Several acoustic representations have been compared in speaker-dependent and independent connected and isolated-word recognition tests with undegraded speech and with speech degraded by adding white noise and by applying a 6-dB/octave spectral tilt. The representations comprised the output of an auditory model, cepstrum coefficients derived from an FFT-based mel-scale filter bank with various weighting schemes applied to the coefficients, cepstrum coefficients augmented with measures of their rates of change with time, and sets of linear discriminant functions derived from the filter-bank output and called IMELDA. The model outperformed the cepstrum representations except in noise-free connected-word tests, where it had a high insertion rate. The best cepstrum weighting scheme was derived from within-class variances. Its behavior may explain the empirical adjustments found necessary with other schemes. IMELDA outperformed all other representations in all conditions and is computationally simple.
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