Syllabic Units Automatically Segmented Data for Continuous Speech Recognition

Madhav Singh Solanki

doi:10.55524/ijircst.2021.9.6.53

Abstract

We present novel approach for constant speech processing in which the detection and recognition tasks are separated A syllable is utilized as a measure both to detection and localization. A minimal phase’s group delay characteristic approach and an utterance isolated style are used to segment the speech signal at the boundaries of syllabic units. For two Indigenous languages, an HMM recognizing system has been created. Viterbi algorithm-based methods are suggested to solve recognition problems caused by shifts in segment borders and syllabic unit merging.

Keywords

Speech Recognition, Hidden Markov Models, Databases, Natural Languages, Delay Effects.

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Cites this article as

M. S. Solanki "Syllabic Units Automatically Segmented Data for Continuous Speech Recognition", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), vol. 9, no. 6, pp. 239-242, 2021. doi: https://doi.org/10.55524/ijircst.2021.9.6.53

Corresponding Author

Madhav Singh Solanki

SOEIT, Sanskriti University, Mathura, Uttar Pradesh, India (madhavsolanki.cse@sanskriti.edu.in)

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Syllabic Units Automatically Segmented Data for Continuous Speech Recognition

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