisoft Anti-Malware

Emsisoft’s solution works great with Opera and will keep you safe every time you surf the web.

Free trial Visit Website

If you need an antivirus for Opera, you might want to consider Avast Antivirus. Just like other tools on our list, Avast will protect you from malware and spyware.

In addition, this tool also has phishing protection that will prevent malicious users from stealing your personal data.

To ensure your safety, Avast will send suspicious files for analysis to the cloud for scanning. The application has a Wi-Fi Inspector feature that will scan your Wi-Fi network for any weaknesses.

In addition to Wi-Fi, thanks to Smart Scan feature you can easily find other vulnerabilities on your PC. Lastly, the application has its own password manager.

The Ransomware shield, as well as the Sandbox feature, will run suspicious files in a safe environment without affecting your PC.

Additional features include a passive mode that allows you to use another antivirus alongside, and a Browser Cleanup tool that will remove unwanted extensions, toolbars, and add-ons.

Other great features include:

Ransomware protection

Scans your Wi-Fi for vulnerabilities

Remove unwanted extensions, toolbars, and add-ons

Malware and spyware protection

Avast Free Antivirus

Don’t miss out on this great antivirus that will keep you protected 24/7.

Free Visit Website

If you’re looking for an antivirus to use together with for Opera browser, feel free to try any application from our list.

Moreover, you can check our guide including the best antiviruses for Windows 10 if you want to discover even more solutions to keep your computer safe.

Was this page helpful?


Start a conversation

Introduction To Automatic Speech Recognition And Natural Language Processing

This article was published as a part of the Data Science Blogathon.


Photo by Clem Onojeghuo on Unsplash

What makes speech recognition hard?

Like many other AI problems we’ve seen, automatic speech recognition can be implemented by gathering a large pool of labeled data, training a model on that data, and then deploying the trained model to accurately label new data. The twist is that speech is structured in time and has a lot of variabilities.

We’ll identify the specific challenges we face when decoding spoken words and sentences into text. To understand how these challenges can be met, we’ll take a deeper dive into the sound signal itself as well as various speech models. The sound signal is our data. We’ll get into the signal analysis, phonetics, and how to extract features to represent speech data.

Types of Models in Speech Recognition Challenges in Automatic Speech Recognition

Continuous speech recognition has had a rocky history. In the early 1970s, the United States funded automatic speech recognition research with a DARPA challenge. The goal was achieved a few years later by Carnegie-Mellon’s Harpy System. But the future prospects were disappointing and funding dried up. More recently computing power has made larger dimensions in neural network modeling a reality. So what makes speech recognition hard?

Photo by Jason Rosewell on Unsplash

The first set of problems to solve are related to the audio signal itself, noise for instance. Cars going by, clocks ticking, other people talking, microphones static, our ASR has to know which parts of the audio signal matter and which parts to discard. Another factor is the variability of pitch and variability of volume. One speaker sounds different than another even when saying the same word. The pitch and loudness at least in English don’t change the ground truth of which word was spoken.

If I say hello in a different pitch, it’s all the same word and spelling. We could even think of these differences as another kind of noise that needs to be filtered out. Variability of word speed is another factor. Words spoken at different speeds need to be aligned and matched. If I give a speech at a different speed, it’s still the same word with the same number of letters.

Aligning the sequences of sound correctly is done by ASR. Also, word boundaries are an important factor. When we speak, words run from one another without a pause. We don’t separate them naturally. Humans understand it because we already know that the word boundaries should be in certain places. This brings us to another class of problems that are language or knowledge related.

We have domain knowledge of our language that allows us to automatically sort out ambiguities as we hear them. Word groups that are reasonable in one context but not in another.

Photo by Ben White on Unsplash

Also, spoken language is different than written language. There are hesitations, repetitions, fragments of sentences, slips of the tongue, a human listener is able to filter this out. Imagine a computer that only knows language from audiobooks and newspapers read aloud. Such a system may have a hard time decoding unexpected sentence structures. Okay, we’ve identified lots of problems to solve here.

Some are the variability of the pitch, volume, and speed, ambiguity due to word boundaries, spelling, and context. I am going to introduce some ways to solve these problems with a number of models and technologies. I’ll start at the beginning with the voice itself.

Signal Analysis

When we speak we create sinusoidal vibrations in the air. Higher pitches vibrate faster with a higher frequency than lower pitches. These vibrations can be detected by a microphone and transduced from acoustical energy carried in the sound wave, to electrical energy where it is recorded as an audio signal. The amplitude in the audio signal tells us how much acoustical energy is in the sound, how loud it is. Our speech is made up of many frequencies at the same time. The actual signal is really a sum of all those frequencies stuck together. To properly analyze the signal, we would like to use the component frequencies as features. We can use a fourier transform to break the signal into these components. The FFT algorithm or Fast Fourier Transform, is widely available for this task.

We can use this splitting technique to convert the sound to a Spectrogram. To create a Spectrogram first, divide the signal into time frames. Then split each frame signal into frequency components with an FFT. Each time frame is now represented with a vector of amplitudes at each frequency. If we line up the vectors again in their time-series order, we can have a visual picture of the sound components, the Spectrogram.

Photo by Jacek Dylag on Unsplash

The Spectrogram can be lined up with the original audio signal in time. With the Spectrogram, we have a complete representation of our sound data. But we still have noise and variability embedded into the data. In addition, there may be more information here than we really need. Next, we’ll look at Feature Extraction techniques to, both, reduce the noise and reduce the dimensionality of our data.

Feature Extraction What part of the audio signal is really important for recognizing speech?

One human creates words and another human hears them. Our speech is constrained by both our voice-making mechanisms and what we can perceive with our ears. Let’s start with the ear and the pitches we can hear.

The Mel Scale was developed in 1937 and tells us what pitches human listeners can truly discern. It turns out that some frequencies sound the same to us but we hear differences in lower frequencies more distinctly than in higher frequencies. If we can’t hear a pitch, there is no need to include it in our data, and if our ear can’t distinguish two different frequencies, then they might as well be considered the same for our purposes.

For the purposes of feature extraction, we can put the frequencies of the spectrogram into bins that are relevant to our own ears and filter out the sound that we can’t hear. This reduces the number of frequencies we’re looking at by quite a bit. That’s not the end of the story though. We also need to separate the elements of sound that are speaker-independent. For this, we focus on the voice-making mechanism we use to create speech. Human voices vary from person to person even though our basic anatomy features are the same. We can think of a human voice production model as a combination of source and filter, where the source is unique to an individual and the filter is the articulation of words that we all use when speaking.

Photo by Abigail Keenan on Unsplash

The cepstral analysis relies on this model for separating the two. The main thing to remember is that we’re dropping the component of speech unique to individual vocal cords and preserving the shape of the sound made by the vocal tract. The cepstral analysis combined with mel frequency analysis gets you 12 or 13 MFCC features related to speech. Delta and Delta-Delta MFCC features can optionally be appended to the feature set. This will double or triple the number of features but has been shown to give better results in ASR. The takeaway for using MFCC feature extraction is that we greatly reduce the dimensionality of our data and at the same time we squeeze noise out of the system. Next, we’ll look at the sound from a language perspective, the phonetics of the words we hear.


Phonetics is the study of sound in human speech. Linguistic analysis of language around the world is used to break down human words into their smallest sound segments. In any given language, some number of phonemes define the distinct sounds in that language. In US English, there are generally 39 to 44 phonemes to find. A Grapheme, in contrast, is the smallest distinct unit that can be written in a language. In US English the smallest grapheme set we can define is a set of the 26 letters in the alphabet plus space. Unfortunately, we can’t simply map phonemes to a grapheme or individual letters because some letters map to multiple phonemes sounds, and some phonemes map to more than one letter combination.

For example, in English, the C letter sounds different in cat, chat, and circle. Meanwhile, the phoneme E sound we hear in receive and beat is represented by different letter combinations. Here’s a sample of a US English phoneme set called Arpabet. Arpabet was developed in 1971 for speech recognition research and contains thirty-nine phonemes, 15 vowel sounds, and 24 consonants, each represented as a one or two-letter symbol.

Phonemes are often a useful intermediary between speech and text. If we can successfully produce an acoustic model that decodes a sound signal into phonemes the remaining task would be to map those phonemes to their matching words. This step is called Lexical Decoding and is based on a lexicon or dictionary of the data set. Why not just use our acoustic model to translate directly into words?

Why take the intermediary step?

That’s a good question and there are systems that do translate features directly to words. This is a design choice and depends on the dimensionality of the problem. If we want to train a limited vocabulary of words we might just skip the phonemes, but if we have a large vocabulary converting to smaller units first, reduces the number of comparisons that need to be made in the system overall.

Voice Data Lab Introduction

We’ve learned a lot about speech audio. We’ve introduced signal analysis and feature extraction techniques to create data representations for that speech audio. Now, we need a lot of examples of audio, matched with text, the labels, that we can use to create our dataset. If we have those labeled examples, say a string of words matched with an audio snippet, we can turn the audio into spectrograms or MFCC representations for training a probabilistic model.

Fortunately for us, ASR is a problem that a lot of people have worked on. That means there is labeled audio data available to us and there are lots of tools out there for converting sound into various representations.

One popular benchmark data source for automatic speech recognition training and testing is the TIMIT Acoustic-Phonetic Corpus. This data was developed specifically for speech research in 1993 and contains 630 speakers voicing 10 phoneme-rich sentences each, sentences like, ‘George seldom watches daytime movies.’ Two popular large vocabulary data sources are the LDC Wall Street Journal Corpus, which contains 73 hours of newspaper reading, and the freely available LibriSpeech Corpus, with 1000 hours of readings from public domain books. Tools for converting these various audio files into spectrograms and other feature sets are available in a number of software libraries.

Acoustic Models And the Trouble with Time

With feature extraction, we’ve addressed noise problems due to environmental factors as well as the variability of speakers. Phonetics gives us a representation of sounds and language that we can map to. That mapping, from the sound representation to the phonetic representation, is the task of our acoustic model. We still haven’t solved the problem of matching variable lengths of the same word. DTW calculates the similarity between two signals, even if their time lengths differ. This can be used in speech recognition, for instance, to align the sequence data of a new word to its most similar counterpart in a dictionary of word examples.

As we’ll soon see, hidden Markov models are well-suited for solving this type of time series pattern sequencing within an acoustic model, as well. This characteristic explains their popularity in speech recognition solutions for the past 30 years. If we choose to use deep neural networks for our acoustic model, the sequencing problem reappears. We can address the problem with a hybrid HMM/DNN system, or we can solve it another way.

Photo by Andy Kelly on Unsplash

Later, we’ll talk about how we can solve the problem in DNNs with connectionist temporal classification or CTC. First, though, we’ll review HMMs and how they’re used in speech recognition.

HMM’s in Speech Recognition

We learned the basics of hidden Markov models. HMMs are useful for detecting patterns through time. This is exactly what we are trying to do with an acoustic model. HMMs can solve the challenge, we identified earlier, of time variability. For instance, my earlier example of speech versus speech, the same word but spoken at different speeds. We could train an HMM with label time series sequences to create individual HMM models for each particular sound unit. The units could be phonemes, syllables, words, or even groups of words. Training and recognition are fairly straightforward if our training and test data are isolated units.

We have many examples, we train them, we get a model for each word. Then recognition of a single word comes down to scoring the new observation likelihood over each model. It gets more complicated when our training data consists of continuous phrases or sentences which we’ll refer to as utterances. How can the series of phonemes or words be separated in training?

In this example, we have the word brick, connected continuously in nine different utterance combinations. To train from continuous utterances HMMs can be tied together as pairs. We define these connectors as HMMs. In this case, we would train her brick, my brick, a brick, brick house, brick walkway, and brick wall, by tying the connecting states together. This will increase dimensionality. Not only will we need an HMM for each word, but we also need one for each possible work connection, which could be a lot if there are a lot of words.

The same principle applies if we use phonemes. But for large vocabularies, the dimensionality increase isn’t as profound as with words. With a set of 40 phonemes, we need 1600 HMMs to account for the transitions. Still a manageable number. Once trained, the HMM models can be used to score new utterances through chains of probable paths.

Language Models

So far, we have tools for addressing noise and speech variability through our feature extraction. We have HMM models that can convert those features into phonemes and address the sequencing problems for our full acoustic model. We haven’t yet solved the problems in language ambiguity though. With automatic speech recognition, the goal is to simply input any continuous audio speech and output the text equivalent. The system can’t tell from the acoustic model which combinations of words are most reasonable.

That requires knowledge. We either need to provide that knowledge to the model or give it a mechanism to learn this contextual information on its own. We’ll talk about possible solutions to these problems, next.

N Grams

The job of the Language Model is to inject the language knowledge into the words to text step in speech recognition, providing another layer of processing between words and text to solve ambiguities in spelling and context. For example, since an Acoustic Model is based on sound, we can’t distinguish the correct spelling for words that sound the same, such as hear. Other sequences may not make sense but could be corrected with a little more information.

The words produced by the Acoustic Model are not absolute choices. They can be thought of as a probability distribution over many different words. Each possible sequence can be calculated as the likelihood that the particular word sequence could have been produced by the audio signal. A statistical language model provides a probability distribution over sequences of words.

If we have both of these, the Acoustic Model and the Language Model, then the most likely sequence would be a combination of all these possibilities with the greatest likelihood score. If all possibilities in both models were scored, this could be a very large dimension of computations.

We can get a good estimate though by only looking at some limited depth of choices. It turns out that in practice, the words we speak at any time are primarily dependent upon only the previous three to four words. N-grams are probabilities of single words, ordered pairs, triples, etc. With N-grams we can approximate the sequence probability with the chain rule.

The probability that the first word occurs is multiplied by the probability of the second given the first and so on to get probabilities of a given sequence. We can then score these probabilities along with the probabilities from the Acoustic Model to remove language ambiguities from the sequence options and provide a better estimate of the utterance in text.

A New Paradigm

The previous discussion identified the problems of speech recognition and provided a traditional ASR solution using feature extraction HMMs and language models. These systems have gotten better and better since they were introduced in the 1980s.

But is there a better way?

As computers become more powerful and data more available, deep neural networks have become the go-to solution for all kinds of large probabilistic problems including speech recognition. In particular, recurrent neural networks RNNs can be leveraged, because these types of networks have temporal memory, an important characteristic for training and decoding speech. This is a hot topic and an area of active research.

The information that follows is primarily based on recent research presentations. The tech is bleeding edge, and changing rapidly but we’re going to jump right in. Here we go.

Deep Neural Networks as Speech Models

If HMM’s work why do we need a new model. It comes down to potential. Suppose we have all the data we need and all the processing power we want. How far can an HMM model take us, and how far could some other model take us?

According to Baidu’s Adam Coates in a recent presentation, additional training of a traditional ASR level off inaccuracy. Meanwhile, Deep Neural Network Solutions are unimpressive with small data sets but they shine as we increase data and model sizes. Here’s the process we’ve looked at so far. We extract features from the audio speech signal with MFCC. Use an HMM acoustic model to convert to sound units, phonemes, or words. Then, it uses statistical language models such as N-grams to straighten out language ambiguities and create the final text sequence. It’s possible to replace the many tune parts with a multiple layer deep neural network. Let’s get a little intuition as to why they can be replaced.

In feature extraction, we’ve used models based on human sound production and perception to convert a spectrogram into features. This is similar, intuitively, to the idea of using Convolutional Neural Networks to extract features from image data. Spectrograms are visual representations of speech. So, we ought to be able to let CNN find the relevant features for speech in the same way. An acoustic model implemented with HMMs includes transition probabilities to organize time series data. Recurrent Neural Networks can also track time-series data through memory, as we’ve seen in RNNs.

The traditional model also uses HMMs to sequence sound units into words. The RNNs produce probability densities over each time slice. So we need a way to solve the sequencing issue. A Connectionist Temporal Classification layer is used to convert the RNN outputs into words. So, we can replace the acoustic portion of the network with a combination of RNN and CTC layers. The end-to-end DNN still makes linguistic errors, especially on words that it hasn’t seen in enough examples. The existing use of N-grams can be made. Alternately, a Neural Network Language Model can be trained on massive amounts of available text. Using an NLM layer, the probabilities of spelling and context can be re-scored for the system.


We’ve covered a lot of ground. We started with signal analysis taking apart the sound characteristics of the signal, and extracting only the features we required to decode the sounds and the words. We learned how the features could be mapped to sound representations of phonemes with HMM models, and how language models increase accuracy when decoding words and sentences.

Finally, we shifted our paradigm and looked into the future of speech recognition, where we may not need feature extraction or separate language models at all. I hope you’ve enjoyed learning this subject as much as I’ve enjoyed writing it 😃


1. Introduction to Stemming vs Lemmatization (NLP)

2. Introduction to Word Embeddings (NLP)

About Me

With this, we have come to the end of this article. Thanks for reading this and following along. Hope you loved it! Bundle of thanks for reading it!

My Portfolio and Linkedin 🙂

The media shown in this article are not owned by Analytics Vidhya and at the Author’s discretion.


7 Musts For A Successful Youtube Channel

YouTube is the second largest search engine on the web, right behind Google. And by now, you probably know that Google owns YouTube.

So, as social media managers and SEO professionals, YouTube is a platform we cannot afford to ignore.

An optimized channel is the foundation of successful content.

Some of these optimizations are no-brainers. Others tend to get overlooked.

What follows are seven musts for a successful YouTube Channel.

1. Channel Banner

Once people get to your YouTube Channel, the first thing they see is your channel banner.

A channel banner is a piece of creative that runs across the top of your channel.

The desired specs for a YouTube channel banner is 2,560 x 1,440 pixels, but keep in mind the “safe area” is 1,546 x 423 pixels – so all content should be kept within that middle section.

Create Your Ideal YouTube Channel Banner

Ideally, your channel banner will tell people what kind of videos they can expect and when they can expect them.

Or, if YouTube is not your primary social platform, you may want to put your other social media handles on your channel banner instead.

However, you don’t want so much information on the channel banner that people don’t read it all, so keep it simple!

Here’s an example from Roger Wakefield’s plumbing channel.

2. Introduction Video

Upon entering a channel, a set introduction video will start auto-playing under the channel banner, and it is the largest video on the screen.

Better yet, the first portion of the description of the video you set will also be shown on your channel home page.

This is a great place to tell people a little more about yourself and your channel.

This introduction video from Cass Thompson’s YouTube channel does just that.

3. Optimized Playlists

Now, the other things shown on your home page are playlists.

Playlists are defined groups of video that are selected, and named, by the channel owner. They are a great way to group your content and answer all of the questions around a specific topic or keyword.

Think of playlists and their titles/descriptions as pillar content.

You want to title your playlist the broad keyword you’d like to rank for, then add a description that includes long-tail or secondary keywords.

All of the videos you add to this playlist should be related to the larger topic you want your videos to rank for.

Optimized YouTube Playlist Example

Nextiva has done a great job creating videos for the keyword Connected Communications.

To date, this playlist features 20 videos, all of which answer a specific question around connected communications.

Some of the videos have thousands of views, while others have just a hundred or so.

But, when looking at the SERP, you’ll see that these videos have really paid off.

4. Defined Channel Keywords

YouTube is like Google. It relies on user-generated signals to determine who to show videos to and when to show them videos.

One of the ways you can help YouTube understand your content and who it should be served to is by defining your channel keywords.

This is a step that gets skipped rather often because it’s not the easiest setting to find.

How to Set YouTube Channel Keywords

Go to YouTube Studio.

Select Settings.

From the menu, toggle to Channel.

Set your keywords.

You don’t need to add a million keywords here but instead, focus on 5 to 10 important keywords that describe your channel.

Backlinko did a study that found you don’t want to use more than 50 characters in this section.

5. Custom URL

The magic number is 100.

At 100 subscribers you are able to get the coveted custom URL.

The custom URL is useful for one major reason – it makes it much easier to link to your YouTube channel.

Setting your custom URL only becomes available once you hit 100 subscribers, have a 30-day old channel, and have set a profile and channel banner photo.

6. Channel Description

Your channel description is one of the other signals YouTube relies on to determine what your content is about and who it should be served to.

However, it’s also used to tell your audience what they can expect from your channel both in content and results.

This space should be used to list the topics you will be covering, using keywords that your audience may use to search for your content.

When writing your channel description, it’s most important to take into consideration the first 100-150 characters of your description.

These characters are often what you will have to rely on to catch the audience’s attention in the search results.

7. ‘Connect with Me’ Template

The last thing to consider is creating a “connect with me” template to include in all of your video descriptions.

Now, this template isn’t always used to actually encourage people to actually connect with you, instead, it should be used to get people to interact with you.

These interactions could include things like:

What video to watch next.

What content to read on your website.

Links to the tools you use.

Online courses you may offer.

Links to your social channels.

A link for people to subscribe to your channel.

A brief description of who you are and what you offer.

You can create a template for this portion of your video description that you can use on every video created.

Above is an example of Shopify’s version of a “connect with me” template. You will see a version of this on almost all of their videos.

Start Building a Successful YouTube Channel

The listed optimizations shouldn’t take you more than a day to complete – so what are you waiting for?

More Resources:

Image Credits

All screenshots taken by author, November 2023

Update the detailed information about 7 Natural Remedies For Ibs on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!