Using English Acoustic Models for Hindi Automatic Speech Recognition

Bilingual speakers of Hindi and English often mix English and Hindi together in their everyday conversations. This motivates us to build a mix language Hindi-English recognizer. For this purpose, we need well-trained English and Hindi recognizers. For training our English recognizer we have at our disposal many hours of annotated English speech data. For Hindi, however, we have very limited resources. Therefore, in this paper we are proposing methods for rapid development of a Hindi speech recognizer using (i) trained English acoustic models to replace Hindi acoustic models; and (ii) adapting Hindi acoustic models from English acoustic models using Maximum Likelihood Linear Regression. We propose using data-driven methods for both substitution and adaptation. Our proposed recognizer has an accuracy of 96% for recognizing isolated Hindi words.