Speaker Identification (SI) is the process of identifying the speaker from a given utterance by comparing the voice biometrics of the utterance with those utterance models stored beforehand. SI technologies are taken a new direction due to the advances in artificial intelligence and have been used widely in various domains. Feature extraction is one of the most important aspects of SI, which significantly influences the SI process and performance. This systematic review is conducted to identify, compare, and analyze various feature extraction approaches, methods, and algorithms of SI to provide a reference on feature extraction approaches for SI applications and future studies. The review was conducted according to Kitchenham systematic review methodology and guidelines, and provides an in-depth analysis on proposals and implementations of SI feature extraction methods discussed in the literature between year 2011 and 2106. Three research questions were determined and an initial set of 535 publications were identified to answer the questions. After applying exclusion criteria 160 related publications were shortlisted and reviewed in this paper; these papers were considered to answer the research questions. Results indicate that pure Mel-Frequency Cepstral Coefficients (MFCCs) based feature extraction approaches have been used more than any other approach. Furthermore, other MFCC variations, such as MFCC fusion and cleansing approaches, are proven to be very popular as well. This study identified that the current SI research trend is to develop a robust universal SI framework to address the important problems of SI such as adaptability, complexity, multi-lingual recognition, and noise robustness. The results presented in this research are based on past publications, citations, and number of implementations with citations being most relevant. This paper also presents the general process of SI.

Speaker identification features extraction methods: A systematic review

This paper explains the concept of Automatic speech recognition (ASR) from the view point of pattern recognition. An ASR system can be broadly classified into two parts: front end and the back end which are responsible for feature extraction and acoustic modelling respectively. The presented paper elaborates and compares all popular feature extraction and acoustic modelling techniques along with the challenges and advancements in the field of ASR.

http://ijarcsee.org/index.php/IJARCSEE/article/view/440/407

Automatic Speech Recognition: A Survey

The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content. In this study, the authors designed and implemented a novel text-independent multimodal speaker identification system based on wavelet analysis and neural networks. Wavelet analysis comprises discrete wavelet transform, wavelet packet transform, wavelet sub-band coding and Mel-frequency cepstral coefficients (MFCCs). The learning module comprises general regressive, probabilistic and radial basis function neural networks, forming decisions through a majority voting scheme. The system was found to be competitive and it improved the identification rate by 15% as compared with the classical MFCC. In addition, it reduced the identification time by 40% as compared with the back-propagation neural network, Gaussian mixture model and principal component analysis. Performance tests conducted using the GRID database corpora have shown that this approach has faster identification time and greater accuracy compared with traditional approaches, and it is applicable to real-time, text-independent speaker identification systems.

Speaker identification using multimodal neural networks and wavelet analysis

The aim of this paper is to show the accuracy and time results of a text independent automatic speaker recognition (ASR) system, based on Mel-Frequency Cepstrum Coefficients (MFCC) and Gaussian Mixture Models (GMM), in order to develop a security control access gate. 450 speakers were randomly extracted from the Voxforge.org audio database, their utterances have been improved using spectral subtraction, then MFCC were extracted and these coefficients were statistically analyzed by GMM in order to build each profile. For each speaker two different speech files were used: the first one to build the profile database, the second one to test the system performance. The accuracy achieved by the proposed approach is greater than 96% and the time spent for a single test run, implemented in Matlab language, is about 2 seconds on a common PC.

/pdf/text-independent-automatic-speaker-recognition-system-using-5bw041wuc8.pdf

Text Independent Automatic Speaker Recognition System Using Mel-Frequency Cepstrum Coefficient and Gaussian Mixture Models

Human listeners are capable of identifying a speaker, over the telephone or an entryway out of sight, by listening to the voice of the speaker. Achieving this intrinsic human specific capability is a major challenge for Voice Biometrics. Like human listeners, voice biometrics uses the features of a person’s voice to ascertain the speaker’s identity. The best-known commercialized forms of voice Biometrics is Speaker Recognition System (SRS). Speaker recognition is the computing task of validating a user’s claimed identity using characteristics extracted from their voices. This literature survey paper gives brief introduction on SRS, and then discusses general architecture of SRS, biometric standards relevant to voice/speech, typical applications of SRS, and current research in Speaker Recognition Systems. We have also surveyed various approaches for SRS.

A Survey on Automatic Speaker Recognition Systems

This paper presents the effectiveness of perceptual features and iterative clustering approach for performing both speech and speaker recognition. Procedure used for formation of training speech is different for developing training models for speaker independent speech and text independent speaker recognition. So, this work mainly emphasizes the utilization of clustering models developed for the training data to obtain better accuracy as 91%, 91% and 99.5% for mel frequency perceptual linear predictive cepstrum with respect to three categories such as speaker identification, isolated digit recognition and continuous speech recognition. This feature also produces 9% as low equal error rate which is used as a performance measure for speaker verification. The work is experimentally evaluated on the set of isolated digits and continuous speeches from TI digits_1 and TI digits_2 database for speech recognition and on speeches of 50 speakers randomly chosen from TIMIT database for speaker recognition. The noteworthy feature of speaker recognition algorithm is to evaluate the testing procedure on identical messages of all the 50 speakers, theoretical validation of results using F-ratio and validation of results by statistical analysis using 2 χ distribution.

Text Independent Speaker Recognition and Speaker Independent Speech Recognition Using Iterative Clustering Approach

The main objective of this paper is to explore the effectiveness of perceptual features for performing isolated digits and continuous speech recognition. The proposed perceptual features are captured and code book indices are extracted. Expectation maximization algorithm is used to generate HMM models for the speeches. Speech recognition system is evaluated on clean test speeches and the experimental results reveal the performance of the proposed algorithm in recognizing isolated digits and continuous speeches based on maximum log likelihood value between test features and HMM models for each speech. Performance of these features is tested on speeches randomly chosen from “TI Digits_1”, “TI Digits_2” and “TIMIT” databases. This algorithm is tested for VQ and combination of VQ and HMM speech modeling techniques. Perceptual linear predictive cepstrum yields the accuracy of 86% and 93% for speaker independent isolated digit recognition using VQ and combination of VQ & HMM speech models respectively. This feature also gives 99% and 100% accuracy for speaker independent continuous speech recognition by using VQ and the combination of VQ & HMM speech modeling techniques.

Speaker independent continuous speech and isolated digit recognition using VQ and HMM

Affective computing is gaining paramount importance in ensuring the better and effective human–machine interaction. As glottal and speech signals depict the characteristics of the emotional nature of the speaker in addition to the linguistic information, speaker’s emotions are needed to be recognised to give meaningful response by the system. This paper emphasises the effectiveness and efficiency in selecting the energy features by passing the speech through the Gamma tone filters spaced in Equivalent rectangular bandwidth (ERB), MEL and BARK scale. Various modelling techniques are used to develop the robust multi-speaker independent speaker’s emotion/stress recognition system. Since EMO-DB Berlin database and SAVEE emotional audio-visual database used in this work contain the only limited set of speech utterances uttered by 10/4 actors/speakers in different emotions, it has become challenging to improve the performance of the stress/emotion recognition system. Speaker independent emotion recognition is done by extracting the Gamma tone energy features and cepstral features by passing the concatenated speech considered for training through the Gamma tone filters spaced in ERB, MEL and BARK scales. Subsequently, VQ/Fuzzy clustering models and continuous density hidden Markov models are created for all emotions and evaluation is done with the utterances of a speaker independent of speeches considered for training. The proposed features for test utterances are captured and applied to the VQ/Fuzzy/MHMM/SVM models and testing is performed by using minimum distance criterion/maximum log-likelihood criterion. The proposed Gamma tone energy/cepstral features and modelling techniques provide complementary evidence in assessing the performance of the system. This algorithm offers 96%, 79%, and 95.3% as weighted accuracy recall for the stress recognition system with respect to the classification done on emotion-specific group VQ/Fuzzy/MHMM/SVM models for GTF energy features with Gamma tone filters spaced in ERB, MEL and BARK scale respectively for the system evaluated for the EMO-DB database. Weighted accuracy recall is found to be 91%, 93% and 94% for the classification done on emotion-specific group models for GTF energy features with Gamma tone filters spaced in ERB, MEL and BARK scale respectively for the evaluation done on the utterances chosen from the SAVEE database. Gamma tone Cepstral features provide the overall accuracy of 92%, 90% and 92% for filters spaced in ERB, MEL and BARK scale for Berlin EMO-DB. Decision level fusion classification based on GTF energy features and modelling techniques provides the overall accuracy as 99.8% for EO-DB database and 100% for SAVEE database.

Robust emotion recognition from speech: Gamma tone features and models

The main objective of this paper is to explore the effectiveness of feature selection for performing composite speaker identification/verification. We propose features such as line spectral frequency (LSF), differential line spectral frequency (DLSF), mel frequency cepstral coefficients (MFCC), discrete cosine transform cepstrum (DCTC), perceptual linear predictive cepstrum (PLP) and mel frequency perceptual linear predictive cepstrum (MF-PLP). These features are captured and training models are developed by K-means clustering procedure. A speaker identification system is evaluated on noise added test speeches and the experimental results reveal the performance of the proposed algorithm in identifying speakers based on minimum distance between test features and clusters and also highlight the best choice of feature set among all the proposed features for 50 speakers chosen randomly from "TIMIT" database. Analysis is performed on the identification results to emphasize the choice of features which produce better results for speaker verification with respect to equal error rate. In this work, F-ratio is computed as a theoretical measure to validate the experimental results for both identification and verification.

Text Independent Composite Speaker Identification/Verification Using Multiple Features

This paper describes isolated word recognition of deaf students by unsupervised and supervised neural network. Compared to normal speech, there is high variability in deaf speech and by hearing once we couldn't understand it. By the use of proposed method deaf people can make use of all voice operated devices. In this paper we use combination of SOFM and BPN neural network for recognition. Initially the input is sampled, filtered, windowed and Perceptual Linear Predictive Coefficients are determined for each frame. These coefficients are applied as input to the SOFM neural network. The output of this network is given to BPN neural network comprising of 3 layers for learning. The network has been trained with five words uttered by five different deaf persons in the age group of 5–10 years. Another set of same five words uttered by same five deaf persons were used for test purposes. The recognition results for the word one, three, four is 50 to 60% and for five is 50%…But the recognition results for word two are only10% since the variability is high for two. The results can be improved by varying the parameters of the hybrid neural network.

A. Revathi

Papers

Text Independent Speaker Recognition and Speaker Independent Speech Recognition Using Iterative Clustering Approach

Speaker independent continuous speech and isolated digit recognition using VQ and HMM

Robust emotion recognition from speech: Gamma tone features and models

Text Independent Composite Speaker Identification/Verification Using Multiple Features

Speech recognition of deaf and hard of hearing people using hybrid neural network