Multilingual and Multimode Phone Recognition System for Indian Languages

TL;DR

This paper presents a flexible multilingual and multimode phone recognition system for Indian languages, combining speech mode classification and phonetic recognition to improve accuracy across different speech modes.

Contribution

It introduces a novel framework that integrates speech mode classification with mode-specific phonetic recognition using deep neural networks for Indian languages.

Findings

Proposed system outperforms mode-dependent systems in recognition accuracy.

Speech mode classification achieves high accuracy using vocal tract and excitation features.

Combining SMC with MPRS improves robustness across different speech modes.

Abstract

The aim of this paper is to develop a flexible framework capable of automatically recognizing phonetic units present in a speech utterance of any language spoken in any mode. In this study, we considered two modes of speech: conversation, and read modes in four Indian languages, namely, Telugu, Kannada, Odia, and Bengali. The proposed approach consists of two stages: (1) Automatic speech mode classification (SMC) and (2) Automatic phonetic recognition using mode-specific multilingual phone recognition system (MPRS). In this work, the vocal tract and excitation source features are considered for speech mode classification (SMC) task. SMC systems are developed using multilayer perceptron (MLP). Further, vocal tract, excitation source, and tandem features are used to build the deep neural network (DNN)-based MPRSs. The performance of the proposed approach is compared with mode-dependent MPRSs. Experimental results show that the proposed approach which combines both SMC and MPRS into a single system outperforms the baseline mode-dependent MPRSs.