Environment adaptation for robust speaker verification by cascading maximum likelihood linear regression and reinforced learning

2007 ◽  
Vol 21 (2) ◽  
pp. 231-246 ◽  
Author(s):  
K.K. Yiu ◽  
M.W. Mak ◽  
S.Y. Kung
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Speaker Verification ◽  
Environment Adaptation ◽  
Reinforced Learning ◽  
Maximum Likelihood Linear Regression

scholarly journals Discriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling

2020 ◽  
Vol 30 (1) ◽  
pp. 165-179
Author(s):  
A. Kumar ◽  
R.K. Aggarwal
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Maximum Likelihood ◽  
Linear Regression ◽  
Recurrent Neural Network ◽  
Gaussian Mixture ◽  
Language Modeling ◽  
Baseline System ◽  
Maximum Likelihood Linear Regression ◽  
Asr System

Abstract This paper implements the continuous Hindi Automatic Speech Recognition (ASR) system using the proposed integrated features vector with Recurrent Neural Network (RNN) based Language Modeling (LM). The proposed system also implements the speaker adaptation using Maximum-Likelihood Linear Regression (MLLR) and Constrained Maximum likelihood Linear Regression (C-MLLR). This system is discriminatively trained by Maximum Mutual Information (MMI) and Minimum Phone Error (MPE) techniques with 256 Gaussian mixture per Hidden Markov Model(HMM) state. The training of the baseline system has been done using a phonetically rich Hindi dataset. The results show that discriminative training enhances the baseline system performance by up to 3%. Further improvement of ~7% has been recorded by applying RNN LM. The proposed Hindi ASR system shows significant performance improvement over other current state-of-the-art techniques.

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