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This paper presents 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 parameters calculated by linear regression...
In this paper auditory like features MLPC and MFCC have been used as front-end and their performance has been evaluated on Aurora-2 database for Hidden Markov Model (HMM) based noisy speech recognition. The clean data set is used for training and test set A is used to examine the performance. It has been found that almost the same recognition performance has been obtained both for MLPC and MFCC and...
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...
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