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Abstract

An automated speech recognition system converts a speech signal into a compact, coded representation that correlates to a speech phoneme set. A number of different neural network pattern matching schemes may be used to perform the necessary speech coding. An integrated user interface guides a user unfamiliar with the details of speech recognition or neural networks to quickly develop and test a neural network for phoneme recognition. To train the neural network, digitized voice data containing known phonemes that the user wants the neural network to ultimately recognize are processed by the integrated user interface. The digitized speech is segmented into phonemes with each segment being labelled with a corresponding phoneme code. Based on a user selected transformation method and transformation parameters, each segment is transformed into a series of multiple dimension vectors representative of the speech characteristics of that segment. These vectors are iteratively presented to a neural network to train/adapt that neural network to consistently distinguish and recognize these vectors and assign an appropriate phoneme code to each vector. Simultaneous display of the digitized speech, segments, vector sets, and a representation of the trained neural network assist the user in visually confirming the acceptability of the phoneme training set. A user may also selectively audibly confirm the acceptability of the digitization scheme, the segments, and the transform vectors so that satisfactory training data are presented to the neural network. If the user finds a particular step or parameter produces an unacceptable result, the user may modify one or more of the parameters and verify whether the modification effected an improvement in performance. The trained neural network is also automatically tested by presenting a test speech signal to the integrated user interface and observing both audibly and visually automatic segmentation of the speech, transformation into multidimensional vectors, and the resulting neural network assigned phoneme codes. A method of decoding such phoneme codes using the neural network is also disclosed.

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