基于中文语义−音韵信息的语音识别文本校对模型

Chinese semantic and phonological information-based text proofreading model for speech recognition

为了研究拼音对检测和纠正语音识别文本错误的影响,提出了一种基于中文语义−音韵信息的文本校对模型。定义了5种拼音编码方法构建字符−音韵嵌入向量,以此作为基于GRU的Seq2Seq模型的输入,并应用注意力机制提取语句的语义−音韵信息来校对语音识别文本错误。针对标注语料不足的问题,提出了一种基于拼音声韵置换的数据增强方法。在AISHELL-3公开数据集的实验结果表明,拼音携带的音韵信息有利于校对语音识别文本错误,所提方法可提升模型的检错性能。

To study the influence of Chinese Pinyin on detecting and correcting text errors in speech recognition,a text proofreading model based on Chinese semantic and phonological information was proposed.Five Pinyin coding methods were designed to construct the character-Pinyin embedding vector that was employed as the input of the Seq2Seq model based on gated recurrent unit.At the same time,the attention mechanism was adopted to extract the Chinese semantic and phonological information of sentences to correct speech recognition errors.Aiming at the problem of insufficient labeled corpus,a data augmentation method was introduced,which could automatically obtain annotated corpora by exchanging the initials or finals of Chinese Pinyin.The experimental results on AISHELL-3’s public data show that phonological in-formation is conducive to the text proofreading model to detect and correct text errors after speech recognition,and the proposed data augmentation method can improve the error detection performance of the model.