Improved acoustic models for spontaneous speech recognition

2012 ◽  
Vol 131 (4) ◽  
pp. 3236-3236
Author(s):  
Qingqing Zhang ◽  
Shang Cai ◽  
Jielin Pan ◽  
Yonghong Yan
Keyword(s):  
Speech Recognition ◽  
Spontaneous Speech ◽  
Acoustic Models

Improving Acoustic Models for Russian Spontaneous Speech Recognition

2015 ◽  
pp. 234-242 ◽  
Author(s):  
Alexey Prudnikov ◽  
Ivan Medennikov ◽  
Valentin Mendelev ◽  
Maxim Korenevsky ◽  
Yuri Khokhlov
Keyword(s):  
Speech Recognition ◽  
Spontaneous Speech ◽  
Acoustic Models

scholarly journals Towards Robust Indonesian Speech Recognition with Spontaneous-Speech Adapted Acoustic Models

2016 ◽  
Vol 81 ◽  
pp. 167-173 ◽  
Author(s):  
Devin Hoesen ◽  
Cil Hardianto Satriawan ◽  
Dessi Puji Lestari ◽  
Masayu Leylia Khodra
Keyword(s):  
Speech Recognition ◽  
Spontaneous Speech ◽  
Acoustic Models

scholarly journals A De Novo Divide-and-Merge Paradigm for Acoustic Model Optimization in Automatic Speech Recognition

2020 ◽  
Author(s):  
Conghui Tan ◽  
Di Jiang ◽  
Jinhua Peng ◽  
Xueyang Wu ◽  
Qian Xu ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
De Novo ◽  
Superior Performance ◽  
Acoustic Model ◽  
Acoustic Models ◽  
Public Data ◽  
Speech Data ◽  
Low Efficiency ◽  
Novel Algorithms

Due to the rising awareness of privacy protection and the voluminous scale of speech data, it is becoming infeasible for Automatic Speech Recognition (ASR) system developers to train the acoustic model with complete data as before. In this paper, we propose a novel Divide-and-Merge paradigm to solve salient problems plaguing the ASR field. In the Divide phase, multiple acoustic models are trained based upon different subsets of the complete speech data, while in the Merge phase two novel algorithms are utilized to generate a high-quality acoustic model based upon those trained on data subsets. We first propose the Genetic Merge Algorithm (GMA), which is a highly specialized algorithm for optimizing acoustic models but suffers from low efficiency. We further propose the SGD-Based Optimizational Merge Algorithm (SOMA), which effectively alleviates the efficiency bottleneck of GMA and maintains superior performance. Extensive experiments on public data show that the proposed methods can significantly outperform the state-of-the-art.

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