Mohammed Rokibul Alam Kotwal

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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(More)
Speaker-specific characteristics play an important role on the performance of Bangla (widely used as Bengali) automatic speech recognition (ASR). It is difficult to recognize speech affected by gender factors, especially when an ASR system contains only a single acoustic model. If there exists any suppression process that represses the decrease of(More)
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(More)
Hidden factor such as gender characteristic plays an important role on the performance of Bangla (widely used as Bengali) automatic speech recognition (ASR). If there is a suppression process that represses the decrease of differences in acoustic-likelihood among categories resulted from gender factors, a robust ASR system can be realized. In our previous(More)
—— This paper describes an evaluation of Inhibition/Enhancement (In/En) network for robust automatic speech recognition (ASR). In distinctive phonetic features (DPFs) based speech recognition using neural network, In/En network is needed to discriminate whether the DPFs dynamic patterns of trajectories are convex or concave. The network is used to achieve(More)
This This research constructs a phonetic feature (PF) table for all the phonemes pronounced in Bangla (widely known as Bengali) language where the whole study is divided into two parts. In the first part, a PF table is constructed, while the second part deals with Bangla automatic speech recognition (ASR) using PFs. For Bangla language, fifty three phonemes(More)
This paper describes the Bangla Document Categorization using Stochastic Gradient Descent (SGD) classifier. Here, document categorization is the task in which text documents are classified into one or more of predefined categories based on their contents. The proposed system can be divided into three steps: 1. feature extraction incorporating term frequency(More)
This paper presents a Bangla (widely used as Bengali) automatic speech recognition system (ASR) by suppressing gender effects. Gender characteristic plays an important role on the performance of ASR. If there is a suppression process that represses the decrease of differences in acoustic-likelihood among categories resulted from gender factors, a robust ASR(More)
Inherent features of the Bangla (widely used as Bengali) language like long and short vowels and many instances of allophones make it difficult to build a continuous speech recognizer for the language. Stress and accent vary in spoken Bangla language from region to region. But in formal read Bangla speech, stress and accents are ignored. There are three(More)