Adapting monolingual resources for code-mixed hindi-english speech recognition

Ayushi Pandey, Brij Mohan Lal Srivastava, Suryakanth V. Gangashetty

Abstract: The paper presents an automatic speech recognition (ASR) system for code-mixed read speech in Hindi-English, developed upon the extrapolation of monolingual training resources. A monolingual Hindi acoustic model, mixed with code-mixed speech data has been implemented to train a neural network based speech recognition framework. The testing corpus also follows a similar structure: containing data from both monolingual and code-mixed speech. The shared phonetic transcription, captured in WX notation has been exploited to harness the commonality between the pooled phonesets of Hindi and English. The experiments have been conducted in two separate formulations of a trigram based language model 1) In the first experiment, the language model contains no out-of-vocabulary words, as the test utterances are included in the training of the language model. The word error rate in this case has been obtained to be 10.63 %. 2) In the second experiment, the testing utterances have been excluded from the training language model. The word error rate in this case has been obtained to be 41.66 %.