DA-IICT/IIITV System for Low Resource Speech Recognition Challenge 2018

Hardik B. Sailor, Maddala Venkata Siva Krishna, Diksha Chhabra, Ankur T. Patil, Madhu Kamble, Hemant Patil

This paper presents an Automatic Speech Recognition (ASR) system, in the Gujarati language, developed for Low Resource Speech Recognition Challenge for Indian Languages in INTERSPEECH 2018. For front-end, Amplitude Modulation (AM) features are extracted using the standard and data-driven auditory filterbanks. Recurrent Neural Network Language Models (RNNLM) are used for this task. There is a relative improvement of 36.18% and 40.95% in perplexity on the test and blind test sets, respectively, compared to 3-gram LM. TimeDelay Neural Network (TDNN) and TDNN-Long Short-Term Memory (LSTM) models are employed for acoustic modeling. The statistical significance of proposed approaches is justified using a bootstrap-based % Probability of Improvement (POI) measure. RNNLM rescoring with 3-gram LM gave an absolute reduction of 0.69-1.29% in Word Error Rate (WER) for various feature sets. AM features extracted using the gammatone filterbank (AM-GTFB) performed well on the blind test set compared to the FBANK baseline (POI>70%). The combination of ASR systems further increased the performance with an absolute reduction of 1.89 and 2.24% in WER for test and blind test sets, respectively (100% POI).

 DOI: 10.21437/Interspeech.2018-1553

Cite as: Sailor, H.B., Venkata Siva Krishna, M., Chhabra, D., Patil, A.T., Kamble, M., Patil, H. (2018) DA-IICT/IIITV System for Low Resource Speech Recognition Challenge 2018. Proc. Interspeech 2018, 3187-3191, DOI: 10.21437/Interspeech.2018-1553.

  author={Hardik B. Sailor and Maddala {Venkata Siva Krishna} and Diksha Chhabra and Ankur T. Patil and Madhu Kamble and Hemant Patil},
  title={DA-IICT/IIITV System for Low Resource Speech Recognition Challenge 2018},
  booktitle={Proc. Interspeech 2018},