Text-Independent Speaker Recognition in Clean and Noisy Backgrounds Using Modified VQ-LBG Algorithm

Mallikarjunan, M.; Radha, P.; Bharath, Karthik; M, Rajesh Kumar

doi:10.1007/s00034-018-0992-4

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…Mallikarjunan et al 24 adopted TI speaker recognition using a modified vector quantization‐ linde buzo and gray algorithm. In this, MFCC is used for the extraction of features and is considered as input to modified vector quantization via Linde‐Buzo‐Gray and expectation‐maximization (EM) algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic speaker recognition from speech signal using bidirectional long‐short‐term memory recurrent neural network

Devi

Thongam

2020

Computational Intelligence

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Section: Literature Reviewmentioning

confidence: 99%

Automatic speaker recognition from speech signal using bidirectional long‐short‐term memory recurrent neural network

Devi

Thongam

2020

Computational Intelligence

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“…Although SER is a widely used research topic, most existing systems are processed under ideal acoustic conditions. Noise is a major factor that affects the performance of speech related tasks [6], but it is still not well studied in the emotional context. Hence, one of the main purposes of this work is to propose an implementation of a SER system in a more realistic setting.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of a Feature-Level Fusion for Noisy Speech Emotion Recognition

2019

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Because one of the key issues in improving the performance of Speech Emotion Recognition (SER) systems is the choice of an effective feature representation, most of the research has focused on developing a feature level fusion using a large set of features. In our study, we propose a relatively low-dimensional feature set that combines three features: baseline Mel Frequency Cepstral Coefficients (MFCCs), MFCCs derived from Discrete Wavelet Transform (DWT) sub-band coefficients that are denoted as DMFCC, and pitch based features. Moreover, the performance of the proposed feature extraction method is evaluated in clean conditions and in the presence of several real-world noises. Furthermore, conventional Machine Learning (ML) and Deep Learning (DL) classifiers are employed for comparison. The proposal is tested using speech utterances of both of the Berlin German Emotional Database (EMO-DB) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) speech databases through speaker independent experiments. Experimental results show improvement in speech emotion detection over baselines.

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Combined i-Vector and Extreme Learning Machine Approach for Robust Speaker Identification and Evaluation with SITW 2016, NIST 2008, TIMIT Databases

Al-Kaltakchi

Abdullah

Woo

et al. 2021

Circuits Syst Signal Process

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Text-Independent Speaker Recognition in Clean and Noisy Backgrounds Using Modified VQ-LBG Algorithm

Cited by 6 publications

References 18 publications

Automatic speaker recognition from speech signal using bidirectional long‐short‐term memory recurrent neural network

Automatic speaker recognition from speech signal using bidirectional long‐short‐term memory recurrent neural network

An Investigation of a Feature-Level Fusion for Noisy Speech Emotion Recognition

Combined i-Vector and Extreme Learning Machine Approach for Robust Speaker Identification and Evaluation with SITW 2016, NIST 2008, TIMIT Databases

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