Sarah Samson Juan

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—This paper explores speech recognition performance for Malay language with multi accents from speakers of different origins or ethnicities. Accented speech imposes accuracy problem in automatic speech recognition systems. This frequently occurs to non-native speakers of a language due to insufficiency of the non-natives data in the recognizers. In this(More)
This paper describes our experiments and results on using a local dominant language in Malaysia (Malay), to bootstrap automatic speech recognition (ASR) for a very under-resourced language: Iban (also spoken in Malaysia on the Borneo Island part). Resources in Iban for building a speech recognition were nonexistent. For this, we tried to take advantage of a(More)
This paper presents our strategies for developing an automatic speech recognition system for Iban, an under-resourced language. We faced several challenges such as no pronunciation dictionary and lack of training material for building acoustic models. To overcome these problems, we proposed approaches which exploit resources from a closely-related language(More)
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