Speaker identification features extraction methods: A systematic review

Tirumala, Sreenivas Sremath; Shahamiri, Seyed Reza; Garhwal, Abhimanyu Singh; Wang, Ruili

doi:10.1016/j.eswa.2017.08.015

Cited by 157 publications

(69 citation statements)

References 76 publications

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“…In the review analysis, the existing proposals and implementations of methods for identifying the features of the identification system were considered. Analysis of the results shows that MFCC-based approaches have been used more than any other approach and, moreover, it was revealed that the current trend of the identification system research is to solve important identification system problems, such as adaptability, complexity, multilingual recognition and noise resistance [9].…”

Section: Related Workmentioning

confidence: 99%

Voice Identification Using Classification Algorithms

Mamyrbayev¹,

Mekebayev²,

Turdalyuly³

et al. 2020

Intelligent System and Computing

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This article discusses the classification algorithms for the problem of personality identification by voice using machine learning methods. We used the MFCC algorithm in the speech preprocessing process. To solve the problem, a comparative analysis of five classification algorithms was carried out. In the first experiment, the support vector method was determined-0.90 and multilayer perceptron-0.83, that showed the best results. In the second experiment, a multilayer perceptron with an accuracy of 0.93 was proposed using the Robust scaler method for personal identification. Therefore, to solve this problem, it is possible to use a multi-layer perceptron, taking into account the specifics of the speech signal.

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Section: Related Workmentioning

confidence: 99%

Voice Identification Using Classification Algorithms

Mamyrbayev¹,

Mekebayev²,

Turdalyuly³

et al. 2020

Intelligent System and Computing

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“…In literatures, pure Mel-Frequency Cepstral Coefficients (MFCCs) based feature extraction approaches have been used more than any other approaches. Furthermore, other MFCC variations, such as MFCC fusion and cleansing approaches, are proven to be very popular as well [7].…”

Section: Literature Reviewsmentioning

confidence: 99%

“…Such CMS coefficients are the features extracted by MFCCs method. That means, MFCCs based feature extracted method used frequency analysis based on progressing the speech frame through filter bank [7]. The result from the method is the extracted features in a form of MFCCs coefficients.…”

Section: Mel-frequency Cepstral Coefficients (Mfccs)mentioning

confidence: 99%

“…It can be thought that verification is a special case of open-set identification. Where, speaker identification 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 [7]. In our study, the method for text-independent speaker identification is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…The classical Mel-frequency cepstral coefficients (MFCCs) method is likely the most popular feature extraction strategy used to date [6] for audio and speech signal. From literature, most of the state-of-the-art speaker identification systems use MFCC as feature extractor, and then feed these features to GMM-based approaches for creating speaker models for identification [7]. Nevertheless, the researchers are primary proposed the techniques suitable for a large amount of speech in training and test [8].…”

Section: Introductionmentioning

confidence: 99%

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Text-Independent Speaker Identification Using Deep Learning Model of Convolution Neural Network

Bunrit¹,

Inkian²,

Kerdprasop³

et al. 2019

IJMLC

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Speaker recognition approach can be categorized into speaker identification and speaker verification. These two subfields have a bit varied in definition from domain usage. If we has a voice input, the goal of speaker verification is for authentication by determining an answer from a question: "is the voice someone's voice?" For speaker identification, will try to find an answer: "the voice is whose voice?" It can be thought that verification is a special case of open-set identification. In this work, deep learning model using a convolution neural network (CNN) for speaker identification is proposed. The voice input to the method is no constrained on the words the speaker speaks. That means it is in a form of text-independent of which more difficult than text-dependent system. By the method, each 2 seconds of the speaker voice is transform to a spectrogram image and input to the generated CNN model training from scratch. The proposed CNN based method is compared to the classic Mel-frequency cepstral coefficients (MFCCs) based featured extraction method classified by support vector machine (SVM). Where, up to date, MFCC is the most popular feature extracted method for audio and speech signal. Our proposed method that the spectrogram image is used as an input is also compared to a case when image of raw signal wave is employed to the CNN model. Experiments are conducted on the speech from five speakers speak in Thai language of which voices are extracted from YouTube. It reveals the proposed CNN based method trains on spectrogram image of voice is the best compared to the other two methods. The average classification results of the testing set by the proposed method is 95.83%. For MFCC based method is 91.26% and for CNN model trained on image of raw signal wave is only 49.77%. The proposed method is very efficient when only short utterance of voice is used as an input.Index Terms-Convolution neural network (CNN), deep learning, speaker recognition, speaker identification, text-independent.

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Nearest Neighbor Classification Approach for Bilingual Speaker and Gender Recognition

Mohdiwale

Sahu

2019

Advances in Biometrics

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Speaker identification features extraction methods: A systematic review

Cited by 157 publications

References 76 publications

Voice Identification Using Classification Algorithms

Voice Identification Using Classification Algorithms

Text-Independent Speaker Identification Using Deep Learning Model of Convolution Neural Network

Nearest Neighbor Classification Approach for Bilingual Speaker and Gender Recognition

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