A novel approach for speaker diarization system using TMFCC parameterization and Lion optimization

Ramaiah, V. Subba; Rao, R. Srinivasa

doi:10.1007/s11771-017-3678-3

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…This section describes the performance analysis of the proposed SDS‐HXLP‐DCNN‐SOA speaker diarization scheme depending on metrics, namely, tracking distance, FAR, DER. The performance is likened to the existing methods, such as real‐time implementation of speaker diarization system on Raspberry PI3 utilizing TLBO clustering algorithm (SDS‐RPi3‐TLBO), 35 deep self‐supervised hierarchical clustering for speaker diarization (SDS‐TDNN), 36 meta‐learning with latent space clustering in generative adversarial network for speaker diarization (SDS‐MCGAN), 37 A new method for speaker diarization system utilizing TMFCC parameterization with lion optimization (SDS‐TMFCC‐DNN‐LOA), 38 speaker diarization system utilizing HXLPS with deep neural network (SDS‐HXLP‐DNN), 39 respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In this article, a hybrid speaker diarization system is proposed to cluster the speech operation of the audio streams observed in the same speaker classes. The characteristics of the voice signals are derived using holoentropy with extended linear prediction with autocorrelation snapshot (HXLPS) 19 and tangent weighted mel‐frequency cepstral coefficient (TMFCC) 20 . The HXLPS‐TMFCC increases the system's efficiency by independently protecting both the low energy and high energy thresholds, and by enhancing the accuracy of detection of the voice activity detection (VAD).…”

Section: Introductionmentioning

confidence: 99%

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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: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Subba Ramaiah, V., & Rajeswara Rao, R. [3] modeled a speaker diarization method based on Lion optimization and Tangent weighted Mel frequency cepstral coefficient (TMFCC). In this method, the Lion optimization was used for clustering the audio stream that was detection in the voice activity into particular speaker groups.…”

Section: Literature Reviewmentioning

confidence: 99%

“… The speaker diarization method based on Lion optimization and TMFCC provided better tracking accuracy but had lower tracking distance. Thus, the challenge lies in improving the tracking distance of the speech signal [3].  The challenge of the speaker diarization based on NME was analyzing the ratios of both the tuning parameter to the values of NME and the generalization of the different data on the production system [4].…”

Section: Challengesmentioning

confidence: 99%

“…The speaker diarization method has become the challenging task due to the development of the recorded speech that consists of audio broadcasts, voice mails, television and meeting [1]. The conversation of the speaker is found from the mixed signal that consists of speech signal of several persons [3]. The speech signals are segmented and the speech signals of the same speakers are grouped together in speaker diarization process.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Speaker Diarization using Deep LSTM in Audio Lecturing of e-Khool Platform

Rajeyyagari¹

2020

JNACS

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The speaker diarization is the process of segmentation and the grouping of the input speech signal into a region based on the identity of the speaker. The main challenge in the speaker diarization method is improving the readability of the speech transcription. Hence, in order to overcome the challenge, a speaker diarization method based on deep LSTM is proposed in this research. Initially, the pre-processing is performed for the removal of the noise from the audio lecturing of E-Khoolusers. Then, Linear Predictive Coding (LPC) is used for the extraction of the efficient features from the audio lectures of the E-Khoolusers. After the extraction of the features, the absence or presence of the speaker in the audio lecture is detected using the VAD technique which is followed by the segmentation of the speaker using the extracted features. Finally, the feature vector is determined and the speaker from the audio lecturing of the E-Khoolusers is clustered using the deepLSTM. The proposed speaker diarization method based on deep LSTM is evaluated using the metrics, such as sensitivity, accuracy and specificity. When compared with the existing speaker diarization methods, the proposed speaker diarization method based on deep LSTM obtained a minimum DER of 0.0623, minimum false alarm rate of 0.0369, and minimum distance of 2546 for varying frame length and obtained a minimum DER of 0.0923, minimum false alarm rate of 0.0869, and minimum distance of 1146 for varying Lambda.

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Filter algorithm based on cochlear mechanics and neuron filter mechanism and application on enhancement of audio signals

Gao

Kan

Zha

2021

J. Cent. South Univ.

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A novel approach for speaker diarization system using TMFCC parameterization and Lion optimization

Cited by 6 publications

References 16 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

Automatic Speaker Diarization using Deep LSTM in Audio Lecturing of e-Khool Platform

Filter algorithm based on cochlear mechanics and neuron filter mechanism and application on enhancement of audio signals

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