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Recognition of human speech has long been a hot topic among artificial intelligence and signal processing researches. Most of current policies for this subject are based on extraction of precise features of voice signal and trying to make most out of them by heavy computations. But this focus on signal details has resulted in too much sensitivity to noise and as a result, the necessity of complex noise detection and removal algorithms, which composes a trade-off between fast or noise robust recognition. This paper presents a novel approach to speech recognition using fuzzy modeling and decision making that ignores noise instead of its detection and removal. To do so, the speech spectrogram is converted into a fuzzy linguistic description and this description is used instead of precise acoustic features. During the training period, a genetic algorithm finds appropriate definitions for phonemes, and when these definitions are ready, a simple novel operator consisting of low cost functions such as Max, Min, and Average makes the recognition. The approach is tested on a standard speech database and is compared with Hidden Markov model recognition system with MFCC features as a widely used speech recognition approach. |
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