Open AccessAutomated Waves Files Splitting
Author(s) -
Mrinal Paliwal,
Pankaj Saraswat
Publication year - 2021
Language(s) - English
DOI - 10.55524/ijircst.2021.9.6.4
Subject(s) - syllabic verse , speech recognition , segmentation , computer science , syllable , feature (linguistics) , speech segmentation , natural language processing , sentence , hindi , repetition (rhetorical device) , matching (statistics) , artificial intelligence , pattern recognition (psychology) , linguistics , mathematics , philosophy , statistics
The ASS (Automatic Speech Segmentation) Technique is used in this article to segment spontaneous speech into syllable-like units. The segmentation of the acoustic signal into syllabic units is an essential step in the construction of a syllable-centric ASS system. The purpose of this article is to determine the smallest unit of speech that should be regarded when writing. Any voice recognition system may be trained. In a few Indian cities, technologies for continuous voice recognition have been created. Hindi and Tamil are examples of such languages. This article examines the statistical characteristics of Punjabi syllables and how they may be used to reduce the number of syllables in sentence. During voice recognition, the search area is expanded. We explain how to perform the majority of the segmentation in this article automatically. The frequency of syllables and the number of syllables in each word are shown. We suggest the following: For objective evaluation of stuttering disfluencies, an automated segmentation technique for syllable repetition in read speech was developed. It employs a novel method and consists of three stages: feature extraction, rule matching, and segmentation.