Domain adaptation for robust automatic speech recognition in car environments

A major obstacle for the migration of automatic speech recognition into every-day life products is environmental robustness. Automatic speech recognition systems work reasonably well under 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 environmental situation by additive artificial (e.g. Gaussian) or recorded noise from the real environment [1]. 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 adaptation ([2]). We present experimental results on data recorded for task-dependent whole word and phoneme modeling in the car environment on data from the the MoTiV Car Speech Data Collection (CSDC) [3].