scholarly journals Gaussian mixture models for adaptation of deep neural network acoustic models in automatic speech recognition systems

2016 ◽  
pp. 1063-1072
Author(s):  
N.A. Tomashenko ◽  
Yu.Yu. Khokhlov ◽  
A. Larcher ◽  
Ya. Estève ◽  
Yu.N. Matveev
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Mixture Models ◽  
Automatic Speech Recognition ◽  
Deep Neural Network ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Acoustic Models ◽  
Recognition Systems

Adaptation of Deep Neural Network Acoustic Models for Robust Automatic Speech Recognition

2017 ◽  
pp. 219-243 ◽  
Author(s):  
Khe Chai Sim ◽  
Yanmin Qian ◽  
Gautam Mantena ◽  
Lahiru Samarakoon ◽  
Souvik Kundu ◽  
...  
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Deep Neural Network ◽  
Acoustic Models

scholarly journals UCSY-SC1: A Myanmar speech corpus for automatic speech recognition

2019 ◽  
Vol 9 (4) ◽  
pp. 3194 ◽  
Author(s):  
Aye Nyein Mon ◽  
Win Pa Pa ◽  
Ye Kyaw Thu
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Gaussian Mixture ◽  
Error Rates ◽  
Training Data ◽  
Speech Corpus ◽  
Total Size ◽  
Test Sets ◽  
Web News

This paper introduces a speech corpus which is developed for Myanmar Automatic Speech Recognition (ASR) research. Automatic Speech Recognition (ASR) research has been conducted by the researchers around the world to improve their language technologies. Speech corpora are important in developing the ASR and the creation of the corpora is necessary especially for low-resourced languages. Myanmar language can be regarded as a low-resourced language because of lack of pre-created resources for speech processing research. In this work, a speech corpus named UCSY-SC1 (University of Computer Studies Yangon - Speech Corpus1) is created for Myanmar ASR research. The corpus consists of two types of domain: news and daily conversations. The total size of the speech corpus is over 42 hrs. There are 25 hrs of web news and 17 hrs of conversational recorded data.<br />The corpus was collected from 177 females and 84 males for the news data and 42 females and 4 males for conversational domain. This corpus was used as training data for developing Myanmar ASR. Three different types of acoustic models  such as Gaussian Mixture Model (GMM) - Hidden Markov Model (HMM), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) models were built and compared their results. Experiments were conducted on different data  sizes and evaluation is done by two test sets: TestSet1, web news and TestSet2, recorded conversational data. It showed that the performance of Myanmar ASRs using this corpus gave satisfiable results on both test sets. The Myanmar ASR  using this corpus leading to word error rates of 15.61% on TestSet1 and 24.43% on TestSet2.<br /><br />

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