Applications of MFCC and Vector Quantization in speaker recognition

The Mel-Frequency Cepstral Coefficients (MFCC) feature can be cast-off in speaker recognition. The process of feature extraction of the speech signal using Mel-Frequency Cepstral Coefficients (MFCC) feature vectors will generate an acoustic speech signal. Locality Sensitive Hashing (LSH) is frequently used as a classifier for Big Data related problems. In this research, we proposed a new model based on MFCC and LSH to integrate into speaker recognition model. The main returns of our newly proposed model are to get robustness, effective and accurate results in comparison with MFCC+GMM, LPCC+GMM and MFCC+PNN models. This model also contributes to the literature of Big Data. In this model, first, we extract the MFCC features from the wave file then we applied LSH classifier on extracted feature to transform into hash-table. Finally, the hash-tables of train and test wave files are compared and obtained 92.66% speaker recognition accuracy. We compared the accuracy ratio of proposed model with other traditional models namely MFCC+GMM, MFCC+PNN, and LPCC+GMM. Experimental results show that proposed model is more accurate and robust than traditional models and good for speaker recognition.

ABSTRACT: Speech is one of the most natural and efficient forms of communication among humans, and it is becoming increasingly integral to human-computer interaction in modern applications such as virtual assistants, smart devices, and automated customer service systems. Among the various technologies that leverage speech, speaker recognition stands out as a biometric method that identifies or verifies individuals based on the unique characteristics of their voice. This approach has gained significant traction due to its non-intrusive nature, ease of integration, and applicability across various domains such as security, forensics, and personalised user experiences. In this paper, we introduce a novel speaker recognition system specifically designed to identify speakers based on utterances in the Marathi language, a linguistically rich and widely spoken regional language in India. The system utilises Mel-Frequency Cepstral Coefficients (MFCCS) to extract distinguishing vocal features that closely mimic the human auditory perception. MFCCS are particularly effective in capturing speech’s phonetic and acoustic properties, making them a preferred choice in speech and speaker recognition tasks. Vector quantisation (VQ) is applied to optimise the feature set and reduce computational complexity. VQ compresses the high-dimensional MFCC feature vectors into representative clusters without significantly compromising accuracy, thereby enhancing system efficiency. These features are then processed using hidden Markov Models (HMMS), which are adept at modelling temporal sequences and dynamic variations in speech. HMMS offer a statistical framework that effectively captures the sequential nature of speech patterns, leading to more reliable speaker modelling and recognition.

This paper investigates the development of language identification based on Mel-Frequency Cepstral Coefficients (MFCC) and Vector Quantization (VQ) algorithm. In this study, a total of ten speakers were chosen randomly with different languages from online language database. A total of six males and four females were selected as subjects for this research and each of them spoke different languages, including Arabic, Chinese, English, Korean and Malay. The MFCC will be extracted to derive the related feature vector. Vector Quantization (VQ) algorithm is then used as classifier. The recognition rate is then calculated for each language. Several experiments were conducted to find the optimum parameters, in which we found that sampling frequency of 16000 Hz and codebook size of 75 provided good results. On average, the recognition rate for all five languages evaluated was 78%. The experimental results show that our proposed system provides a good recognition rate.

This paper presents significance of Mel-frequency Cepstral Coefficients (MFCC) Frequency band selection for text-independent speaker identification. Recent studies have been focused on speaker specific information that may extends beyond telephonic passband. The selection of the frequency band is an important factor to effectively capture the speaker specific information present in the speech signal for speaker recognition. This paper focuses on development of a speaker identification system based on MFCC features which are modeled using vector quantization. Here, the frequency band is varied up to 7.75 kHz. Speaker identification experiments evaluated on TIMIT database consisting of 630 speaker shows that the average recognition rate achieved is 97.37 % in frequency band 0–4.85 kHz for 20 MFCC filters.

This paper examines the performance improvement via combining feature vectors for speaker verification. As feature vectors, we use LPC (linear predictive coding) spectral envelope (LPCSE), Mel-frequency cepstral coefficients (MFCC) and piecewise LPC (pLPC) pole distribution (pLPCPD). Here, pLPCPD is the feature which we have introduced and developed, and this paper shows a relationship of the underlying pLPC speech signal model for speaker verification with ARMA and AR models for speech production. We execute experiments of speaker verification with the combinations of the features. We examine the experimental results and show that (1) the smallest AER (average error rate) is achieved by MFCC followed in order by pLPCPD and LPCSE, (2) the AER achieved by LPCSE, MFCC and LPCSE+MFCC (combination of LPCSE and MFCC) is reduced by combining pLPCPD, (3) there are conflicting features in LPCSE, MFCC and pLPCPD to increase the AER, and the smallest AER is obtained by MFCC+pLPCPD among the combinations of LPCSE, MFCC and pLPCPD. We also investigate the relationship between pLPCPD and the aperiodicity used for vocal cord excitation in recent vocoders, which seems to explain the above result (2).

Speaker recognition is an Artificial Intelligent (AI) technology that lets the machine to process, interpret and respond to human language. In this work, the recorded speech developed from a collection of audio speeches is used as a database. Mel-frequency cepstral coefficients (MFCC) and gammatone frequency cepstral coefficients (GFCC) are two different cepstral features used in this work. These extracted features are then used to train, validate and test the classifier. Support Vector Machine (SVM) is the classifier used in developing the speaker identification system. This classifier is trained to classify the input speech into one of the ethnicity classes: Malay, Chinese, Indian or Bumiputera. The results are based on the two different usages of cepstral features from the same speech utterances by speakers. Finally, the comparative analysis of the speaker identification system is made concerning features and classifier. The results revealed that a combination of GFCC and pitch as the feature vectors (Model 4) produced the highest accuracy rate of 86.1%.

Feature selection using singular value decomposition and QR factorization with column pivoting for text-independent speaker identification

In order to improve the speaker recognition accuracy, a new mel-frequency cepstral coefficient (MFCC) parameters extraction criterion based on F-Ratio and correlated distance criterion is proposed. Take the correlation of the characteristics into account, F-Ratio and correlated distance criterion (FRCDC) is proposed, which provides an idea about the significance of each component for MFCC feature. Two methods based on this criterion to extract MFCC parameters are given: dimensionality reduced according to the FRCDC value, improving the MFCC's discrimination by using a new bandpass liftering window. Experimental results based on two languages voice database show that the recognition rate adopting proposed dimensionality reduced method is improved by 10-15% than that of method without dimensionality reduced. The recognition rate using the new liftering process is improved by 20-25% than that without the liftering and 10-20% than conventional methods.

Recognition of car horns based on principal component analysis of MEL frequency Cepstral coefficients and support vector machine

In this artificial intelligence time, speaker recognition is the most useful biometric recognition technique. Security is a big issue that needs careful attention because of every activities have been becoming automated and internet based. For security purpose, unique features of authorized user are highly needed. Voice is one of the wonderful unique biometric features. So, developing speaker recognition based on scientific research is the most concerned issue. Nowadays, criminal activities are increasing day to day in different clever way. So, every country should have strengthen forensic investigation using such technologies. The study was done by inspiration of contextualizing this concept for our country. In this study, text-independent Amharic language speaker recognition model was developed using Mel-Frequency Cepstral Coefficients to extract features from preprocessed speech signals and Artificial Neural Network to model the feature vector obtained from the Mel-Frequency Cepstral Coefficients and to classify objects while testing. The researcher used 20 sampled speeches of 10 each speaker (total of 200 speech samples) for training and testing separately. By setting the number of hidden neurons to 15, 20, and 25, three different models have been developed and evaluated for accuracy. The fourth-generation high-level programming language and interactive environment MATLAB is used to conduct the overall study implementations. At the end, very promising findings have been obtained. The study achieved better performance than other related researches which used Vector Quantization and Gaussian Mixture Model modelling techniques. Implementable result could obtain for the future by increasing number of speakers and speech samples and including the four Amharic accents.

Significance of analytic phase of speech signals in speaker verification

In the study of speaker identification, Mel Frequency Cepstral Coefficient (MFCC) method is the best and most popular which is used to feature extraction. Further Vector Quantization (VQ) technique is used to minimize the amount of data to be handled and mapping vectors from a large vector space to a finite number of regions in that space in recent years. Voice, like other biometrics, cannot be forgotten or misplaced, unlike knowledge-based (e.g., password) or possession-based (e.g., key) access control methods. In the present work, modified Mel frequency cepstral coefficients using discrete fractional Fourier transform and vector quantization is obtained. The experimental results are analyzed with the help of MATLAB.

This paper describes the implementation of a speaker identification system with reference to Assamese language. The database consists of speech samples that were collected from 15 (fifteen) speakers for ten Assamese words representing the Assamese digits from 0 (shounyo) to 9 (no). Mel Frequency Cepstral Coefficients (MFCC) are used as features for this study. Two independent speaker identification systems have been built in this paper using Vector Quantization (VQ) and I-vector technique. The system built using the I-vector technique obtains comparatively better identification accuracy for speakers when compared with the system developed using VQ technique. Three different systems have been built for both the techniques based on variable feature size. A maximum accuracy of 92.38% is achieved using I-vector technique with 39 MFCC features.

In this paper, an expert system for security based on biometric human features that can be obtained without any contact with the registering sensor is presented. These features are extracted from human’s voice, so the system is called Voice Recognition System (VRS). The proposed system consists of a combination of three stages: signal pre-processing, features extraction by using Wavelet Packet Transform (WPT) and features matching by using Artificial Neural Networks (ANNs). The features vectors are formed after two steps: firstly, decomposing the speech signal at level 7 with Daubechies 20-tap (db20), secondly, the energy corresponding to each WPT node is calculated which collected to form a features vector. One hundred twenty eight features vector for each speaker was fed to the Feed Forward Back-propagation Neural Network (FFBPNN). The data used in this paper are drawn from the English Language Speech Database for Speaker Recognition (ELSDSR) database which composes of audio files for training and other files for testing. The performance of the proposed system is evaluated by using the test files. Our results showed that the rate of correct recognition of the proposed system is about 100% for training files and 95.7% for one testing file for each speaker from the ELSDSR database. The proposed method showed efficiency results were better than the well-known Mel Frequency Cepstral Coefficient (MFCC) and the Zak transform.

In this work, a Recurrent Neural Network (RNN) is trained using cepstral features and a set of difference cepstral feature (DCF) vectors on a frame to frame basis. The DCF vector is formulated to capture the temporal patterns of fricative sounds or phonemes of Assamese language. A hybrid algorithm is developed to recognize these fricative phonemes from certain words containing them. To preserve the temporal information of the speech segment, we here consider a frame-based hybrid approach to recognize fricatives from Assamese speech. A hybrid feature set is developed where simple frame-based feature is combined with differential frame-based feature. Investigation of feature extraction techniques like Linear Predictive Cepstral Coefficient (LPCC) and Mel-Frequency Cepstral Coefficient (MFCC) have been carried out and their performances have been evaluated in comparison to that obtained from the DCF set for Assamese fricative phoneme recognition. Here, speech segment is divided into 20 millisecond frames with overlap of 10 millisecond to extract features. Also, difference of a current frame with its preceding and succeeding frame is considered for forming a more accurate dynamic approach for fricative recognition. The differential processing enables to reduce correlation and retain only the most relevant portion of the input. After obtaining the feature vectors, Self Organizing Map (SOM) has been used to categorize the related features into different classes and remove repeating data. Thus the features obtained from the phoneme signal has been reduced into different sized cluster centres provided by SOM. The reduced feature vector is next applied to the RNN based hybrid classifier for learning the pattern and recognizing any unknown fricative segment.