A computationally efficient approach to warp factor estimation in VTLN using EM algorithm and sufficient statistics

Akhil, P T; Rath, Shakti P.; Umesh, S.; Sanand, D. Rama

doi:10.21437/interspeech.2008-382

Cited by 18 publications

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Multiple background models for speaker verification using the concept of vocal tract length and MLLR super-vector

Sarkar

Umesh

2012

Int J Speech Technol

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In this paper, we investigate the use of Multiple Background Models (M-BMs) in Speaker Verification (SV). We cluster the speakers using either their Vocal Tract Lengths (VTLs) or by using their speaker specific Maximum Likelihood Linear Regression (MLLR) supervector, and build a separate Background Model (BM) for each such cluster. We show that the use of M-BMs provide improved performance when compared to the use of a single/gender wise Universal Background Model (UBM). While the computational complexity during test remains same for both M-BMs and UBM, M-BMs require switching of models depending on the claimant and also scorenormalization becomes difficult. To overcome these problems, we propose a novel method which aggregates the information from Multiple Background Models into a single gender independent UBM and is inspired by conventional Feature Mapping (FM) technique. We show that using this approach, we get improvement over the conventional UBM method, and yet this approach also permits easy use of score-normalization techniques. The proposed method provides relative improvement in Equal-Error Rate (EER) by 13.65 % in the case of VTL clustering, and 15.43 % in the case of MLLR super-vector when compared to the conventional single UBM system. When AT-norm scorenormalization is used then the proposed method provided a relative improvement in EER of 20.96 % for VTL clustering and 22.48 % for MLLR super-vector based clustering. Furthermore, the proposed method is compared with the gender dependent speaker verification system using Gaussian Mixture Model-Support Vector Machines (GMM-SVM) supervector linear kernel. The experimental results show that the proposed method perform better than gender dependent speaker verification system.

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Multiple background models for speaker verification using the concept of vocal tract length and MLLR super-vector

Sarkar

Umesh

2012

Int J Speech Technol

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A computationally efficient approach for acoustic class specific VTLN using regression tree

Rath

2018

Int J Speech Technol

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Studies on inter-speaker variability in speech and its application in automatic speech recognition

Umesh

2011

Sadhana

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In this paper, we give an overview of the problem of inter-speaker variability and its study in many diverse areas of speech signal processing. We first give an overview of vowel-normalization studies that minimize variations in the acoustic representation of vowel realizations by different speakers. We then describe the universal-warping approach to speaker normalization which unifies many of the vowel normalization approaches and also shows the relation between speech production, perception and auditory processing. We then address the problem of inter-speaker variability in automatic speech recognition (ASR) and describe techniques that are used to reduce these effects and thereby improve the performance of speaker-independent ASR systems.

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A computationally efficient approach to warp factor estimation in VTLN using EM algorithm and sufficient statistics

Cited by 18 publications

References 0 publications

Multiple background models for speaker verification using the concept of vocal tract length and MLLR super-vector

Multiple background models for speaker verification using the concept of vocal tract length and MLLR super-vector

A computationally efficient approach for acoustic class specific VTLN using regression tree

Studies on inter-speaker variability in speech and its application in automatic speech recognition

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