集成技术Issue(6)：26-36,11.
深度神经网络建模方法用于数据缺乏的带口音普通话语音识别的研究
Investigation of Deep Neural Network Acoustic Modelling Approaches for Low Resource Accented Mandarin Speech Recognition
摘要
Abstract
The Mandarin Chinese language is known to be strongly influenced by a rich set of regional accents, while Mandarin speech with each accent is of quite low resource. Hence, an important task in Mandarin speech recognition is to appropriately model the acoustic variabilities imposed by accents. In this paper, an investigation of implicit and explicit use of accent information on a range of deep neural network (DNN) based acoustic modeling techniques was conducted. Meanwhile, approaches of multi-accent modelling including multi-style training, multi-accent decision tree state tying, DNN tandem and multi-level adaptive network (MLAN) tandem hidden Markov model (HMM) modelling were combined and compared. On a low resource accented Mandarin speech recognition task consisting of four regional accents, an improved MLAN tandem HMM systems explicitly leveraging the accent information was proposed, and signiifcantly outperformed the baseline accent independent DNN tandem systems by 0.8%-1.5% absolute (6%-9% relative) in character error rate after sequence level discriminative training and adaptation.关键词
语音识别/决策树/深度神经网络/口音/自适应Key words
speech recognition/decision tree/deep neural network/accent/adaptation分类
信息技术与安全科学引用本文复制引用
谢旭荣,隋相,刘循英,王岚..深度神经网络建模方法用于数据缺乏的带口音普通话语音识别的研究[J].集成技术,2015,(6):26-36,11.基金项目
Foundation:National Natural Science Foundation of China(NSFC 61135003)；Shenzhen Fundamental Research Program (JCYJ20130401170306806，JC201005280621A) （NSFC 61135003）
