A Hindi speech recognition system for connected words using HTK. Issue 1 (1st January 2012)

• Record Type: Journal Article
• Authors: Kumar, Kuldeep; Aggarwal, R.K.; Jain, Ankita
• Is Part Of: International journal of computational systems engineering. Volume 1:Issue 1(2012)
• Page Start: 25
• Page End: 32
• Publication Date: 2012-01-01
• Subjects: hidden Markov models -- HMMs -- speech recognition system -- automatic speech recognition -- ASR -- hidden Markov model toolkit -- HTK -- Hindi -- connected words -- Mel frequency cepstral coefficient -- MFCC; Computer science -- Periodicals; Electronic data processing -- Periodicals; System analysis -- Periodicals; 003.3
• Languages: English
• ISSNs: 2046-3391

Abstract

A speech recognition system converts the speech-sound into the corresponding text. The uttered speech is first understood by the machine and then the corresponding text is displayed. This paper aims to build a connected-words speech recognition system for Hindi language. The system has been developed using hidden Markov model toolkit (HTK) that uses hidden Markov models (HMMs) for recognition. The system has been trained to recognise any sequence of words selected from the vocabulary of 102 words. Initially, Mel frequency cepstral coefficients (MFCCs) have been used to extract the features from the speech-files. Then, the system has been trained to estimate the HMM parameters using word level acoustic models. The training data has been collected from 12 speakers including both males and females. The test-data used for evaluating the system-performance has been collected from the five speakers. The experiments have also been performed on the system. The experimental results show that the presented system provides the overall word-accuracy of 87.01%, word-error rate of 12.99%, and word-correction rate of 90.93% respectively. The work has been evaluated by performing the comparative analysis with the existing similar works and the betterment has been reported.