Acoustic Model Interpolation for Non-Native Speech Recognition

This paper proposes three interpolation techniques which use the target language and the speaker's native language to improve non-native 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.