Abstract
Application specific voice interfaces in local languages will go a long way in reaching the benefits of technology to rural India. A continuous speech recognition system in Hindi tailored to aid teaching Geometry in Primary schools is the goal of the work. This paper presents the preliminary work done towards that end. We have used the Mel Frequency Cepstral Coefficients as speech feature parameters and Hidden Markov Modeling to model the acoustic features. Hidden Markov Modeling Tool Kit —3.4 was used both for feature extraction and model generation. The Julius recognizer which is language independent was used for decoding. A speaker independent system is implemented and results are presented.
Highlights
To make Information Technology (IT) relevant to rural India, voice access to a variety of computer based services is imperative
Hidden Markov Modeling Tool Kit −3.4 was used both for feature extraction and model generation
This paper presents the preliminary work done to demonstrate the relevance of a Hindi Continuous Speech Recognition System in primary education
Summary
To make Information Technology (IT) relevant to rural India, voice access to a variety of computer based services is imperative. Efforts are on to develop speech recognition systems in different Indian Languages. The hybrid HMM and Artificial Neural Network (ANN) framework is used in an effort to overcome the challenges posed by speech variability due to physiological differences, style variability due to co-articulation effects, varying accents, emotional states, context variability etc [20]. Another method to handle the problem of changes in the acoustic environment or speaker specific voice characteristics is by adapting the statistical models of a speech recognizer and speaker tracking.
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