Latent class model with application to speaker diarization

He, Liang; Chen, Xianhong; Xu, Can; Liu, Yi; Liu, Jia; Johnson, Michael T.

doi:10.1186/s13636-019-0154-z

Cited by 2 publications

(1 citation statement)

References 53 publications

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“…He et al 23 suggested a latent class model (LCM) for speaker diarization. The LCM system utilizes soft details and prevents premature difficult decisions in its isolations, was similar to the variational Bayes (VB) system of Patrick Kenny.…”

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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Analysis of transition cost and model parameters in speaker diarization for meetings

Martínez-González

Pardo

Vallejo-Pinto

et al. 2021

J AUDIO SPEECH MUSIC PROC.

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There has been little work in the literature on the speaker diarization of meetings with multiple distance microphones since the publications in 2012 related to the last National Institute of Standards (NIST) Rich Transcription Evaluation Campaign in 2009 (RT09). Lately, the Second DIHARD Challenge Evaluation has also covered diarization at dinner party meetings that include multiple distant microphones. Dinner party meetings are somehow harder than office meetings because their participants can move freely around the room. In this paper, we studied some of the algorithms on speaker diarization for meetings with multiple distant microphones for the NIST Rich Transcription Evaluation Campaign in 2007 (RT07) and RT09 and provide definite and clear improvements. On the one hand, little or no care has been taken to the problem of penalizing or favoring transitions between speakers other than proposing a minimum duration of a speaker turn or calculating the speakers’ probabilities using Variational Bayes (VB). We have studied this issue and determined that a transition penalty term is needed that should be independent both of the number of active speakers and the minimum duration of speaker turns. On the other hand, the determination of a method to automatically select the right number of parameters is crucial in developing good models for speakers. Previous studies have proposed the dynamic selection of the number of parameters based on the duration of the speaker’s speech with a mixed performance when tested at one distant microphone meetings or multiple distant microphones meetings. In this paper, we propose a new method that takes into account both the duration of speaker’s speech to determine a minimum number of parameters, and the question of overfitting issue to determine a maximum number of them, also taking into account the computation time in order to reduce it.We have carried out experiments to support our findings, and we have been able to improve our baseline speaker error rate with multiple distant-microphone meetings. Both methods achieve improved performance over the baseline. The first method obtains a 21.6% decrease in relative speaker error for the development set and a 4.6% decrease in relative speaker error for the test set (RT09). The second method obtains a 46.47% decrease in relative speaker error for the development set and a 17.54% decrease in relative speaker error for the test set. Both methods complement each other, and when they are applied in combination, we obtain a 47.2% decrease in relative speaker error for the development set and a 22.02% decrease in relative speaker error for the test set.The performance obtained with our proposal is outstanding in some subsets of the development test such as the NIST RT07 and among the best for RT09 using our proposed simple modifications. Furthermore, with our algorithm we obtain gains in computation time without jeopardizing performance. Results with a different publicly available database, augmented multiparty interaction (AMI) obtains a 28.44% decrease in relative speaker error confirming the validity of our methods. Preliminary experiments with a single stream (mfcc) endorse the validity of our findings. Comparisons with an x-vector system deliver superior performance of our system on unseen test data.

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Latent class model with application to speaker diarization

Cited by 2 publications

References 53 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

Analysis of transition cost and model parameters in speaker diarization for meetings

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