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In this paper, the authors compare the performance of recognition of short sentences of speech using Hidden Markov Models (HMM) in Artificial Neural Networks (ANN) and Fuzzy Logic. The data sets used are sentences from The DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus. Currently, most speech recognition systems are based on Hidden Markov Models, a statistical framework that supports both acoustic and temporal modeling. Despite their state-of-the-art performance, HMMs make a number of suboptimal modeling assumptions that limit their potential effectiveness. Neural networks avoid many of these assumptions, while they can also learn complex functions, generalize effectively, tolerate noise, and support parallelism.
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