Discriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling
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 (...
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De Gruyter
2020
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oai:doaj.org-article:d4a0a539143344e18b0d33ff422f1ec42021-12-05T14:10:51ZDiscriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling2191-026X10.1515/jisys-2018-0417https://doaj.org/article/d4a0a539143344e18b0d33ff422f1ec42020-07-01T00:00:00Zhttps://doi.org/10.1515/jisys-2018-0417https://doaj.org/toc/2191-026XThis 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.Kumar A.Aggarwal R.K.De Gruyterarticleautomatic speech recognitionmfccgfccwerbcplpdiscriminative trainingmmimpernn lmScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 165-179 (2020) |
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automatic speech recognition mfcc gfcc werbc plp discriminative training mmi mpe rnn lm Science Q Electronic computers. Computer science QA75.5-76.95 |
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automatic speech recognition mfcc gfcc werbc plp discriminative training mmi mpe rnn lm Science Q Electronic computers. Computer science QA75.5-76.95 Kumar A. Aggarwal R.K. Discriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling |
description |
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. |
format |
article |
author |
Kumar A. Aggarwal R.K. |
author_facet |
Kumar A. Aggarwal R.K. |
author_sort |
Kumar A. |
title |
Discriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling |
title_short |
Discriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling |
title_full |
Discriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling |
title_fullStr |
Discriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling |
title_full_unstemmed |
Discriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling |
title_sort |
discriminatively trained continuous hindi speech recognition using integrated acoustic features and recurrent neural network language modeling |
publisher |
De Gruyter |
publishDate |
2020 |
url |
https://doaj.org/article/d4a0a539143344e18b0d33ff422f1ec4 |
work_keys_str_mv |
AT kumara discriminativelytrainedcontinuoushindispeechrecognitionusingintegratedacousticfeaturesandrecurrentneuralnetworklanguagemodeling AT aggarwalrk discriminativelytrainedcontinuoushindispeechrecognitionusingintegratedacousticfeaturesandrecurrentneuralnetworklanguagemodeling |
_version_ |
1718371691466326016 |