Automatic Speech Recognition Experiments Research Articles

Speech recognition experiments with linear predication, bandpass filtering, and dynamic programming

Automatic speech recognition experiments are described in which several popular preprocessing and classification strategies are compared. Preprocessing is done either by linear predictive analysis or by bandpass filtering. The two approaches are shown to produce similar recognition scores. The classifier uses either linear time stretching or dynamic programming to achieve time alignment. It is shown that dynamic programming is of major importance for recognition of polysyllabic words. The speech is compressed into a quasi-phoneme character string or preserved uncompressed. Best results are obtained with uncompressed data, using nonlinear time registration for multisyllabic words.

Utterance classification confidence in automatic speech recognition

A variety of automatic speech recognition experiments have been executed that support a measure of confidence for utterance classification. The confidence measure tested was the ratio of the two best "Hamming distance" scores obtained in matching utterance templates with an unknown utterance. The results show that it is possible to reliably predict when the utterance classifier has made the correct decision.