Enhancing Large Vocabulary Continuous Speech Recognition System for Urdu-English Conversational Code-Switched Speech

This paper presents first step towards Large Vocabulary Continuous Speech Recognition (LVCSR) system for Urdu-English code-switched conversational speech. Urdu is the national language and lingua franca of Pakistan, with 100 million speakers worldwide. English, on the other hand, is official language of Pakistan and commonly mixed with Urdu in daily communication. Urdu, being under-resourced language, have no substantial Urdu-English code-switched corpus in hand to develop speech recognition system. In this research, readily available spontaneous Urdu speech corpus (25 hours) is revised to use it for enhancement of read speech Urdu LVCSR to recognize code-switched speech. This data set is split into 20 hours of train and 5 hours of test set. 10 hours of Urdu BroadCast (BC) data are collected and annotated in a semi-supervised way to enhance the system further. For acoustic modeling, state-of-the-art DNN-HMM modeling technique is used without any prior GMM-HMM training and alignments. Various techniques to improve language model using monolingual data are investigated. The overall percent Word Error Rate (WER) is reduced from 40.71% to 26.95% on test set.