Artificial Neural Networks in Recognition of Phonetic Features in Speech

A set of recurrent artificial neural networks are used for speech recognition. By representing speech waves as vectors of mel-cepstral coefficients and energy, we can train a neural network to classify the values of phonetic features in a given sentence of speech. The effectiveness of the training depends both on the range of possible values for a given feature classification, on the distribution of the values in training samples, and possibly on how well a given feature is represented by cepstral analysis. The nets are able to identify both specific feature values and broader feature value groupings in speech. 1 Introduction A popular method for doing speech recognition is to use hidden Markov models (HMM's). HMM's approach speech recognition is to determine the probability of the speech being a certain utterance. Meanwhile, neural networks take a non-probabilistic approach to problem solving. They can be used to learn the patterns in the input that give the desired output and to gen..