Development of Large Vocabulary Speech Recognition System with Keyword Search for Manipuri

Abstract

Research in Automatic Speech Recognition (ASR) has witnessed a steep improvement in the past decade (especially for English language) where the variety and amount of training data available is huge. In this work, we develop an ASR and Keyword Search (KWS) system for Manipuri, a low-resource Indian Language. Manipuri (also known as Meitei), is a Tibeto-Burman language spoken predominantly in Manipur (a northeastern state of India). We collect and transcribe telephonic read speech data of 90+ hours from 300+ speakers for the ASR task. Both state-of-the-art Gaussian Mixture-Hidden Markov Model (GMM-HMM) and Deep Neural Network-Hidden Markov Model (DNN-HMM) based architectures are developed as a baseline. Using the collected data, we achieve better performance using DNN-HMM systems, i.e., 13.57% WER for ASR and 7.64% EER for KWS. The KALDI speech recognition tool-kit is used for developing the systems. The Manipuri ASR system along with KWS is integrated as a visual interface for demonstration purpose. Future systems will be improved with more amount of training data and advanced forms of acoustic models and language models.

Speaker Recognition through Deep Learning Techniques

“…Table 8 [124][125][126][127][128][129] shows the few popular deep learning model implementations for speaker recognition with their performance.…”