Recognition of Vernacular Language Speech for Discrete Words using Linear Predictive Coding Technique
Omesh Wadhwani1, Amit Kolhe2, Sanjay Dekate3
1Omesh Wadhwani, M-Tech Student, Department of Electronics and Telecommunication, Chhattisgarh State University (CSVTU), Rungta College of Engineering and Technology.
2Prof. Amit Kolhe, Department of Electronics and Telecommunication, Chhattisgarh State University (CSVTU), Rungta College of Engineering and Technology.
3Prof. Sanjay Dekate, Department of Electronics and Telecommunication, Chhattisgarh State University (CSVTU), Rungta College of Engineering and Technology.
Manuscript received on October 04, 2011. | Revised Manuscript received on October 21, 2011. | Manuscript published on November 05, 2011. | PP: 188-192 | Volume-1 Issue-5, November 2011. | Retrieval Number: E0187091511/2011©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Vernacular language spoken in various countries creates a limitation on software associated with speech recognition. This paper is an attempt to overcome such problem. The suggested work makes use of Linear Predictive Technique for better interpretation of spoken words. The rule based structure of fuzzy suits very well with closeness of vernacular speech recognition. In this paper we study the feasibility of Speech Recognition with fuzzy neural Networks for discrete Words Different Technical methods are used for speech recognition. Most of these methods are based on transfiguration of the speech signals for phonemes and syllables of the words. We use the expression “word Recognition” (because in our proposed method there is no need to catch the phonemes of words.). In our proposed method, LPC coefficients for discrete spoken words are used for compaction and learning the data and then the output is sent to a fuzzy system and an expert system for classifying the conclusion. The experimental results show good precisions. The recognition precision of our proposed method with fuzzy conclusion is around 90 percent.
Keywords: Automatic Speech Recognition, Feature Extraction, Linear Predictive Coding, LPC Coefficients, Vernacular, Words Recognition, Word error rate.