Discriminative training of auditory filters of different shapes for robust speech recognition

The bank-of-filters spectrum analysis model is commonly used in the extraction of acoustic features for automatic speech recognition. The most critical component in the analysis model is a bank of bandpass filters. We studied a data-driven approach to designing a bank of "optimal" filters of various shapes discriminatively so that the recognition error of a task is minimized. Three different shapes of varying degree of constraints were investigated: (1) parametric Gaussian filters; (2) non-parametric but constrained triangular-like filters; and (3) non-parametric and unconstrained free-formed filters. Filters were trained to derive the new robust auditory features proposed by the Bell Labs. In addition, both the filters (and thus the ensuing acoustic features) and the acoustic model parameters were discriminatively trained. The major result is that our proposed triangular-like filters perform at least as well as the free-formed filters and perform better than the Gaussian filters.