Speaker Independent Isolated Tamil Words for Speech Recognition using MFCC, IPS and HMM
نویسندگان
چکیده
The process of converting an acoustic waveform into the text resembling the information, conveyed by the speaker is termed as speech recognition. Nowadays, normally Hidden Markov Model (HMM) based speech recognizer with Mel Frequency Cepstral Coefficient (MFCC) feature extraction is used. The proposed speech feature vector is generated by projecting an observed vector onto an Integrated Phoneme Subspace (IPS) based on Independent Component Analysis (ICA) or Principal Component Analysis (PCA). The performance of the new feature has to be evaluated for Isolated Tamil Word Speech Recognition. The proposed method is expected to provide higher recognition accuracy than conventional method in clean environment.
منابع مشابه
Feature Extraction and Dimensionality Reduction using IPS for Isolated Tamil Words Speech Recognizer
Automatic Speech Recognition (ASR), is the process of converting a speech waveform into the text quite similar to the information being communicated by the speaker. This paper aims to construct a speech recognition system for Tamil language. Mel Frequency Cepstral Coefficients (MFCC) is a commonly used feature extraction technique for speech recognition which is computed by applying DCT to the ...
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