![]() ![]() In this regard, the current work proposes a system, where the dysarthric speech is first recognized by an HMM-based speech recognition system. ![]() In order to enable a dysarthric speaker to communicate more efficiently with others, a text-to-speech synthesis system that generates speech in his voice, but without the errors he makes would be desirable. It has been observed that the Random Forest Classifier gives superior performance.ĭysarthria is a manifestation of an inability to control and coordinate on one or more articulatory subsystems, which results in poorly articulated, slurred, and unintelligible speech. Different classification algorithms such as the regression algorithm and Fuzzy Decision Tree classification algorithm have been used and their results have been compared. The proposed model can be used in various speech to text applications as the algorithm detects lisp words accurately and correct them in real time. MRF Algorithm has been proposed (MFCC-RF) that can be applied on real-time embedded systems that can help people with speech disability. The coefficients are extracted and they form the basis of classification into lisp or non-lisp words. The features extracted are the Mel Frequency Cepstral Coefficients (MFCC). The objective of this paper is to delineate a compound speech processing algorithm that can segment and recognize the individual words present in the speech using feature extraction, and identify any lisp words using Random Forest Classifier and correct it. Lisp is a functional speech impediment that results in difficulty to produce specific speech sounds and specific words. ![]()
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