A Review on Various Approach of Speech Recognition Technique
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Abstract
The Speech is most prominent & primary mode of Communication among of human being. The communication among human computer interaction is called human computer interface. Speech has potential of being important mode of interaction with computer. This paper gives an overview of major technological perspective and appreciation of the fundamental progress of speech recognition and also gives overview technique developed in each stage of speech recognition. This paper helps in choosing the technique along with their relative merits & demerits. A comparative study of different technique is done asperstages.
After years of research and development the accuracy of automatic speech recognition remains one of the promising research challenges (eg. variation of the context, speakers, and environment). The design of Speech Recognition system requires careful attentions to the following issues: Definition of various types of speech classes, speech representation, feature extraction techniques, speech classifiers, and database and performance evaluation. The problems that are existing in ASR and the various techniques to solve these problems constructed by various research workers have been presented in a chronological order.
Real-time speech recognition is a challenging task due to the variability of speech signals and the need for fast and accurate processing. Support Vector Machines (SVMs) are a popular machine learning technique that has been used for speech recognition tasks. In this paper, we present a real-time speech recognition system using SVM. The system is based on a feature extraction process that uses Mel-Frequency Cepstral Coefficients (MFCCs) to represent speech signals. The extracted features are then used as input to the SVM classifier, which is trained to recognize different speech signals. The proposed system was implemented using the Python programming language and the Scikit-learn machine learning library. The performance of the system was evaluated using a dataset of spoken digits. The results showed that the proposed system achieved high recognition accuracy and real-time performance, making it suitable for practical applications.
Speech is a unique human characteristic used as a tool to communicate and express ideas. Automatic speech recognition (ASR) finds application in electronic devices that are too small to allow data entry via the commonly used input devices such as keyboards. Personal Digital Assistants (PDA) and cellular phones are such examples in which ASR plays an important role.
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