Gazi Md. Moshfiqul Islam

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In this paper, we compare among performance of different acoustic features for Bangla Automatic Speech Recognition (ASR). Most of the Bangla ASR system uses a small number of speakers, but 40 speakers selected from a wide area of Bangladesh, where Bangla is used as a native language, are involved here. In the experiments, mel-frequency cepstral coefficients(More)
This paper presents a method that describes the effect of articulatory velocity coefficient (Δ) on neural network based speech recognition. The method consists of three stages: a) two multilayer neural networks (MLNs), where second MLN takes Δ articulatory parameters as input b) Inhibition/Enhancement (In/En) network and c) Gram-Schmidt(More)
In this paper, we have prepared a medium size Bangla speech corpus and compare performances of different acoustic features for Bangla word recognition. Most of the Bangla automatic speech recognition (ASR) system uses a small number of speakers, but 40 speakers selected from a wide area of Bangladesh, where Bangla is used as a native language, are involved(More)
This paper presents a method for automatic phoneme recognition for Japanese language using tandem MLNs. The method comprises three stages: (i) multilayer neural network (MLN) that converts acoustic features into distinctive phonetic features DPFs, (ii) MLN that combines DPFs and acoustic features as input and generates a 45 dimensional DPF vector with less(More)
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