Speaker dependent Hindi speech recognition using optimized classifiers

Abstract

Speech is the most natural way of human communication. The variability in speech signal makes automatic speech recognition (ASR) a challenging task. The variability in speech depends on environmental conditions, speaker attributes such as emotion, age, gender and many other factors. Speech recognition is one of the most promising fields of current research due to its versatile applications. Many international organizations as well as research groups are working in this field. The performance of ASR systems has been improved since last decade and now it has been used for many practical applications.Even now, there are lot of possibilities to improve their recognition rate, speed, vocabulary and usefulness for the end-users.Another issue with ASR system is that it has not been developed for a good number of languages due to limited data availability and proper statistical framework of acoustic and language models. Hindi is the national language of India and people in several other Asian countries can easily understand and speak it. So there is a need to develop an efficient ASR system for Hindi language. The main objective of ASR is to build a system that can map the acoustic signal into a string of words. An ASR system has two main elements,i.e., front-end processor and back-end classifier. The front-end processor is used to extract speech features or parameters. These features are processed by a back-end classifier, for speech recognition. The Artificial neural network (ANN), support vector machine (SVM) and hidden Markov model (HMM) classifiers have been widely used for speech recognition. An ANN is inspired by biological neural network and it processes input information using an interconnected group of artificial neurons and a connectionist approach to computation. Training of an ANN is a tedious task, because search space is high dimensional and multimodal. An ANN training needs efficient optimization techniques to search a set of weights and biases that minimizes the error.