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It is an interdisciplinary branch which demands knowledge of computer science, linguistics, mathematics, computing and such related fields. Although officially, it was first developed in 1952 at Bell <a href="https://www.ssla.co.uk/about-us">laboratories</a> since then, the technologies and methodology used in it have undergone a lot of waves of innovation. At present, the accuracy of speech recognition systems is at per with those of humans. This technology today allows us to talk with our devices replacing the existing tiresome techniques of typing and clicking. Whether it is playing music, switching off lights, making a call, everything today can be done at your command, all you need to do is “speak” and they will act accordingly. This technology is a boon especially for those who are physically handicapped. It is commonly called an Automatic Speech Recognition (ASR) system.</span></p><h3 style="text-align: left;"><span style="color: #000000;">Working Principle of speech recognition.</span></h3><p><span style="font-weight: 400;">Speech recognition is a case of pattern recognition and the basic principle of its working are as follows:</span></p><ul><li style="font-weight: 400;"><b>Inpu</b><span style="font-weight: 400;">t: The input to the system is the speech which acts as a trigger to it. A high-quality noise-cancelling microphone is used to capture it.</span></li><li style="font-weight: 400;"><b>Pre-processing</b><span style="font-weight: 400;">: The speech which is an analog signal is then digitized using a <a href="https://www.ssla.co.uk/buy">DSP</a> (Digital Signal Processor) into a series of 8- or 16-bit values at a particular sampling frequency. The unwanted noises surrounding the speech are filtered before extracting the features. </span></li><li style="font-weight: 400;"><b>Feature Extraction</b><span style="font-weight: 400;">: The pre-processed raw speech is used for the extraction of relevant features for recognition. Since speech varies with the person, those features that would be independent of the speaker are the important ones for ASR. These features act as discriminants and help in classifying the input in different class labels. The features might be spectral or temporal or both depending on the purpose. In the temporal analysis, the features are extracted directly from the speech signal. Some of the commonly used temporal techniques for feature extraction are Power estimation, Fundamental frequency estimation, Gold and Rabiner Algorithm, Cepstrum based pitch determination. For spectral analysis, the speech signal needs to be transformed from time to frequency domain to extract features. Some of the spectral techniques commonly used are Critical Band filter bank analysis, Cepstrum analysis, Mel Cepstrum analysis, Linear Predictive Coding (LPC) Analysis, Perceptually Based Linear Predictive Analysis (PLP). This stage is common for both the training and testing phase. </span></li><li style="font-weight: 400;"><b>Training/Classification</b><span style="font-weight: 400;">: At this stage, the system is trained with the dataset. Parameters are estimated using complex mathematics. These parameters are used in the classification model which are stored in the memory as a template for reference. Some common examples are listed as follows.</span></li><li style="font-weight: 400;"><b>Dynamic Time Warping (DTW)</b><span style="font-weight: 400;">: In this <a href="https://www.ssla.co.uk/referral">approach</a>, time warping techniques are used to find the distance between the reference and the unknown input speech patterns.</span></li><li style="font-weight: 400;"><b>Hidden Markov Model (HMM)</b><span style="font-weight: 400;">: This approach is simple and effective for modelling time-varying sequences (continuous speech) based on the Markov model. </span></li><li style="font-weight: 400;"><b>VQ (Vector Quantization)</b><span style="font-weight: 400;">: It is used for mapping from a large vector space to a small finite space where each region represents a cluster.</span></li><li style="font-weight: 400;"><b>ANN (Artificial Neural Network)</b><span style="font-weight: 400;">: It is an electronic computational model based on the idea to replicate the biological neurons present in animal models. The input and output (result) are fed to it during the training phase based on which it creates a mapping function between the two.</span></li><li style="font-weight: 400;"><b>Testing/Recognizing</b><span style="font-weight: 400;">: During the testing (recognizing) phase each of the features is matched with the stored database and the class to which it has the best fit, it is classified into that.</span></li></ul><h3 style="text-align: left;"><span style="color: #000000;"><img data-lazyloaded="1" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIzMDAiIGhlaWdodD0iMjQ0IiB2aWV3Qm94PSIwIDAgMzAwIDI0NCI+PHJlY3Qgd2lkdGg9IjEwMCUiIGhlaWdodD0iMTAwJSIgc3R5bGU9ImZpbGw6I2NmZDRkYjtmaWxsLW9wYWNpdHk6IDAuMTsiLz48L3N2Zz4=" fetchpriority="high" decoding="async" class="alignnone size-medium wp-image-7933" data-src="https://www.ssla.co.uk/wp-content/uploads/2020/08/speech-recognition-300x244.png" alt="speech recognition" width="300" height="244" data-srcset="https://www.ssla.co.uk/wp-content/uploads/2020/08/speech-recognition-300x244.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/08/speech-recognition-1024x834.png 1024w, https://www.ssla.co.uk/wp-content/uploads/2020/08/speech-recognition-768x625.png 768w, https://www.ssla.co.uk/wp-content/uploads/2020/08/speech-recognition-600x489.png 600w, https://www.ssla.co.uk/wp-content/uploads/2020/08/speech-recognition.png 1120w" data-sizes="(max-width: 300px) 100vw, 300px" /><noscript><img fetchpriority="high" decoding="async" class="alignnone size-medium wp-image-7933" src="https://www.ssla.co.uk/wp-content/uploads/2020/08/speech-recognition-300x244.png" alt="speech recognition" width="300" height="244" srcset="https://www.ssla.co.uk/wp-content/uploads/2020/08/speech-recognition-300x244.png 300w, https://www.ssla.co.uk/wp-content/uploads/2020/08/speech-recognition-1024x834.png 1024w, https://www.ssla.co.uk/wp-content/uploads/2020/08/speech-recognition-768x625.png 768w, https://www.ssla.co.uk/wp-content/uploads/2020/08/speech-recognition-600x489.png 600w, https://www.ssla.co.uk/wp-content/uploads/2020/08/speech-recognition.png 1120w" sizes="(max-width: 300px) 100vw, 300px" /></noscript></span></h3><h3 style="text-align: left;"><span style="color: #000000;">Design Constraints</span></h3><p><span style="font-weight: 400;">ASR varies in different forms depending upon the imposed constraints. Some of the constraints are discussed below:</span></p><ul><li style="font-weight: 400;"><b>Speaker size</b><span style="font-weight: 400;">: No matter who the speaker is, ASR might be able to recognise it, such a system is said to be speaker-independent. If an ASR is speaker-dependent it needs to be trained with several speakers. Many adaptive ASRs have also come up which incorporate the facility of adding new speakers.</span></li><li style="font-weight: 400;"><b>Nature of utterance</b><span style="font-weight: 400;">: Depending upon the utterance type different ASRs are designed. For instance, in isolated word recognition systems, one needs to speak words with a pause to make it intelligible to the device. Whereas a continuous speech recognition system has the potential to recognise sentences continuously.</span></li><li style="font-weight: 400;"><b>Size of vocabulary</b><span style="font-weight: 400;">: Based on the size of vocabulary, the type of ASR varies from small (less than hundred), medium (few hundreds), large (thousands) to very large (tens of thousands). </span></li><li style="font-weight: 400;"><b>Spectral bandwidth</b><span style="font-weight: 400;">: ASR may be <a href="https://en.wikipedia.org/wiki/Speech_recognition">wideband</a> or narrowband based on whether the spectrum is broad or narrow.</span></li></ul><h3 style="text-align: left;"><span style="color: #000000;">Applications</span></h3><p><span style="font-weight: 400;">Speech recognition systems are found everywhere, starting from our home to workplaces. It is cost-effective and makes life easier. Especially for those who are disabled, the ASR system gives them a new ray of hope. The list can go on but some of the applications are enlisted below:</span></p><ul><li style="font-weight: 400;"><b>Office</b><span style="font-weight: 400;">: The traditional jobs like printing of documents, plotting graphs, online conferences, meetings, voice browser for the Internet, voice dialler, dictation etc. all can be controlled at a voice command.</span></li><li style="font-weight: 400;"><b>Banking and Industry</b><span style="font-weight: 400;">: Customer service is reduced today by using ASR. This helps in the reduction of friction for customers and gives them more satisfaction. Online payments, transaction history and other services can be controlled by a personalised voice-activated device. To add, speech translators help in initiating better communication by removing the barrier of language thereby making the customers feel comfortable and at home.</span></li><li style="font-weight: 400;"><b>Telecommunications</b><i><span style="font-weight: 400;">: Automation of operator services</span></i><span style="font-weight: 400;">, customer care, voice calls are some examples where these systems can ease the manual labour.</span></li><li style="font-weight: 400;"><b>Military</b><span style="font-weight: 400;">: Speech-to-speech translation, controlling fighter jets are some examples.</span></li><li style="font-weight: 400;"><b>Automobile</b><span style="font-weight: 400;">: ASR can assist the riders by displaying maps, showing shortest routes, traffic status etc on command. It can also be used to play music and switch on/off the radio.</span></li><li style="font-weight: 400;"><b>Healthcare</b><span style="font-weight: 400;">: It can help doctors in creating reports, searching databases thereby reducing human labour. Common people with no medical ground can take help of ASR to understand the common symptoms of a disease, calling a doctor etc. Again, whether one is physically or visually disabled, using their speech they can initiate many activities at their home as well as the workplace. There are many voice-activated games.</span></li></ul><p><span style="font-weight: 400;">Some of the realistic speech activated digital assistants are available today and can be used in any of the above-mentioned fields are</span></p><ul><li style="font-weight: 400;"><span style="font-weight: 400;">Amazon’s Alexa</span></li><li style="font-weight: 400;"><span style="font-weight: 400;">Apple’s Siri</span></li><li style="font-weight: 400;"><span style="font-weight: 400;">Google’s Google Assistant</span></li><li style="font-weight: 400;"><span style="font-weight: 400;">Microsoft’s Cortana</span></li></ul></div></div><div class="vc_btn3-container red-button vc_btn3-inline" ><a class="vc_general vc_btn3 vc_btn3-size-md vc_btn3-shape-rounded vc_btn3-style-modern vc_btn3-color-danger" href="https://www.ssla.co.uk/contact/" title="">Contact Us</a></div></div></div></div></div></div><div class="container"><div class="vc_row vc_row-fluid boxed"><div class="wpb_column vc_column_container 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