Effects of pre-processing filters on a wavelet packet-based algorithm to identify speech transients

Speech transients have been shown to be important cues for identifying speech sounds, and amplification of transients can improve the intelligibility of speech in noise. We have developed a time-frequency approach to identify transients that uses a pre-processing filter, but optimal filter parameters are difficult to determine due to the large number of possibilities. This paper describes the use of the Articulation Index (AI) to evaluate the effects of different high-pass filters on our algorithm. The best filter was found to depend on signal-to-noise ratio (SNR). AI results should be interpreted with caution, but they appear to provide a reasonable approach to selecting a pre-processing filter.