Carrying your research on Speech processing projects using MATLAB is one of the best ways to implement your innovative ideas. Speech processing refers to the techniques used in recognizing and processing audio signals. Speech processing technologies have developed exponentially in recent times. 

  • Speech technologies nowadays have improved quite significantly for analyzing complex data input. 
  • This has created tremendous use of artificial intelligence and machine learning algorithms into speech processing techniques. 

The following article is a brief analysis of speech processing technologies and their various aspects. The analysis includes the following.

  • Application of cognitive sciences in speech recognition
  • Analysis and processing of speech signals
  • Machine learning algorithms and tools
  • Production and perception of speech
  • Understanding multiple languages

In this article, let us see them all in detail. First, let us start with the working of speech recognition.


You might have used apps like Google keyboard or voice searches in Google. While using them, what do you do first? The language is pre-selected.

  • When we make an input invoice, the algorithms of the particular software breaks the input into multiple parts in the first place
  • Then the most suitable word in the language that fits in the place is displayed

In this way, speech recognition works efficiently in any language. Our experts have been designing speech processing projects for the past 20 years. 

They have wide experience in the design of speech recognition systems. So you can opt to get their guidance at any time regarding your Project on speech processing project using MATLAB. Now let us see in detail the issues and challenges associated with speech processing projects.


There are many challenges associated with designing speech processing projects using matlab. This is said because the efficiency of any speech recognition system depends on the accuracy with which it is able to detect the correct word. The accuracy factor depends on many aspects, which are listed below.

  • When speech is more spontaneous the fillers used in between by the user reduces accuracy
  • In case of systems designed for use by a single user, multiple users speech recognition becomes difficult
  • Noise in the environment is a major cause for reducing accuracy
  • Recognition accuracy varies for continuous and discontinuous speech
  • When the length of the word is very big error may occur
  • When letters of same sound are used the system get confused

These are the common causes of errors in which speech recognition systems register less accuracy. You can approach our engineers to solve such kinds of problems. They have solved these issues by designing algorithms to suit the needs of the customers. 

The project that we have delivered helps customers overcome these challenges readily. So you can connect with us to get your doubts solved. Now let us see about the characteristics of speech recognition systems.

Speech Recognition Projects using Matlab


Speech recognizing systems have many basic characteristics associated with them. The efficiency of the system is highly dependent on these features. Now let look at it in detail. The following are the major features of speech recognizing systems. These features are common to any system that works on speech processing. 

  • The system works more based on the concept of machine learning
  • Based on the speech of the user, accents etc the system gets trained to recognize with more accuracy
  • Major feature of the system is integration of the following.
    • Structure
    • Syntax
    • Grammar
    • Audio
    • Voice (human speech)

Thus the system of speech recognition follows many steps to ensure that the above features are incorporated into it. The effectiveness of the system depends on how well it is designed. 

You might be aware of the processes involved in designing speech recognition systems. But still, it is our duty to explain everything to you from the basics. This attitude of our research experts has gained them acknowledgment from students across the world. So let us now take you through the steps involved in designing a good speech recognition system and its workflow


As we said above, speech recognition accuracy is mainly based on its design and workflow. So designing of the system is to be given priority. The following are the basic steps involved in the speech recognition process designing.

The designing of speech recognition processes is done in two phases which are the training and execution phase, respectively.

Training phase

In order to train the system, the following processes are carried out.

  • Preprocessing of input samples (removal of noise and sampling)
  • Feature extraction is the next step (using Mel-Frequency Cepstrum Coefficients or MFCC and pitch)
    • To get solution oriented ML algorithms specific samples extraction of required features is performed
  • The extracted features (dataset) are then classified into training set and test set so as to train and test the ML model respectively.
  • Generalization between feature vectors and class labels is the next step.
  • Cross checking or cross validation plays a key role

The accuracy of the system can be specified only when cross-validation is performed. The analysis of performance is based on the following factors

  • Confusion matrix
    • Accuracy
    • Recall

Therefore in the training phase, the ML algorithm is trained effectively, and a set of input data is effectively classified using a trained classifier.

Execution phase

  • After analyzing the performance of the system it is fit to be trialed for execution
  • One of the important factors of concern is over fitting. It may cause errors while execution
  • In the execution phase the ML model classifies the random input given to it using the trained classifier

These are the steps followed in designing a speech recognition system. For your system to be the perfect one, you need to make changes to your algorithm whenever necessary during the execution phase. Only trial and error methods can help you improve your model. 

So it is essential for you to have someone well experienced in the field to guide and assist you. We stress this fact because we have seen students and research scholars get completely fed up due to the issues they face during the execution phase. 

You need not worry about this because our experienced technical team is here to guide you. As we have designed and delivered thousands of projects related to speech recognition, we can surely guide you and reduce your burden. Visit our website or get connected with us for more information. Now let us see some project ideas on speech recognition.


Speech recognition projects are gaining importance these days due to the increasing demand for them. The following are the most trending and significant project ideas in speech recognition

  • EEG signal based imagined speech recognition
  • Recognition of vowel (Bayesian algorithm)
  • Conversion from pitch to MIDI (using TI TMS320 C 30)
  • Computer speech recognition
  • Predictive coding (linear)
  • EVM and TI TMS320 C30 speech recognition processes
  • Analyzing instrumental music (Digital signal processing technique)

We are providing research support on speech recognition project topics. We also provide guidance for any idea that you approach us with. Our engineers can support you to build a successful speech recognition project. 

The projects that we delivered have always excelled in all the performance metrics used to evaluate the speech recognition processes. Do you want to know in detail about the performance analysis factors used for evaluating the performance of speech recognizing systems? Then keep reading the next section.


The following are the key performance metrics used for evaluating speech recognition methods.

  • Accuracy
    • Command success rate
    • Word error rate
    • Single word error rate
  • User input
    • Pronunciation
    • Accent
    • Speed
    • Pitch
    • Nasality
    • Voice
    • Articulation
    • Roughness
  • Speaker dependent system
  • Speech (continuous, isolated or discontinuous)
  • Constraints (task, grammar and language)
  • Conditions of the environment (noise)
  • Spontaneous speech (or read)

These are the factors based on which the speech recognition method is evaluated. It is important for anyone to note that the design speech recognition method is perfect only when it qualifies in all the above aspects. For this purpose, you can approach our technical teams, who have enough encounters with customers in evaluating the speech recognition projects.

This is the time for us to have some technical batting regarding MATLAB for designing your speech recognition projects. MATLAB is one of the famous tools used by researchers for the design and simulation of their speech recognition projects. Hence it becomes important for you to be an expert in using MATLAB tools. We are here to guide you in that. Now let us have some insight on using MATLAB tools and software for designing speech recognition projects.


The above-mentioned speech processing methods are employed to perform highly accurate speech recognition. You can use MATLAB SIMULATION for the execution of the following steps. 

  • MATLAB is used to convert the complete sound file into smaller acoustic vectors (or sequence of MFCC)
  • There are readily available MATLAB functions listed below that makes it possible
  • Hamming
  • Melfb
  • Fft
  • Wavread
  • Dct

There is also a help function for any type of assistance that you would need regarding the usage of these functions.

For instance let us now try to use MATLAB to answer the following questions.

  • Using sound function to play sound files using MATLAB
  • Sampling rate
  • Highest frequency that can be captured
  • Actual speech contained in 256 samples block

The following steps are followed to reach at answer to the above questions.

  • Signal is plotted in the time domain
  • Speech feature extraction is ideally performed
  • Speech signals are now cut into frames
  • Matrix with N samples in column is obtained
  • Transformation to frequency domain (FFT and windowing)
  • Window Fourier Transform
  • Short time Fourier Transform
  • The obtained result is in the form of spectrum or periodogram

Technically there are algorithms that perform the above tasks. Let us now have some deep insight into the usage of MFCC in MATLAB.


An MFCC or Mel-frequency cepstrum coefficient is used in MATLAB for the following functions.

  • Taking out of MFCC
  • Log energy
  • Data delta

Let us look into detail about the common syntax used in MATLAB for speech processing projects using MATLAB.


The syntax used for the above mentioned objectives is as follows.

coeffs = mfcc(audioIn,fs)

coeffs = mfcc(   , Name, Value)

[ coeffs, delta, deltaDelta, loc] = mfcc(  )

     The purpose of the syntax is described in the next section.

  • coeffs = mfcc(audioIn,fs) for returning MFCC for the given audio input (frequency is fs Hz)
  • coeffs = mfcc(   , Name, Value) for specifying options (for multiple Name, Value pairs)

For instance let us consider the function,

Coeffs = mfcc (audioIn, ‘LogEnergy’, ‘ Replace’)

This syntax gives the log energy value replacing the first coefficient.

Our experts are highly trained in using these functions. So you can get their help in all circumstances. Now let us see about the algorithms used for speech recognition purposes.


The following are the famous feature sets used for speech signal analysis

  • MFCC or Mel Frequency Cepstral Coefficients
  • LPCC or Linear Prediction Cepstral Coefficients

The popular speech recognition models are

  • DTW or Dynamic Time Warping
  • VQ or Visual Quantization
  • ANN or Artificial Neural Networks

The following is a project that we designed and delivered successfully using MATLAB. We have provided its technical details for your reference. You can understand the expertise of our technical team once you go through the following.


The following steps are involved in the proposed project.

  • EEG signal data set is loaded and assessed in the first step
  • Base criterion method – for process of duplicate channel removal
  • Soft Actor Critic – for optimal channel selection
  • Feature extraction include features like median and mean value, scale variance, mode, signal power energy, max and min value, covariance, standard deviation and standard error
  • CapsNet-DNN – for signal classification

The proposed project showed extraordinary results when evaluated on the following metrics.

  • Precision
  • ROC curve
  • Accuracy of classification in iterations involved
  • Relation between training loss and epochs
  • Recall
  • F1 score
  • Accuracy in classifying vowels

Thus we have explained all the basic necessities for doing speech processing projects using MATLAB. Now it is your turn to connect with us, who is one of the world’s best online research guidance providers for the successful design, completion, and execution of your project. Reach out to us at any time. Our technical team is readily equipped to support you.

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