Bangla Voice Command Recognition in end-to-end System Using Topic Modeling based Contextual Rescoring
Nafis Sadeq, Shafayat Ahmed, Sudipta Saha Shubha, Md. Nahidul Islam, Muhammad Abdullah Adnan
Abstract
In this work, we perform contextual rescoring using multi-label topic modeling to improve the performance of an End-to-End Bangla voice command recognition system. We use a hybrid of Connectionist Temporal Classification (CTC) and Attention mechanism in our End-to-End architecture. We use Recurrent Neural Network (RNN) as language model and La-beled LDA (Latent Dirichlet allocation) for contextual rescoring. Our experiments show that our rescoring method reduces Word Error Rate (WER) from 16.7% to 12.8% in Bangla voice command recognition task when the relevant context is provided. The system does not lose any performance when irrelevant context is provided.
BibTeX
@inproceedings{icassp2020_banglavoicecomma,
title = {Bangla Voice Command Recognition in end-to-end System Using Topic Modeling based Contextual Rescoring},
author = {Nafis Sadeq and Shafayat Ahmed and Sudipta Saha Shubha and Md. Nahidul Islam and Muhammad Abdullah Adnan},
booktitle = {ICASSP 2020},
year = {2020}
}