Cross-lingual acoustic modeling using Subspace Gaussian Mixture Model for low-resource languages of Indian origin is investigated. Building acoustic model for a low-resource language with limited vocabulary by leveraging resources from another language with comparatively larger resources was focused upon. Experiments were done on Bengali and Tamil corpus from MANDI database, with Tamil having greater resources than Bengali. We observed that the word accuracy of cross-lingual acoustic model of Bengali was approximately 2.5% above it's CDHMM model and gave equivalent performance as it's monolingual SGMM model. © 2014 IEEE.

Srinivasan Umesh

Department of Electrical Engineering

Architectural acoustics

Object Recognition

Acoustic model

Bengalis

BUILDING ACOUSTICS

Cross-lingual

Indian languages

SGMM

SUBSPACE GAUSSIAN MIXTURE MODELS

WORD ACCURACIES

Communication channels (information theory)

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Cross-lingual acoustic modeling for Indian languages based on Subspace Gaussian Mixture Models

2014 20th National Conference on Communications, NCC 2014

The interest in this paper is in efficient configuration of automatic speech recognition (ASR) systems for use by under-served speaker populations. A task domain involving Indian farmers accessing information on agricultural commodities through a spoken dialog system in multiple languages is presented. To facilitate the development of ASR system for this domain, a speech corpus was collected in rural areas from speakers of four languages over wireless cellular channels. This paper investigates the problem of ASR acoustic modelling for this task domain. Continuous density hidden Markov model (CDHMM) and subspace Gaussian mixture model (SGMM) [1] based techniques are used to train acoustic models in four languages: Assamese, Bengali, Hindi and Marathi. Issues relating to limited linguistic resources with their impact on ASR word accuracy for these languages are addressed. © 2012 IEEE.

2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012

Subspace based for Indian languages

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Generating exact lattices in the WFST framework

2011 IEEE Workshop on Automatic Speech Recognition & Understanding

Regularized subspace Gaussian mixture models for cross-lingual speech recognition

Computer Speech & Language

The subspace Gaussian mixture model—A structured model for speech recognition

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

Multilingual acoustic modeling for speech recognition based on subspace Gaussian Mixture Models

Journal	Data powered by Typeset2014 20th National Conference on Communications, NCC 2014
Publisher	Data powered by TypesetIEEE Computer Society
Open Access	No