Improving Acoustic Models for Russian Spontaneous Speech Recognition

被引：10
|
作者：
Prudnikov, Alexey [1 ,2 ]
Medennikov, Ivan [2 ,3 ]
Mendelev, Valentin [1 ]
Korenevsky, Maxim [1 ,2 ]
Khokhlov, Yuri [3 ]
机构：
[1] Speech Technol Ctr Ltd, St Petersburg, Russia
[2] ITMO Univ, St Petersburg, Russia
[3] STC Innovat Ltd, St Petersburg, Russia
来源：
SPEECH AND COMPUTER (SPECOM 2015) | 2015年 / 9319卷
关键词：
Speech recognition; Russian spontaneous speech; Deep neural networks; Speaker adaptation; I-vectors; Bottleneck features; ADAPTATION;
D O I：
10.1007/978-3-319-23132-7_29
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The aim of the paper is to investigate the ways to improve acoustic models for Russian spontaneous speech recognition. We applied the main steps of the Kaldi Switchboard recipe to a Russian dataset but obtained low accuracy with respect to the results for English spontaneous telephone speech. We found two methods to be especially useful for Russian spontaneous speech: the i-vector based deep neural network adaptation and speaker-dependent bottleneck features which provide 8.6% and 11.9% relative word error rate reduction over the baseline system respectively.
引用
收藏
页码：234 / 242
页数：9
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