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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?

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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.

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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.

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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.

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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.

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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)

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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.

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Windows Speech Recognition Doesn’t Work

Speech recognition is a relatively new but important feature in Windows computers. This option allows you to voice type and give commands to use applications in Windows. However, in some cases, the Speech Recognition Setup may not work. If speech recognition doesn’t work on your Windows 11/10 computer, then please read this article for the resolutions.

Voice recognition not working

Speech Recognition helps you do various things on your computer. From opening a program to dictate text in any text editor – you can do everything using this functionality. However, if it doesn’t work on your computer, the following suggestions may be handy for you.

Windows Speech Recognition doesn’t work

The main causes of Speech Recognition not working are hardware related issues, software/system permissions, missing or corrupt system files, issues with drivers, etc. If you encounter this problem on your computer, then please try the following solutions sequentially to fix the same:

Re-enable Speech Recognition

Check for hardware related issues

Check if the correct microphone is connected

Check for microphone permissions

Increase the input volume of your microphone

Check speech language

Turn on Online Speech Recognition using Registry

Change Group Policy settings

Disable hotkey changer software

Reinstall microphone (if external)

Run the Recording audio troubleshooter

Perform SFC scan

Troubleshoot in Clean Boot State

Miscellaneous solutions

1] Re-enable Speech Recognition

Even if you have turned on the Speech Recognition in Windows Settings, a bug or glitch could disable it automatically. It happens when you use a Beta or Dev Channel build. That is why it is recommended to verify the setting or re-enable it in Windows Settings.

To re-enable Speech Recognition in Windows 11, follow these steps:

Press Win+I to open Windows Settings.

Switch to the Accessibility tab.

Toggle the Windows Speech Recognition button to turn it on.

Then, check if you can use Speech Recognition on your computer or not.

2] Check for hardware related issues 3] Check if the correct microphone is connected

Ideally, the preferred microphone for any function on the computer is the default microphone of the laptop and if it is not attached then you would have to attach an external one. In the latter case, a Windows computer may or may not detect an external microphone. To confirm this, try the following.

Now scroll down to the Input section and check which microphone is connected and currently in use. It can be judged by checking the radio button.

4] Check for microphone permissions

In the Settings window, go to the Privacy and Security tab on the list on the left-hand side.

Turn the switches on for Microphone access and Let your apps access the microphone.

Also make sure that the switch is turned ON for the application for which you need the speech recognition.

5] Increase the input volume of your microphone

Go to the System tab on the list on the left-hand side and then go to the Sound option on the list on the right-hand side.

Scroll down to the Input section and you can use the slider to increase the volume.

6] Check speech language

Another reason for your software not recognizing the speech to the microphone could be that you might have selected the wrong language for speech recognition. This can be checked and fixed as follows.

Go to the Time and Language tab on the list on the left-hand side.

Check the speech language and change it if is incorrect.

Although Speech Recognition works with many languages, if you understand English, it is recommended to set English as the primary language. At times, a bug or glitch may block you from using speech recognition when you use any language other than English. That is why it is recommended to follow this guide to change Windows language back to English.

7] Turn on Online Speech Recognition using Registry

If you get this problem with Online Speech Recognition, you must verify the Registry settings. It is possible to enable or disable online Speech Recognition using Windows Registry. If you deactivated this feature in the past, you might encounter the aforementioned problem. That is why follow these steps to turn on Online Speech Recognition using Registry:

Press Win+R to open the Run prompt.

Navigate to this path: HKEY_CURRENT_USERSoftwareMicrosoftSpeech_OneCoreSettingsOnlineSpeechPrivacy

Set the name as HasAccepted.

Close all windows and restart your PC.

After that, you can use online Speech Recognition without any error.

8] Change Group Policy settings

There is a Group Policy setting that helps you prevent or block users from enabling Speech Recognition on Windows 11/10 PC. If you enabled this setting earlier, you could not turn on the same option from Windows Settings. That is why follow the following steps to allow the user to enable online speech recognition:

Search for  chúng tôi  in the Taskbar search box.

Choose the Enabled or Not Configured option.

After that, you can turn on or off online speech recognition without any problem.

9] Disable hotkey changer software

Windows 11/10 allows users to activate Speech Recognition using a hotkey, Win+Ctrl+S. However, if you have used the same keyboard shortcut to open something else or execute a different command, you won’t be able to use Speech Recognition. That is why it is suggested to check the hotkey changer software or keyboard shortcut changer software minutely.

10] Reinstall microphone (if external)

If you use an external microphone, it is recommended to reinstall it. You can do the following:

Unplug the microphone from your computer first. Restart your computer and re-plug it in.

If you haven’t installed the driver, it is suggested to do that. However, if you have already installed the corresponding driver, you can remove it first and reinstall the same.

Verify if your microphone is in working condition. You can use the same microphone with another computer.

11] Run the Recording audio troubleshooter

The Recording Audio troubleshooter is an excellent tool to check for problems related to microphone and speech recognition. You can run it as follows.

In the Settings window, go to the System tab on the list on the left-hand side.

From the list of troubleshooters, select Run corresponding to the Recording Audio troubleshooter.

12] Run SFC scan

If everything else fails, it could be quite possible that the system files are missing or corrupt. In this case, you can consider performing an SFC scan on your computer. The SFC scan will replace the missing and corrupt files and fix the problem of speech recognition not working.

13] Troubleshoot in Clean Boot State

It is quite possible that an external software might be interfering with speech recognition on the intended software. This case can be isolated by troubleshooting the computer in the clean boot state. While in the clean boot state, no other third-party software will launch at the startup itself. In this case, you can identify the problematic software and halt it while using the speech recognition function.

14] Miscellaneous solutions

You can also try solutions like moving to a quieter place, using an external microphone instead of your laptop’s microphone, updating drivers, etc.

Read: How to disable Speech Recognition feature in Windows

How do I turn on my microphone?

First of all, the hardware of the microphone should be plugged and turned on. Some external microphones may have a switch to turn them on but most of them are simply plug and play. If your external microphone requires a separate power supply, make sure that the same is connected. Usually, windows will recognize the hardware and it will be turned on automatically. If that does not happen, you can use the Realtek audio player or Windows Settings to turn it ON manually.

Read: Best Speech Recognition software for Windows 11/10

What is a microphone used for?

A microphone is simply an audio input device for sending an audio input to the computer. The audio input can be used for recording, voice typing, instructing the system, etc. Usually, these days laptops come with inbuilt microphones.

Windows Speech Recognition is not available for the current display language

If you get Windows Speech Recognition is not available for the current display error in Windows 11/10, you need to set English as the default language. If you have used any regional language or anything else as the primary display language, you may encounter the aforementioned error on your computer.

That is why follow these steps to set English as the default Windows display language in Windows 11:

Press Win+I to open Windows Settings.

Select the Language & region menu.

Expand the Windows display language drop-down menu.

Select English from the list.

Following that, you can choose it from the drop-down menu.

How do I set up voice recognition on Windows 11? Why is my Windows Speech Recognition not working?

There are many reasons why Speech Recognition might not be working on your computer. For example, if it is turned off in the Windows Settings panel, you cannot use it by pressing the Win+Ctrl+S shortcut. On the other hand, it could be a microphone issue as well. If you use a third-party app, an internal conflict can also cause the same issue.

Does Windows 11 have a talk-to-text feature?

Yes, like Windows 10, Windows 11 also has a talk to text features included. For that, you do not need to install third-party programs or services. You can press Win+H to open the corresponding panel and start talking. Everything will be typed automatically in any text editing or word processing application.

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:

The Natural Guest Room Hub To Enhance Hotel Operations And Loyalty

Tech-savvy consumers know that a Smart TV is more than just a device with which to watch TV shows and movies, but it has taken some hoteliers time to catch up with the paradigm shift. Smart TV technology actually transforms the entire purpose of a television set from a hotel operations perspective. It turns what used to be a single-purpose screen for entertainment into a natural hub that unlocks revenue and operational efficiency opportunities in the guest room. In addition to entertainment features, Smart TVs can integrate more fully with content management systems, unlocking richer features and productivity benefits. Hotels that aren’t using this technology are losing productivity and revenue and likely aren’t meeting guest expectations.

Since Smart TVs require an internet connection that’s more reliable than wireless — a capability not all hotels have —the majority of properties haven’t been able to benefit from the features of Smart TVs. However, the new Samsung 694 Series Hospitality Television has an embedded DOCSIS® 3.0 cable modem and doesn’t need an external modem or wired Ethernet. This allows hotels with coax cable — the majority of properties — to deploy Smart TVs in their guest rooms.

Smart TV sets and remote content management systems offer the following benefits to hotels and their guests:

Remote TV management. The days of having to visit each room individually to make adjustments to televisions are over. You can now manage all televisions remotely and perform all administrative tasks from a central location.

Real-time facility and event information. Instead of having a dry listing of events in the lobby or generic facility information on an information channel, Smart TVs can include real-time information that allows you to increase revenue. If you see that reservations are down in a restaurant for the evening, you can begin promoting that restaurant and even include a coupon code on your Smart TVs.

Remote checkout. Guests hate to stand in line at the front desk and are often confused about whether they even need to check out. The printed bills typically placed under guests’ doors also don’t include charges after a certain time. But by using the remote checkout feature on the Smart TV, guests can see late night drinks and breakfast as well as their room charges, allowing them to save time and reducing the line at the front desk.

Room service ordering. Instead of having to pick up the phone and fumble through a paper menu to order room service, Smart TVs let guests order room service online with a real-time menu. This lets you remove items that you’re out of and include nightly specials.

Booking amenities. The same strategy applies to amenities such as spa services and tee times. Guests can see the open schedule and prices in real time to book their appointments without having to make a call and have the employee go through all the options, which is easier for your guests and more productive for your staff.

Upgrading your guest rooms with Smart TVs can impact many aspects of your hotel, especially hotel operations and customer satisfaction. The Samsung LYNK® REACH and H-Browser content management solutions offer specialized capabilities to give guests customized viewing options. Hotels that adopt this technology now will be leaders in the industry, but those that wait will quickly find themselves behind the curve.

Learn more about the features of the Samsung 694 Series Hospitality TV and how it can enhance the guest experience and improve hotel operations.

The 8 Parts Of Speech

A part of speech (also called a word class) is a category that describes the role a word plays in a sentence. Understanding the different parts of speech can help you analyze how words function in a sentence and improve your writing.

Many words can function as different parts of speech depending on how they are used. For example, “laugh” can be a noun (e.g., “I like your laugh”) or a verb (e.g., “don’t laugh”).


A noun is a word that refers to a person, concept, place, or thing. Nouns can act as the subject of a sentence (i.e., the person or thing performing the action) or as the object of a verb (i.e., the person or thing affected by the action).

There are numerous types of nouns, including common nouns (used to refer to nonspecific people, concepts, places, or things), proper nouns (used to refer to specific people, concepts, places, or things), and collective nouns (used to refer to a group of people or things).

Examples: Nouns in a sentenceI’ve never read that



Ella lives in France.

The band played only new songs.

Other types of nouns include countable and uncountable nouns, concrete nouns, abstract nouns, and gerunds.

NoteProper nouns (e.g., “New York”) are always capitalized. Common nouns (e.g., “city”) are only capitalized when they’re used at the start of a sentence.


A pronoun is a word used in place of a noun. Pronouns typically refer back to an antecedent (a previously mentioned noun) and must demonstrate correct pronoun-antecedent agreement. Like nouns, pronouns can refer to people, places, concepts, and things.

There are numerous types of pronouns, including personal pronouns (used in place of the proper name of a person), demonstrative pronouns (used to refer to specific things and indicate their relative position), and interrogative pronouns (used to introduce questions about things, people, and ownership).

Examples: Pronouns in a sentenceI don’t really know



That is a horrible painting!

Who owns the nice car?


A verb is a word that describes an action (e.g., “jump”), occurrence (e.g., “become”), or state of being (e.g., “exist”). Verbs indicate what the subject of a sentence is doing. Every complete sentence must contain at least one verb.

Verbs can change form depending on subject (e.g., first person singular), tense (e.g., past simple), mood (e.g., interrogative), and voice (e.g., passive voice).

Regular verbs are verbs whose simple past and past participle are formed by adding“-ed” to the end of the word (or “-d” if the word already ends in “e”). Irregular verbs are verbs whose simple past and past participles are formed in some other way.

Examples: Regular and irregular verbs“Will you


if this book is in stock?”

“I’ve already checked twice.”

“I heard that you used to sing.”

“Yes! I sang in a choir for 10 years.”

Other types of verbs include auxiliary verbs, linking verbs, modal verbs, and phrasal verbs.


An adjective is a word that describes a noun or pronoun. Adjectives can be attributive, appearing before a noun (e.g., “a red hat”), or predicative, appearing after a noun with the use of a linking verb like “to be” (e.g., “the hat is red”).

Adjectives can also have a comparative function. Comparative adjectives compare two or more things. Superlative adjectives describe something as having the most or least of a specific characteristic.

Examples: Adjectives in a sentenceThe dog is


than the cat.

He is the laziest person I know

Other types of adjectives include coordinate adjectives, participial adjectives, and denominal adjectives.


Examples: Adverbs in a sentenceRay acted



Talia writes quite quickly.

Let’s go outside!


A preposition is a word (e.g., “at”) or phrase (e.g., “on top of”) used to show the relationship between the different parts of a sentence. Prepositions can be used to indicate aspects such as time, place, and direction.

Examples: Prepositions in a sentenceHasan is coming for dinner


6 p.m.

I left the cup on the kitchen counter.

Carey walked to the shop.

NoteA single preposition can often describe many different relationships, depending upon how it’s used. For example, “in” can indicate time (“in January”), location (“in the garage”), purpose (“in reply”), and so on.


A conjunction is a word used to connect different parts of a sentence (e.g., words, phrases, or clauses).

The main types of conjunctions are coordinating conjunctions (used to connect items that are grammatically equal), subordinating conjunctions (used to introduce a dependent clause), and correlative conjunctions (used in pairs to join grammatically equal parts of a sentence).

Examples: Conjunctions in a sentenceDaria likes swimming



You can choose what movie we watch because I chose the last time.

We can either go out for dinner or go to the theater.


An interjection is a word or phrase used to express a feeling, give a command, or greet someone. Interjections are a grammatically independent part of speech, so they can often be excluded from a sentence without affecting the meaning.

Types of interjections include volitive interjections (used to make a demand or request), emotive interjections (used to express a feeling or reaction), cognitive interjections (used to indicate thoughts), and greetings and parting words (used at the beginning and end of a conversation).

Examples: Interjections in a sentence


. What time is it?

Ouch! I hurt my arm.

I’m, um, not sure.

Hey! How are you doing?

Other parts of speech

The traditional classification of English words into eight parts of speech is by no means the only one or the objective truth. Grammarians have often divided them into more or fewer classes. Other commonly mentioned parts of speech include determiners and articles.


A determiner is a word that describes a noun by indicating quantity, possession, or relative position.

Common types of determiners include demonstrative determiners (used to indicate the relative position of a noun), possessive determiners (used to describe ownership), and quantifiers (used to indicate the quantity of a noun).

Examples: Determiners in a sentence


chair is more comfortable than



My brother is selling his old car.

Many friends of mine have part-time jobs.

Other types of determiners include distributive determiners, determiners of difference, and numbers.

NoteIn the traditional eight parts of speech, these words are usually classed as adjectives, or in some cases as pronouns.


An article is a word that modifies a noun by indicating whether it is specific or general.

The definite article the is used to refer to a specific version of a noun. The can be used with all countable and uncountable nouns (e.g., “the door,” “the energy,” “the mountains”).

The indefinite articles a and an refer to general or unspecific nouns. The indefinite articles can only be used with singular countable nouns (e.g., “a poster,” “an engine”).

Examples: Definite and indefinite articles in a sentenceI live just outside of the town.

There’s a concert this weekend.

Karl made an offensive gesture.

NoteWhile articles are often considered their own part of speech, they are also frequently classed as a type of determiner (or, in some grammars, as a type of adjective).

Interesting language articles

If you want to know more about nouns, pronouns, verbs, and other parts of speech, make sure to check out some of our language articles with explanations and examples.

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