Ahmed, Basem H. A.
(2014)
A Framework For Automatic Code Switching Speech Recognition With Multilingual Acoustic And Pronunciation Models Adaptation.
PhD thesis, Perpustakaan Hamzah Sendut.
Abstract
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 codeswitching
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.
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