Author(s): Utpal Bhattacharjee
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Vol.2 - Issue 1 (January - 2013)
In this paper two popular feature extraction techniques Linear Predictive Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) have been investigated and their performances have been evaluated for the recognition of Assamese phonemes. A multilayer perceptron based baseline phoneme recognizer has been built and all the experiments have been carried out using that recognizer. In the present study, attempt has been made to evaluate the performance of the speech recognition system with different feature set in quiet environmental condition as well as at different level of noise. It has been observed that at noise free operating environment when same speaker is used for training and testing the system, the system given 100% recognition accuracy for the recognition of Assamese phones for both the feature set. However, the performance of the system degrades considerably with increase in environmental noise level.It has been observed that the performance of LPCC based system degrades more rapidly compare to MFCC based system under environmental noise condition whereas under speaker variability conditions, LPCC shows relative robustness compare to MFCC though the performance of both the systems degrades considerably.
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