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This paper describes an effect of articulatory Δ and ΔΔ parameters on automatic speech recognition (ASR). Articulatory features (AFs) or distinctive phonetic features (DPFs)-based system shows its superiority in performances over acoustic features based ASR. These performances can be further improved by incorporating articulatory dynamic parameters into it. In this paper, we have proposed such a phoneme...
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 describes a Bangla phoneme recognition method for Automatic Speech Recognition (ASR). The method consists of two stages: i) a multilayer neural network (MLN), which converts acoustic features, mel frequency cepstral coefficients (MFCCs), into phoneme probabilities and ii) the phoneme probabilities obtained from the first stage and corresponding Δ and ΔΔ are inserted into another MLN to...
This paper presents a method for extracting distinctive phonetic features (DPFs) for automatic speech recognition (ASR). The method comprises three stages: i) a acoustic feature extractor, ii) a multilayer neural network (MLN) and iii) a hidden Markov model (HMM) based classifier. At first stage, acoustic features, local features (LFs), are extracted from input speech. On the other stage, MLN generates...
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