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

众所周知中文普通话被众多的地区口音强烈地影响着，然而带不同口音的普通话语音数据却十分缺乏。因此，普通话语音识别的一个重要目标是恰当地模拟口音带来的声学变化。文章给出了隐式和显式地使用口音信息的一系列基于深度神经网络的声学模型技术的研究。与此同时，包括混合条件训练，多口音决策树状态绑定，深度神经网络级联和多级自适应网络级联隐马尔可夫模型建模等的多口音建模方法在本文中被组合和比较。一个能显式地利用口音信息的改进多级自适应网络级联隐马尔可夫模型系统被提出，并应用于一个由四个地区口音组成的、数据缺乏的带口音普通话语音识别任务中。在经过序列区分性训练和自适应后，通过绝对上 0.8% 到 1.5%(相对上 6% 到 9%)的字错误率下降，该系统显著地优于基线的口音独立深度神经网络级联系统。

Investigation of Deep Neural Network Acoustic Modelling Approaches for Low Resource Accented Mandarin Speech Recognition

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 modeling 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 significantly 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.