Sixth European Conference on Speech Communication and Technology
A major obstacle for the migration of automatic speech recog-nition into every-day life products is environmental robustness. Automatic speech recognition systems work reasonably well un-der clean (laboratory) conditions but degrade seriously under real world conditions (e.g. out-door, car). A lot of research work is devoted to increase the environmental robustness of automatic speech recognition systems. A common method is to use clean (office) data as a starting point and simulate the degraded environ-mental situation by additive artificial (e.g. Gaussian) or recorded noise from the real environment . We study the validity of such additive noise experiments with regard to a real noisy environment. With regard to a previously published work on database adaptation we also examine the possible benefit when using models trained in the simulated environment as a starting point for adap-tation (). We present experimental results on data recorded for task-dependent whole word and phoneme modeling in the car envi-ronment on data from the the MoTiV Car Speech Data Collection (CSDC) .
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Bibliographic reference. Bippus, Rolf / Fischer, Alexander / Stahl, Volker (1999): "Domain adaptation for robust automatic speech recognition in car environments", In EUROSPEECH'99, 1943-1946.