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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 here. In the experiments, mel-frequency cepstral...
This paper describes an evaluation of Inhibition/Enhancement (In/En) network for noise robust automatic speech recognition (ASR). In articulatory feature based speech recognition using neural network, the In/En network is needed to discriminate whether the articulatory features (AFs) dynamic patterns of trajectories are convex or concave. The network is used to achieve categorical AFs movement by...
This paper presents a Bangla phoneme recognition method for Automatic Speech Recognition (ASR). The method consists of three stages: i) a multilayer neural network (MLN), which converts acoustic features, mel frequency cepstral coefficients (MFCCs), into phoneme probabilities, ii) the phoneme probabilities obtained from the first stage and corresponding Δ and ΔΔ are inserted into another MLN to improve...
This paper describes an isolated word recognition method based on distinctive phonetic features (DPFs). The method comprises two multilayer neural networks (MLNs). The first MLN, MLNLF-DPF, maps local features (LFs) of an input speech signal into discrete DPFs and the second MLN, MLNDyn, restricts dynamics of outputted DPFs by the MLNLF-DPF. In the experiments on Tohokudai Isolated Spoken-Word Database...
This paper presents a speech recognition technique based on inhibition/enhancement (In/En) of articulatory features (AFs) by determining the dominant factor between inhibition and enhancement. The proposed method comprises three stages-a) Multilayer Neural Networks (MLNs), b) In/En Network and c)Gram-Schmidt (GS) Orthogonalization. At first stage, the MLNs detects AFs and then In/En network is used...
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