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Integration of phoneme pattern recognition with hidden Markov models to enhance performance of low level speech recognition
journal contribution
posted on 2023-06-09, 01:16 authored by Mohammed Al-Darkazali, Rupert YoungRupert Young, Chris ChatwinChris Chatwin, Phil BirchPhil BirchThe hidden Markov model (HMM) is commonly employed in automatic speech recognition (ASR). The HMM has been shown to have a good performance in many applications, although it has some well-known limitations in modelling speech. Therefore, the standard HMM topology has been modified in a variety of ways to reduce errors, including factorization of the HMM into multiple-streams. However, the gap between the theoretical representation of speech and its acoustic representation can be further reduced. This paper describes a new method of correcting the HMM based on matching two dimensional templates of word time-frequency patterns to assist in low level speech recognition. This is shown to be a promising method to enhance speech recognition performance.
Funding
Speech Recognition; 160516iisp; Iraqi Ministry of Education
History
Publication status
- Published
File Version
- Accepted version
Journal
Asian Journal of PhysicsISSN
0971-3093Publisher
Anita PublicationsIssue
6Volume
25Department affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2016-05-16First Compliant Deposit (FCD) Date
2016-05-16Usage metrics
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