Recent Progress of Mandrain Spontaneous Speech Recognition on Mandrain Conversation Dialogue Corpus

Abstract

This paper presents a progress report on a relatively difficult ASR task on a spontaneous speech corpus - Mandarin Conversational Dialogue Corpus (MCDC). A DNN-based acoustic model is constructed based on the CLDNN structure with a large dataset that comprises two spontaneous-speech corpora and one read-speech corpus. The study uses a large text dataset formed by seven corpora to train an efficient general language model (LM). Two adapted LMs specially for spontaneous speech recognition are also constructed. Experimental results showed that the best performances of 26.3% in character error rate (CER) and 32.5% in word error rate (WER) were reached on MCDC. They represented 27.9% and 22.2% of relative CER and WER reductions as compared with the performances by the previous best HMM-based method. This confirms that the proposed method is promising in tackling on Mandarin spontaneous speech recognition.