A Study on Text-Independent Speaker Recognition Systems in Emotional Conditions Using Different Pattern Recognition Models

Alluri, K. N. R. K. Raju; Achanta, Sivanand; Prasath, Rajendra; Gangashetty, Suryakanth V.; Vuppala, Anil Kumar

doi:10.1007/978-3-319-58130-9_7

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Alluri et al 28 have presented the analysis of text dependent speaker identification in emotional situations. Speaker basic information was known as both the device and the source functions.…”

Section: Related Work: a Brief Reviewmentioning

confidence: 99%

A hybrid HXPLS‐TMFCC parameterization and DCNN‐SFO clustering based speaker diarization system

Sailaja

Maloji

Mannepalli

2022

Concurrency and Computation

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The speaker diarization is considered to be the process by which the speaker signal is segmented, and the speaker identity is grouped into homogenous regions. The central point behind this scheme is the ability to distinguish between the speaker signal and each speaker signal with the label. As mass communication and meetings grow quickly, the diarization of the speakers is burden to improve the readability of the speech transcript. To solve this problem, tangent weighted mel‐frequency cepstral coefficient (TMFCC) and the extended linear prediction with autocorrelation snapshot feature extraction and the speaker diarization approach proposes a deep convolutional neural network (DCNN) for clustering and optimization using sailfish optimizer. A new development in the HXLPS extraction method is the holoentropy with extended linear prediction with autocorrelation snapshot. TMFCC makes more efficient and improves the effectiveness of the proposed scheme using lesser energy frame and higher energy framework. When achieve this, the voice activity detection method can recognize speech and non‐speech signals. Therefore, every segmented signal is represented by the d‐vector. The label of the speaker signal is clustered according to the speaker label used in the DCNN. The evaluation methods, like tracking distance, false alarm rate, diarization error rate examine the effectiveness.

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Section: Related Work: a Brief Reviewmentioning

confidence: 99%

A hybrid HXPLS‐TMFCC parameterization and DCNN‐SFO clustering based speaker diarization system

Sailaja

Maloji

Mannepalli

2022

Concurrency and Computation

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“…The extraction of excitation information from the speech signal can be done using LP residual features in different domains such as temporal, cepstral, and spectral. In [10] MFCCs and Real Cepstral Coefficients (RCCs) were extracted and pattern classification algorithms like GMM, GMM-UBM and Deep Neural Networks(DNN) were used for emotional speaker recognition. DNNs had less performance degradation than GMM and GMM-UBM.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Implementations of GCI detection algorithms such as DYPSA and SEDREAMS are available on VOICEBOX Matlab toolbox. In literature, most of the speaker recognition task in emotional environment is done using spectral features such as MFCCs and modeling techniques such as GMM, GMM-UBM, SVM whereas excitation features are not explored in this area [1], [10], [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Speaker Recognition in Emotional Environment using Excitation Features

Thomas¹,

Spoorthy

Sobhana³

et al. 2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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Speaker Recognition is known as the task of recognizing the person speaking from his/her speech. Speaker recognition has many applications including transaction authentication, access control, voice dialing, web services, etc. Emotive speaker recognition is important because in real life, human beings extensively express emotions during conversations, and emotions alter the human voice. A text-independent speaker recognition system is proposed in the work. The system designed is for emotional environment. The proposed system in this work is trained using the speech samples recorded in neutral environment and the system evaluation is performed in an emotional environment. Here, excitation source features are used to represent speakerspecific details contained in speech signal. The excitation source signal is obtained after separating the segmental level features from the voice samples. The excitation source signal is almost considered as a noise so identifying a speaker in an emotive environment is a challenging task. Excitation features include Linear Prediction (LP) residual, Glottal Closure Instance (GCI), LP residual phase, residual cepstrum, Residual Mel-Frequency Cepstral Coefficient (R-MFCC), etc. A decrease in performance is observed when the system is trained with neutral speech samples and tested with emotional speech samples. Different emotions considered for emotional speaker identification are happy, sad, anger, fear, neutral, surprise, disgust, and sarcastic For the classification of speakers the algorithms used are Gaussian Mixture Model (GMM), Support Vector Machine (SVM), K-Nearest Neighbor(KNN), Random Forest and Naive Bayes.

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Speaker Modeling Using Emotional Speech for More Robust Speaker Identification

Milošević

Glavitsch³

et al. 2019

J. Commun. Technol. Electron.

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A Study on Text-Independent Speaker Recognition Systems in Emotional Conditions Using Different Pattern Recognition Models

Cited by 3 publications

References 22 publications

A hybrid HXPLS‐TMFCC parameterization and DCNN‐SFO clustering based speaker diarization system

A hybrid HXPLS‐TMFCC parameterization and DCNN‐SFO clustering based speaker diarization system

Speaker Recognition in Emotional Environment using Excitation Features

Speaker Modeling Using Emotional Speech for More Robust Speaker Identification

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