Method and apparatus for time series signal recognition with signal

Abstract

A time series signal recognition capable of obtaining a high recognition rate even for the speech data with low S/N ratio in noisy environments. The time series signals are recognized by extracting a plurality of candidate feature vectors characterizing an individual time series signal, without fixing a boundary for the individual time series signal. Similarity values are calculated for each of the plurality of candidate feature vectors and the reference patterns stored in the recognition dictionary, from which one reference pattern for which the similarity value is greater than a prescribed threshold value is selected as a recognition result. New reference patterns to be stored in the recognition dictionary are learned by acquiring actual background noise of the apparatus, and mixing prescribed noiseless signal patterns with the acquired background noise to form signal patterns for learning. The signal patterns for learning are recognized by extracting features vectors for learning from the signal patterns for learning, and the new reference patterns are obtained from the extracted feature vectors for learning. The learning process is iterated at different noise levels, so as to optimize the determination of the word boundary. The background noise may be constantly acquired, and learning can be carried out using the noise data acquired immediately before the speech data is input.