INTERSPEECH 2006 - ICSLP
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 recognition 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.
Bibliographic reference. Boril, Hynek / Fousek, Petr / Pollák, Petr (2006): "Data-driven design of front-end filter bank for Lombard speech recognition", In INTERSPEECH-2006, paper 1803-Mon2BuP.10.