Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling
TABLE I: WERs of the five acoustic models on WSJ. bab and caf denote ADTbabble and ADTcafeteria1, respectively. w/o refers to noisy evaluation data without speech enhancement (i.e. unenhanced speech), and w/ evaluation data with enhancement.
['SNR', 'clean bab', 'clean bab', 'clean caf', 'clean caf', 'noise-dependent bab', 'noise-dependent bab', 'noise-dependent caf', 'noise-dependent caf', 'noise-mismatched bab', 'noise-mismatched bab', 'noise-mismatched caf', 'noise-mismatched caf', 'noise-independent bab', 'noise-independent bab', 'noise-independent caf', 'noise-independent caf', 'distortion-independent bab', 'distortion-independent bab', 'distortion-independent caf', 'distortion-independent caf']
We use ADTbabble and ADTcafeteria1 as the noises for evaluation. The clean acoustic model clearly benefits from speech enhancement, as enhanced speech has a higher SNR than the corresponding noisy speech. The noise-mismatched acoustic models are able to benefit from speech enhancement in our experiments on WSJ. Such an ability, however, is influenced by the type of noise used for testing. We will discuss this more after presenting the results on CHiME-2. This indicates that acoustic models trained on one noise cannot generalize to untrained noises. This performance degradation caused by noise mismatch supports our analysis on the distortion problem.