Md. Fayzur Rahman

Improvement of speech enhancement techniques for robust speaker identification in noise

This paper presents an approach of speech enhancement techniques to improve the performance of the robust speaker identification under noisy environments. Start-end points detection, silence part removal, frame segmentation and windowing technique have been used to pre-process and wiener filter has been used to remove the silence parts from the speech utterances. To extract the features from the speech various speech parameterization techniques that is LPC, LPCC, RCC, MFCC, ΔMFCC and ΔΔMFCC have been simulated. Finally, to measure the performance of the proposed speech enhancement techniques, genetic algorithm has been used as a classifier for the noise robust automated speaker identification system and various experiments have performed on genetic algorithm to select the optimum parameters. According to the NOIZEOUS speech database, the highest identification rate of 70.31 [%] for text-dependent and of 61.26 [%] for text-independent speaker identification system have been achieved.

Microprocessor based temperature monitoring and control system using fuzzy logic controller

A closed loop control system incorporating fuzzy logic has been developed for a class of maintenances of temperature of a heating chamber. A unique fuzzy logic controller (FLC) structure with an efficient realization and a small rule base that can be easily implemented in existing industrial controllers was proposed. The performance of this system has been measured in various environmental conditions such as compensating for thermo mass changes in the system, dealing with unknown and variable delays, operating at different temperature setpoints without retuning, etc. It is achieved by implementing, in FLC, a classical control strategy and an adaptation mechanism to compensate for the dynamic changes in the system. This paper deals with the detail issue for the improvement of microprocessor based design and implementations of the heating chamber temperature maintenances on practical demand and finally focuses the performance of this system to other environmental conditions in various systems.

Noise Robust Speaker Identification using PCA based Genetic Algorithm

This paper emphasizes text dependent speaker identification system on Principal Component Analysis based Genetic Algorithm which deals with detecting a particular speaker from a known population under noisy environment. At first, the system prompts the user to get speech utterance. Noises are eliminated from the speech utterances by using wiener filtering technique. To extract the features from the speech, various types of feature extraction techniques such as RCC, LPCC, MFCC, MFCC and MFCC have been used. Principal Component Analysis has been used to reduce the dimensionality of the speech feature vector. To classify the speech utterances, Genetic Algorithm has been used. NOIZEOUS speech database has been used to measure the performance of this system under the condition of various SNRs. Experimental results show the superiority of the proposed close-set text dependent speaker identification system which can be used for security and access control purposes. General Terms Pattern Recognition, Soft Computing, Human Computer Interaction.

Codebook design method for speaker identification based on Genetic Algorithm

In this paper, a novel method of designing a codebook for speaker identification purpose utilizing genetic algorithm has been proposed. Speech features have been extracted using standard speech parameterization method such as LPC, LPCC, RCC, MFCC, DeltaMFCC and DeltaDeltaMFCC. For each of these techniques, the performance of the proposed system has been compared. In this codebook design method, genetic algorithm has the capability of getting global optimal result and hence improves the quality of the codebook. Experimental results show the superiority (i.e. 92% accuracy has been achieved) of this proposed codebook design method.