Mohammad Nurul Huda

Automatic speech recognition for Bangla digits

In this paper, we introduce a system for Bangla digit automatic speech recognition (ASR). Though Bangla is one of the largely spoken languages in the world, only a few works on Bangla ASR can be found in the literature, especially on Bangladeshi accented Bangla. In this work, the corpus is collected from natives in Bangladesh. Mel-frequency cepstral coefficients (MFCCs) based features and hidden Markov model (HMM) based classifiers are used for recognition. Experimental results show comparatively high recognition performance (more than 95%) for first six digits (0 – 5) and low performance (less than 90%) for the next four digits (6 – 9). We notice two confused pairs of digits: one with (6) and (9), and the other with (7) and (8), in the experiments. We also find that different dialects in Bangladesh have a greater role on this confusion.

Environment Recognition Using Selected MPEG-7 Audio Features and Mel-Frequency Cepstral Coefficients

In this paper, we propose a system for environment recognition using selected MPEG-7 audio low level descriptors together with conventional mel-frequency cepstral coefficients (MFCC). The MPEG-7 descriptors are first ranked based on Fisher’s discriminant ratio. Then principal component analysis is applied on top ranked 30 MPEG-7 descriptors to obtain 13 features. These 13 features are appended with MFCC features to complete the feature set of the proposed system. Gaussian mixture models (GMMs) are used as classifier. The system is evaluated using ten different environment sounds. The experimental results show a significant improvement in recognition performance of the proposed system over MFCC or full MPEG-7 descriptor based systems. For example, the best performance is achieved in Restaurant environment where MFCC, full MPEG-7, and the proposed method give 90%, 94%, and 96% accuracy, respectively.

Bangla text document categorization using Stochastic Gradient Descent (SGD) classifier

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 (TF) and inverse document frequency (IDF), 2. classifier design using the Stochastic Gradient Descent (SGD) algorithm by learning the distinct features, and 3. performance measure using F1-score. In the experiments on BDNews24 documents, it is observed that our proposed method provides higher accuracy in comparison with the methods based on Support Vector Machine (SVM) and Naive Bayesian (NB) classifier.

Gender independent Bangla automatic speech recognition

Speaker-specific characteristics play an important role on the performance of Bangla (widely used as Bengali) automatic speech recognition (ASR). Gender factor shows adverse effect in the classifier while recognizing a speech by an opposite gender, such as, training a classifier by male but testing is done by female or vice-versa. To obtain a robust ASR system in practice it is necessary to invent a system that incorporates gender independent effect for particular gender. In this paper, we have proposed a Gender-Independent technique for ASR that focused on a gender factor. The proposed method trains the classifier with the both types of gender, male and female, and evaluates the classifier for the male and female. For the experiments, we have designed a medium size Bangla (widely known as Bengali) speech corpus for both the male and female. The proposed system has showed a significant improvement of word correct rates, word accuracies and sentence correct rates in comparison with the method that suffers from gender effects. Moreover, it requires a fewer mixture component in hidden Markov model (HMMs) and hence, computation time.

Bangla phoneme recognition for ASR using multilayer neural network

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 (LR) are inserted into a hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings. From the experiments on Bangla speech corpus prepared by us, it is observed that the proposed method provides higher phoneme recognition performance than the existing method. Moreover, it requires a fewer mixture components in the HMMs.

Local Feature or Mel Frequency Cepstral Coefficients - Which One Is Better for MLN-Based Bangla Speech Recognition?

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 Markov model (HMM) based classifier to obtain more accurate phoneme strings. In the experiments on Bangla speech corpus prepared by us, it is observed that the LFbased automatic speech recognition (ASR) system provides higher phoneme correct rate than the MFCC-based system. Moreover, the proposed system requires fewer mixture components in the HMMs.

Structure of Dictionary Entries of Bangla morphemes for morphological rule generation for Universal Networking Language

Dictionary plays a crucial role in any machine translation (MT) system. The Universal Networking Language (UNL) is an artificial language developed for conveying linguistic expressions in order to represent websites information into a standard form. In order to integrate Bangla into this platform it is necessary to develop both a dictionary and a grammar. This paper focuses on the development of a Structure of Dictionary Entries and Analysis of Grammatical Attributes of Bangla words such as Bangla Roots, Krit Prottoy (primary suffix) and Kria Bivokti (verb suffix). The goal is to make possible Bangla sentence encoversion to UNL and vice-versa. The theoretical analysis of our model proves that the proposed work is successfully able to prepare Universal words for Bangla roots, Krit Prottoy and Kria Bivokti along with their grammatical attributes for UNL.

Gender Effects Suppression in Bangla ASR by Designing Multiple HMM-Based Classifiers

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 differences in acoustic-likelihood among categories resulted from gender factors, a robust ASR system can be realized. In this paper, we have proposed a technique of gender effects suppression that composed of two hidden Markov model (HMM)-based classifiers and that focused on a gender factor. In an experiment on Bangla speech database prepared by us, the proposed system has provided a significant improvement of word correct rate, word accuracy and sentence correct rate in comparison with the method that incorporates only a single HMM-based classifier for both male and female speakers.

Bangla ASR design by suppressing gender factor with gender-independent and gender-based HMM classifiers

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 paper, we proposed a technique of gender effects suppression that composed of two hidden Markov model (HMM)-based classifiers that focused on a gender factor. In the proposed study, we have designed a new ASR for Bangla by suppressing the gender effects, which embeds three HMM-based classifiers for corresponding male, female and geneder-independent (GI) characteristics. In an experiment on Bangla speech database prepared by us, the proposed system that incorporates GI-classifier has achieved a significant improvement of word correct rate, word accuracy and sentence correct rate in comparison with our previous method that did not incorporate GI-classifier.

Survey on matrix multiplication algorithms

This paper focuses on the time analysis of different matrix multiplication algorithms. In the paper, the methods invented by Karatsuba and Strassen are analyzed and implemented; theoretical and real time is calculated. Afterwards the Karatsuba and the Strassen algorithms are merged, and combining these two algorithms a new approach is designed, which can be considered as a method for reducing the time complexity.