Syllable based Hindi speech recognition. Issue 6 (17th August 2020)

• Record Type: Journal Article
• Authors: Bhatt, Shobha; Jain, Anurag; Dev, Amita
• Is Part Of: Journal of information & optimization sciences. Volume 41:Issue 6(2020)
• Page Start: 1333
• Page End: 1351
• Publication Date: 2020-08-17
• Subjects: 68T10; Speech recognition -- Syllable -- Acoustic model -- HMM -- PLP -- Hindi speech; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2020.1809091
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: In this paper, one of the acoustic units of speech, the syllable, is used for the development of a continuous Hindi speech recognition system. The syllable is a larger acoustic unit that overcomes the contextual effects and requires fewer training samples in comparison to triphone based and word-based models. Other acoustic units such as phoneme-based suffer from contextual influences, and context-dependent triphones suffer due to the non-availability of triphone patterns with a large memory storage for numerous models. Earlier research works related to Hindi speech recognition were performed using the word, phoneme, and context-dependent models. The authors proposed a syllable based Hindi speech recognition system in this study due to different advantages of syllable units such as longer acoustic units, fast decoding, reducing contextual effects, and reduction of irregularities due to phonemes. The continuous Hindi speech recognition system was developed utilizing syllable based acoustic units. Hindi is widely spoken in India and other parts of the world also. The experiments are performed on Continuous Hindi speech by using a widely known Hidden Markov Model (HMM) with perceptual linear predictive coefficients(PLPs). The research outcomes reveal that by using syllables, the performance of the system was increased by 27% than phoneme and 20% than triphones. Research findings indicate that by selecting an appropriate acoustic unit for Hindi, the performance of the speech recognition system may be improved. Further, the study also provides useful insights to develop a syllable based pronunciation dictionary that may be used in speech recognition, speaker identification, and text to speech conversion systems.