Multilingual and Multimode Phone Recognition System for Indian Languages
This work addresses the challenge of accurate phone recognition in diverse Indian languages and speech modes, which is incremental as it builds upon existing methods by integrating mode classification.
The paper tackled the problem of recognizing phonetic units in speech across multiple Indian languages and speech modes by developing a two-stage system combining automatic speech mode classification and a mode-specific multilingual phone recognition system, achieving performance superior to baseline mode-dependent systems.
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.