A Multiscale Chaotic Feature Extraction Method for Speaker Recognition

Jiang, Lin; Yumei, Yi; Zhang, Maosheng; Chen, Defeng; Wang, Chao; Wang, Tonghan

doi:10.1155/2020/8810901

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…There are also some works that focus on the improvement of feature extraction. Lin et al [ 13 ] introduced a novel feature extraction approach that combines multiresolution analysis with chaotic feature extraction to improve the performance of utterance features. Daqrouq et al [ 14 ] proposed a feature extraction method based on wavelet packet transform (WPT).…”

Section: Related Workmentioning

confidence: 99%

Utterance Clustering Using Stereo Audio Channels

Dong

MacLaren

Cao

et al. 2021

Computational Intelligence and Neuroscience

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Utterance clustering is one of the actively researched topics in audio signal processing and machine learning. This study aims to improve the performance of utterance clustering by processing multichannel (stereo) audio signals. Processed audio signals were generated by combining left- and right-channel audio signals in a few different ways and then by extracting the embedded features (also called d-vectors) from those processed audio signals. This study applied the Gaussian mixture model for supervised utterance clustering. In the training phase, a parameter-sharing Gaussian mixture model was obtained to train the model for each speaker. In the testing phase, the speaker with the maximum likelihood was selected as the detected speaker. Results of experiments with real audio recordings of multiperson discussion sessions showed that the proposed method that used multichannel audio signals achieved significantly better performance than a conventional method with mono-audio signals in more complicated conditions.

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Section: Related Workmentioning

confidence: 99%

Utterance Clustering Using Stereo Audio Channels

Dong

MacLaren

Cao

et al. 2021

Computational Intelligence and Neuroscience

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“…The length of the utterances fluctuates in ASR, which sets it apart from other sequence learning challenges. The same word may be uttered several times for varying amounts of time (3) .…”

Section: Introductionmentioning

confidence: 99%

Use of Bidirectional Long Short Term Memory in Spoken Word Detection with reference to the Assamese language

Kalita¹,

Borbora²,

Nath³

2022

IJST

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Objectives :The proposed method is based on a unique technique of Deep learning for identifying spoken words with reference to Assamese language. Most of the DNN based algorithms have been successfully implemented in the field of image recognition, computer vision, natural language processing and medical picture analysis. Methods: The method used here is the Bidirectional Long Short Term Memory (BLSTM). BLSTM incorporates both past and future situations together. The speech database for this research work is hired from the repository of Indian Language Technology Proliferation and Development Center (ILTP-DC). This repository contains 32,335 utterances by 1000 numbers of male and female participants, which is comprised of 262 unique Assamese native words. The BLSTM based recognition model is using 10 out of the 262 unique words and the remaining words are used in construction or generation of synthesized sentences. The feature extraction module uses 39 feature coefficients, which are composed of MFCC, ∆MFCC and ∆∆MFCC coefficients. Findings: The Word Error Rate (WER) of the BLSTM based recognition model is 18.84% with an average accuracy of 98.12%, which sets one promising benchmark when compared to recent findings. Novelty: In this work an attempt has been made with a different approach to detect certain keywords of Assamese language by adopting deep learning methodology. The future objective of this proposed work is to improve the detection capability of this model by considering multiple DNN models together in a hybrid approach along with the inclusion of additional features.

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“…Saikia et al [2019] has used in effective data visualization [12]. Jiang et al [2020] has described statistical methods for Feature Extraction Method for Speaker Recognition very efficiently in their work with proven experimental outcomes and results [13]. Magdiel and Pilar [2021] used standardized domain adaptation techniques for classification in imagined speech recognition [14].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Speech Signal Data of Mising Vowels using Logistic Regression and K-Means Clustering

Saikia¹,

Hazarika²

2021

IJACSA

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In this paper, an attempt has been made to study and analyze speech signal data. Here, the sound or speech data has different attributes like time, pitch, formant frequencies, speaker type, Vowel No etc. The dataset used here is speech signal data which are analog in nature and has been converted to digital format. After converting the data into digital format we want to establish a Logit model to predict the speaker gender on the basis of the pitch signal values which is also considered as fundamental formant frequency. That is our objective is to predict whether a speaker is male or female by looking at the pitch value by using logistic regression. We have applied clustering techniques to visualize and interpret how it works in speech signal data. The logistic model gives us 91% accuracy rate with low and efficient AIC value where as in case of the clustering algorithm we get a 93% accuracy for the whole sample.

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A Multiscale Chaotic Feature Extraction Method for Speaker Recognition

Cited by 5 publications

References 24 publications

Utterance Clustering Using Stereo Audio Channels

Utterance Clustering Using Stereo Audio Channels

Use of Bidirectional Long Short Term Memory in Spoken Word Detection with reference to the Assamese language

Analysis of Speech Signal Data of Mising Vowels using Logistic Regression and K-Means Clustering

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