One of these preprocessing is creating delta and deltadelta coefficients and append them to mfcc to create feature vector. Speaker identification features extraction methods. On autoencoders in the ivector space for speaker recognition. In 1, the i vector features were tested on the 2008 nist speaker recognition evaluation sre telephone data. Vector quantization v q are the most frequently used pattern recognition techniques in speech recognition field. Mfcc is the technique to exploit the differences of the speech signal. A method of automatic speaker recognition using cepstral. Soong, evaluation of a vector quantization talker recognition system in text independent and text dependent models, computer speech and language 22, pp. The main advantage of vq in pattern recognition is its low. He also pronounces a small vocabulary called the adaptation vocabulary.
This denoising transform can be further netuned using discriminative ob. An emotion is a mental and a physiological state of a person. Introduction speaker recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. Automatic speaker recognition techniques are increasing the use of the speakers voice to control access to personalized telephonic services. This technology has made it possible to use the speakers voice to control access to. Performance comparison of speaker recognition using vector. Two methods for codebook generation have stbeen used. Speaker recognition refers to the concept of recognizing a speaker by hisher. It is the process of automatically recognizing who is.
A vector quantization approach to speaker recognition 1985. Traditional methods for textindependent speaker recognition are gmms reynolds et al. Since fuzzy classifiers based on structural risk minimization have not been applied in speaker recognition area so far. The identification experiments were performed in a closed set of 599 speakers and two various types of features were tested. Treestructured vector quantization for speech recognition. Mfcc and vector quantization techniques are the most preferable and promising these days so as to support a technological aspect and motivation of the significant progress in the area of voice recognition. Textdependent speaker verification using vector quantization. Here we provide a highlevel description of the i vector approach used in stateoftheart speaker recognition systems for a detailed description see, for example, 4 5. Speaker recognitiongeneral classifier approaches and data fusion.
An overview of textindependent speaker recognition. This system is split into two models as mentioned below. The nist 2014 speaker recognition ivector machine learning. Coefficients is one of the performance enhancement parameters for speaker recognition. The main aim of this paper is to investigate the effect that short duration utterances have on both enrolment and training when using the i vector approach. Speech samples recognition based on mfcc and vector.
Performance comparison of speaker identification using vector. A series of speaker recognition experiments was variability of shorttime acoustic features of speakers. I have made a textindependant speaker recognition program in matlab by using mfccs and vector quantization. Index terms speaker recognition, speaker verification, hidden markov model, vector quantization i. Hierarchical clustering introduction mit opencourseware. The approach described in this paper is a speaker independent. Comparison of vector quantization and gaussian mixture. Lvq systems can be applied to multiclass classification problems in a natural way.
Speech recognition, learning vector quantization, language identification, mel frequency cepstrum. The upper is the enrollment process, while the lower panel illustrates the recognition process. In the enrollment mode, a speaker model is trained. Using deep belief networks for vectorbased speaker. Compared to the 2012 nist speaker recognition evaluation, the i vector challenge saw approximately twice as many participants, and a nearly two orders of magnitude increase in. M is the number of vectors classified as one and n is the number of vec. Speech recognition using vector quantization proceedings of the. On the application of vector quantization to speaker. Learning vector quantization lvq neural network approach for multilingual speech recognition rajat haldar1, 2dr. One important application is vector quantization, a technique used in the compression of speech and images 15. Emotion based speaker recognition with vector quantization shraddha bhandavle, rasika inamdar, aarti bakshi kccemsr, thane e, india abstract. It works by encoding values from a multidimensional vector space into a finite set of values from a discrete subspace of lower dimension. Real time speaker recognition system using mfcc and.
In this paper it has been shown that the inverted melfrequency cepstral. Automatic speaker identification by voice based on vector. Cepstrum, kmeans, speaker recognition systems are categorized mel scale, speaker identification, vector quantization. The vector quantizer vq approach, like the nn method. Performance comparison of speaker identification using vector quantization by mfcc algorithm. The mel frequency approach extracts the features of the speech signal to get the training and testing vectors. Pdf ivector based speaker recognition on short utterances. Mani roja3 1, 2 student 3 associate professor 1, 3 dept. Samples taken from a signal are blocked a preliminary version of this paper appeared in the proc. A key ingredient to the success of this approach was the. In this paper, the systems of speaker identification of a textdependent.
A assistant professor, department of instrumentation engineering. In this paper, two approaches for speaker recognition based on. Speech samples recognition based on mfcc and vector quantization. Vector quantization are proposed and their performances are. Speaker recognition refers to task of recognizing peoples by their voices. Vector quantization, also called block quantization or pattern matching quantization is often used in lossy data compression.
Here, speaker modeling schemes vector quantization and gaussian mixture model are used and their performance is compared by calculating average recognition rate. Main approaches in pattern matching for speaker recognition probabilistic model template matching artificial neural network main approach vector quantization f. This can be used to many applications like identification, voice dialling, teleshopping, voice based access services, information services, telebanking, security control of confidential information. Effect of mfcc normalization on vector quantization based.
In this chapter, the vq is employed for efficient creating the extracted feature vector. We use the following scenarios for speaker and language recognition. It is basically divided into speaker identification and. Voice recognition based on vector quantization using lbg. A spatial feature extraction approach for voice recognition latest. In this paper, two approaches for speaker recognition based on vector quantization are proposed and their performances are compared. Multilingual speech recognition and language identification using lvq neural network and pso technique gives slightly better recognition rate as compare to the without pso technique. Learning vector quantization lvq, different from vector quantization vq and kohonen selforganizing maps ksom, basically is a competitive network which uses supervised learning. Recognition algorithms the first algorithm is based on the standard vector quantization vq technique. Robust speaker identification system based on twostage. The process which recognizes the speaker based on the information present in the speech is called voice recognition.
The vector quantization vq approach is used for mapping vectors from a large vector space to a finite number of regions in that space. This paper presents textindependent speaker identification system for. On autoencoders in the i vector space for speaker recognition timur pekhovsky 1. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. Reliability of voice comparison for forensic applications tel. Volume 3, issue 1, july 20 automatic speech and speaker. China,1996 a dissertation submitted in partial fulfillment of the requirements for the degree of. Speech samples recognition based on mfcc and vector quantization 1ch.
Speaker recognition using mfcc and vector quantization. Vector quantization approach for speaker recognition using mfcc. The main aim of this paper is to investigate the effect that short duration utterances have on both enrolment and training when using the ivector approach. We describe some new methods for constructing discrete acoustic phonetic hidden markov models hmms using tree quantizers having very large numbers. Human speech the human speech contains numerous discriminative features that can be used to identify speakers. Apr 30, 2014 speaker recognition using mfcc and vector quantization. Methods of combining multiple classifiers with different features and their applications to textindependent speaker recognition. We will use vq for feature extraction in both the training and testing phases. Vector quantization in text dependent automatic speaker recognition using melfrequency cepstrum coefficient ahsanul kabir, sheikh mohammad masudul ahsan department of computer science and engineering khulna university of engineering and technology fulbarigate, khulna 920300 bangladesh abstract. Results of the speaker recognition performance by varying the number of filters of mfcc to 12, 22, 32, and 42 are given. Performance comparison of speaker identification using.
This paper presents a novel method for isolated english word recognition based on energy and zero crossing features with vector quantization. See the bibliography on the selforganizing map som and learning vector quantization lvq. In figure 1 we show a simplified block diagram of i vector extraction and scoring. The variation of speaker exists in speech signals because of different resonances of the vocal tract. A key issue in lvq is the choice of an appropriate measure of distance or similarity for training and classification. This approach extends from a joint factor analysis which models speaker. For speech recognition and speaker identification ann 11 is used. To prove the robustness of the cvq system, its performance is compared to that of a standard artificial neural network annbased solution. Vector quantization in speech processing explanation. In this paper a method of textindependent speaker recognition using discrete vector quantization is presented. Every language, k, is characterized by its own vq codebook.
Codebook construction for vector quantization using the k. Another preprocessing is coefficients mean normalization. Svms for two applications in this papertextindependent speaker and language recognition. Twoband analysis tree for a discrete wavelet transform. Similarly, the technique of vector quantization vq emerged as useful tool.
This paper describes the use of vector quantization as a feature matching method, in an automatic speaker recognition system, evaluated with speech samples from a sala spanish venezuelan database for fixed telephone network. The vq codebook approach uses training vectors to form clusters and recognize accurately with the help of lbg algorithm key words. To generate code books, the lbg algorithm is used 2, 3. Real time speaker recognition system using mfcc and vector. The results of a case study carried out while developing an automatic speaker recognition system are presented in this paper. We will try to identify the person who is speaking by characteristics of hisher voice by using classical quantization technique from signal processing, vq. Performance comparison of speaker recognition using. Speaker identification based on discriminative vector. Vector quantization in text dependent automatic speaker. Speaker identification and verification based on cepstral. China,1996 a dissertation submitted in partial fulfillment of the requirements for the degree of doctor of philosophy. Abstract in this paper, a novel approach to arabic letter recognition is proposed. Ramaiah department of computer science and engineering v. Speech is a most popular biometrics nowadays used for human interaction.
Robust speaker identification system based on twostage vector quantization 359 figure 1. Each region is called a cluster and can be represented by its center called a codeword. The system is based on the classified vector quantization cvq technique employing the minimum distance classifier. Aug 12, 2014 there are two main speaker recognition system using mfcc and vector quantization approach deepak harjani1 mohita jethwani2 ms. Mariya kharaman university of konstanz, manluolan xu na, carsten. Trained rbm can map any i vector to its denoised version reducing the effect of channel. Approach average recognition rate 1 mfcc with vq 98. Speaker recognition system using mfcc and vector quantization. Mfcc and vector quantization technique in the digital world. Support vector machines for speaker and language recognition. Speaker recognition has many real world applications, including user authentication, access control, and assistance to speech separation and recognition. For ten random but different isolated digits, over 98% speaker identification h. On the application of vector quantization to speaker independent isolated word recognition florina rogers dipl.
Recently, another vq based approach to speaker identification has been. Srinivasan department of ece, srinivasa ramanujan centre, sastra university, kumbakonam612001, india abstract. Multigrained modeling with pattern specific maximum likelihood transformations for textindependent speaker recognition. Emotion based speaker recognition has attracted many. A lowerspace vector requires less storage space, so the data is compressed. Vector quantization and clustering introduction kmeans clustering clustering issues hierarchical clustering divisive topdown clustering agglomerative bottomup clustering applications to speech recognition 6. There are two main speaker recognition system using mfcc and vector quantization approach deepak harjani1 mohita jethwani2 ms. Design of an automatic speaker recognition system using. In a later lecture we will see an approach that uses the vectors directly and does not need a. Evaluation of mfcc with vq and gmm on 64 speakers of timit sr. In the study of speaker recognition, mel frequenc y cepstral coefficient mfcc method is the best. The ivectors are smaller in size to reduce the execution time of the recognition task while maintaining recognition performance similar to that obtained with jfa. Speaker recognition system using combined vector quantization. Learning vector quantization lvq neural network approach.
Our goal is to develop a realtime speaker recognition system that has been. Emotion based speaker recognition with vector quantization. Jan 10, 20 i have made a textindependant speaker recognition program in matlab by using mfccs and vector quantization. Speaker recognition using mfcc hira shaukat 20101 dsp lab project matlabbased programming attiya rehman 2010079 2. Speaker recognition using mfcc program in matlab matlab. A novel vector quantization approach to arabic character. In these approaches, a gmm universal background model gmm ubm is used to derive a vector representation of an utterance which is then used for speaker. Vector quantization vq, code vectors, code book, euclidean distance recognition output 1. Design of an automatic speaker recognition system using mfcc. Textdependent speaker verification and speech recognition do share similarities. Only one speaker the reference speaker pronounces the application vocabulary.
Vector quantization vq has been very popular in fields of speaker recognition. Characterizing the speech signal for speech recognition. With a view to designing a speaker independent large vocabulary recognition system, we evaluate a vector quantization approach for speaker adaptation. The 2019 automatic speaker verification spoofing and countermeasures challenge. Artificial neural network, multilingual speech recognition, learning vector quantization. Vector quantization based speech recognition system for home appliances proceedings of 23rd theiier international conference, singapore, 25th april 2015, isbn.
The pattern matching of the extracted signals are carried out by using the weighted vector quantization. Introduction speaker recognition technology 1 3 makes it possible to extract the identity of the person speaking. Speaker recognition using mfcc and improved weighted. The feature extraction module first transforms the raw signal into feature vectors in which speaker specific properties are emphasized and statistical redundancies suppressed. A vector quantization approach to speaker recognition. The ones i have found are very expensive, can someone suggest a free. The modified ntn computes a hit ratio weighed by the. Volume 3, issue 1, july 20 93 abstractthe speech and speaker recognition by machine are crucial ingredients for many important applications such as natural and flexible human machine interfaces which are most useful for handicap person to live the better life. The language which as the minimal distortion is recognized. This classifier is closely related to a modification of a classical takagisugenokang inference system and is based on a fuzzy moving consequents in ifthen rules.
Comparison of vector quantization and gaussian mixture model. Siddhartha engineering college, vijayawada, andhra pradesh, india. Speaker identification and verification using vector. Speaker identification based on vector quantization. Textindependent speaker identification using vector quantization.
Speaker identification and verification using vector quantization and mel frequency cepstral coefficients a. It is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. This paper presents an application of fuzzy nonlinear classifier to speaker identification and verification. Learning vector quantization lvq lvq, initially developed by kohonen 12, is a supervised twolayer neural network that applies winnertakeall hebbian learning. In this paper, the effect of these two processes on the accuracy of a vector quantization vq speaker identification system is compared. Vector quantization vq is used for feature extraction in both the training and testing phases. For speaker recognition, we consider two problemsspeaker identi. We may define it as a process of classifying the patterns where each output unit represents a class. Introduction speaker recognition refers to task of recognizing peoples by their voices. Speech signal features are extracted by the melfrequency cepstral coefficients mfcc as feature vectors. In 1, the ivector features were tested on the 2008 nist speaker recognition evaluation sre telephone data. An ivector extractor suitable for speaker recognition with. Speaker identification based on discriminative vector quantization and data fusion by guangyu zhou b.1265 194 1337 1069 403 1579 914 1470 1326 1006 1095 683 964 1489 530 203 1180 840 924 738 1085 1204 74 669 1030 33 1092 10 490 197 1075 1349 462 558