In many automatic speech recognition (ASR) tasks, an ideal model has to be
applicable over multiple domains. In this paper, we propose to teach an
all-rounder with experts in different domains. Concretely, we build a
multi-domain acoustic model by applying the teacher-student training framework.
First, for each domain, a teacher model (domain-dependent model) is trained by
fine-tuning a multi-condition model with domain-specific subset. Then all these
teacher models are used to teach one single student model simultaneously. We
perform experiments on two predefined domain setups. One is domains with
different speaking styles, the other is nearfield, far-field and far-field with
noise. Moreover, two types of models are examined: deep feedforward sequential
memory network (DFSMN) and long short term memory (LSTM). Experimental results
show that the model trained with this framework outperforms not only
multi-condition model but also domain-dependent model. Specially, our training
method provides up to 10.4% relative character error rate improvement over
baseline model (multi-condition model).

本文提出一种基于多领域音频模型的教师 - 学生训练框架，成功地构建了一个应用于多个领域的音频模型，通过对两种不同域名的语音风格实验验证效果，取得了比基线模型（多条件模型）更好的结果，相对字符错误率提高达到了 10.4%。