A Framework For Automatic Code Switching Speech Recognition With Multilingual Acoustic And Pronunciation Models Adaptation

A. Ahmed, Basem H. (2014) A Framework For Automatic Code Switching Speech Recognition With Multilingual Acoustic And Pronunciation Models Adaptation. PhD thesis, Universiti Sains Malaysia.

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Recognition of code-switching speech is a challenging problem because of three issues. Code-switching is not a simple mixing of two languages, but each has its own phonological, lexical, and grammatical variations. Second, code-switching resources, such as speech and text corpora, are limited and difficult to collect. Therefore, creating code-switching speech recognition models may require a different strategy from that typically used for monolingual automatic speech recognition (ASR). Third, a segment of language switching in an utterance can be as short as a word or as long as an utterance itself. This variation may make language identification difficult. In this thesis, we propose a novel approach to achieve automatic recognition of code-switching speech. The proposed method consists of two phases, namely, ASR and rescoring. The framework uses parallel automatic speech recognizers for speech recognition. We also put forward the usage of an acoustic model adaptation approach known as hybrid approach of interpolation and merging to cross-adapt acoustic models of different languages to recognize code-switching speech better. In pronunciation modeling, we propose an approach to model the pronunciation of non-native accented speech for an ASR system. Our approach is tested on two code-switching corpora: Malay–English and Mandarin–English. The word error rate for Malay–English code-switching speech recognition reduced from 33.2% to 25.2% while that for Mandarin–English code-switching speech recognition reduced from 81.2% to 56.3% when our proposed approaches are applied. This result shows that the proposed approaches are promising to treat code-switching speech.

Item Type: Thesis (PhD)
Subjects: P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing
Divisions: Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis
Depositing User: HJ Hazwani Jamaluddin
Date Deposited: 26 Apr 2021 07:37
Last Modified: 26 Apr 2021 07:37
URI: http://eprints.usm.my/id/eprint/49021

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