Mandarin-English bilingual Speech Recognition for real world music retrieval

Abstract

This paper presents our recent work on the development of a grammar-constrained, Mandarin-English bilingual Speech Recognition System (MESRS) for real world music retrieval. In order to balance the performance and the complexity of the bilingual SR system, an unified single set of bilingual acoustic models derived by phone clustering is developed. A novel Two-pass phone clustering method based on Confusion Matrix (TCM) is presented and compared with the log-likelihood measure method. In order to deal with the Mandarin accent in spoken English, different non-native adaptation approaches are investigated. With the effective incorporation of approaches on phone clustering and non-native adaptation, the Phrase Error Rate (PhrER) of MESRS for English utterances was reduced by 24.5% relatively compared to the baseline monolingual English system while the PhrER on Mandarin utterances was comparable to that of the baseline monolingual Mandarin system, and the performance for bilingual code-mixing utterances achieved 22.4% relative PhrER reduction.