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Abstract

In automatic speech recognition, confusion easily arises between phonetically similar words (for example, the German words 'zwei' and 'drei') in the case of previous recognition systems. Confusion of words which differ only in a single phoneme (for example, German phonemes 'dem' and 'den') occurs particularly easily with these recognition systems. In order to solve this problem, a method for recognizing patterns in time-variant measurement signals is specified which permits an improved discrimination between such signals by reclassifying in pairs. This method combines the Viterbi decoding algorithm with the method of hidden Markov models, the discrimination-relevant features being examined separately in a second step after the main classification. In this case, different components of feature vectors are weighted differently, it being the case that by contrast with known approaches these weightings are performed in a theoretically based way. The method is suitable, inter alia, for improving speech-recognizing systems.

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First Claim