This paper presents the development of the speech, text and pronunciation dictionary resources required to build a large vocabulary speech recognizer for the Malay language. This project is a collaboration project among three universities: USM, MMU from Malaysia and NTUfrom Singapore. The Malay speech corpus consists of read speech (speaker independent/ dependent and accent independent/ dependent) and broadcast news. To date, 90 speakers have been recorded which is equal to a total ofnearly 70 hours of read speech, and 10 hours of broadcast news from local TV stations in Malaysia was transcribed. The text corpus consists of 700Mbytes of data extracted from Malaysia's local news web pages from 1998-2008 and a rule based G2P tool is develop to generate the pronunciation dictionary.
This paper proposes three interpolation techniques which use the target language and the speaker's native language to improve nonnative speech recognition system. These interpolation techniques are manual interpolation, weighted least square and eigenvoices. Each of them can be used under different situation and constraints. In contrast to weighted least square and eigenvoices methods, manual interpolation can be achieved offline without any adaptation data. These methods can also be combined with MLLR to improve the recognition rate. Experiments presented in this paper show that the best non native adaptation method, combined with MLLR can give 10% WER absolute reduction on a French automatic speech recognition system for both Chinese and Vietnamese native speakers.
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