多声学场景下端到端语音识别声学编码器的自适应  

The self-adaptation of acoustic encoder in end-to-end automatic speech recognition under diverse acoustic scenes

作　　者：刘育坤 郑霖 黎塔 张鹏远[1,2] LIU Yukun;ZHENG Lin;LI Ta;ZHANG Pengyuan(Key Laboratory of Speech Acoustics and Content Understanding,Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049)

机构地区：[1]中国科学院声学研究所语言声学与内容理解重点实验室,北京100190 [2]中国科学院大学,北京100049

出　　处：《声学学报》2023年第6期1260-1268,共9页Acta Acustica

基　　金：国家重点研发计划项目(2020AAA0108002);中国科学院声学研究所自主部署“目标导向”类项目(MBDX202106)资助。

摘　　要：提出了一种面向多样化声学场景自适应设计声学编码器的方法(SAE)。该方法通过学习不同声学场景下语音中包含的声学特征的差异,适应性地为端到端语音识别任务设计出合适的声学编码器。通过引入神经网络结构搜索技术,提高了编码器设计的有效性,从而改善了下游识别任务的性能。在Aishell-1、HKUST和SWBD三个常用的中英文数据集上的实验表明,通过所提场景自适应设计方法得到的声学编码器相比已有的声学编码器可以获得平均5%以上的错误率改善。所提方法是一种深入分析特定场景下语音特征、针对性设计高性能声学编码器的有效方法。In this paper,a scene-adaptive acoustic encoder(SAE)is proposed for different speech scenes.This method adaptively designs an appropriate acoustic encoder for end-to-end speech recognition tasks by learning the differences of acoustic features in different acoustic scenes.By the application of the neural architecture search method,the effectiveness of encoder design and the performance of downstream recognition tasks are improved.Experiments on three commonly used Chinese and English dataset,Aishell-1,HKUST and SWBD,show that the proposed SAE can achieve average 5%relative character error rate reductions than the best human-designed encoders.The results show that the proposed method is an effective method for analysis of acoustic features in specific scenes and targeted design of high-performance acoustic encoders.

关 键 词：自动语音识别 声学编码器 自适应 神经网络结构搜索 

分 类 号：TN912.34[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]