Data-Driven Design of Front-End Filter Ban

Adverse environments not only corrupt speech signal by additive and convolutional noises, which can be successfully addressed by a number of suppression algorithms, but also affect the way how speech is produced. Speech production variations introduced by a speaker in reaction to a noisy background (Lombard effect) may result in a severe degradation of automatic speech recognition. This paper contributes to the solution of Lombard speech recog-nition issue by providing a robust filter bank for use in front-ends. It is shown that cepstral features derived from the proposed filter bank significantly outperform conventional cepstral features. Index Terms: robust speech recognition, Lombard effect, feature extraction, filter bank, data-driven desig