基于拼音约束联合学习的汉语语音识别

当前的语音识别模型在英语、法语等表音文字中已取得很好的效果。然而,汉语是一种典型的表意文字,汉字与语音没有直接的对应关系,但拼音作为汉字读音的标注符号,与汉字存在相互转换的内在联系。因此,在汉语语音识别中利用拼音作为解码时的约束,可以引入一种更接近语音的归纳偏置。该文基于多任务学习框架,提出一种基于拼音约束联合学习的汉语语音识别方法,以端到端的汉字语音识别为主任务,以拼音语音识别为辅助任务,通过共享编码器,同时利用汉字与拼音识别结果作为监督信号,增强编码器对汉语语音的表达能力。实验结果表明,相比基线模型,该文提出的方法取得了更优的识别效果,词错误率降低了2.24%。

Chinese Speech Recognition Based on Pinyin Constraint and Joint Learning

In contrast to phonetic languages achieving good performance of Automatic Speech Recognition (ASR) like English and France, Chinese is a logographic language without direct association with its pronunciation. To employ Pinyin which is the symbol system for the pronunciation of Chinese words, we propose an Automatic Speech Recognition method using Pinyin as a constraint for the decoding via multi-task learning framework. We introduce both Pinyin and Chinese character supervising signal to enhance the Chinese speech representing ability in the shared encoder, with Chinese character target ASR as the primary task and Pinyin target ASR as the auxiliary task. Experiments show that the proposed model gains a better recognition result with 2.24% reduction of the word error rate (WER).