Regression features for recognition of speech in quiet and in noise

It is proposed that the number of speech analysis frames used in calculating regression features should be controlled separately from the time length over which the features are calculated. Regression features are used to represent the first two time derivatives of the speech cepstrum in a speaker-independent, isolated-word recognition task. The recognition system is trained on normal (noise-free, non-Lombard) speech, but tested on normal, noisy, Lombard, or noisy-Lombard speech. It is shown that for recognition based on the combination of the first two regression features with the static cepstral coefficients, increasing the time length to more than 200 ms, using all of the frames in this time interval, resulted in the highest recognition rates for noisy-Lombard test speech