Linear predictive residual analysis compared to bandpass filtering for automatic speech recognition
George M. White · The Journal of the Acoustical Society of America · 1975
It has been recently proposed by Itakura [F. Itakura, “Minimum Predictive Residual Principal Applied to Speech Recognition,” IEEE Symp. Speech Recog. CMU (1974)] that the linear predictive residual can be used as a measure of speech waveform similarity. To measure the similarity between two waveforms, Itakura proposed to construct a linear predictive filter for one waveform and measure the residual (predictive error) for the other waveform. Itakura used this technique to achieve some remarkably good speech recognition scores. We constructed a speech recognition system using both bandpass filtering and linear prediction in order to compare the two techniques. The classifier used dynamic programming. A 36-word vocabulary was used consisting of the alphabet plus digits spoken five times by the same speaker. A single word list was used for training and the other four were used for testing. Speech input was through a noise cancelling microphone. For the digital linear predictive, inverse filtering, analysis, speech was low pass filtered at about 5 kHz and digitized at 10 kHz. For the bandpass filtering experiment, 21 filter channels each 1/3 octave wide wore used covering the audio spectrum from about 100 Hz to 10 kHz. The recognition scores in both cases were 98% correct showing that the linear predictive residual technique is essentially equivalent to bandpass filtering as a means of measuring speech waveform similarity.