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)
This paper discusses the dominancy of local features (LFs), as input to the multilayer neural network (MLN), extracted from a Bangla input speech over mel frequency cepstral coefficients (MFCCs). Here, LF-based method comprises three stages: (i) LF extraction from input speech, (ii) phoneme probabilities extraction using MLN from LF and (iii) the hidden(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 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)
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 presents automatic speech recognition (ASR) for Bangla (widely used as Bengali) by suppressing the speaker gender types based on local features extracted from an input speech. Speaker-specific characteristics play an important role on the performance of Bangla automatic speech recognition (ASR). Gender factor shows adverse effect in the(More)
This paper illustrates the design and implementation of Bangla (widely used as Bengali) Text to Speech (TTS) system from the very raw level without using any third party speech synthesis tool. For constructing the system we have considered two directions, where one is based on phoneme and another one is on syllable. In this study, our proposed system(More)