A Phone Mapping Technique for Acoustic Modeling of Under-Resourced Languages

This paper presents a novel method for acoustic modeling of a new language with a limited amount of training data. In this approach, we use well-trained acoustic models of a foreign language to generate acoustic scores for each feature vector of the target language. These scores are then used as the input for mapping to context dependent triphones of the target language using a limited amount of training data. With this approach, we do not need to modify or have a special requirement for the foreign acoustic models. In this paper, English is used as the foreign language while Malay is used as the target language. Experiments on a Malay large vocabulary continuous speech recognition (LVCSR) task show that with using only few minutes of training data we can achieve a low word error rate which outperforms the best monolingual baseline acoustic model significantly.