Evaluation of speech enhancement based on pre-image iterations using automatic speech recognition

Recently, we developed pre-image iteration methods for single-channel speech enhancement. We used objective quality measures for evaluation. In this paper, we evaluate the de-noising capabilities of pre-image iterations using an automatic speech recognizer trained on clean speech data.

In particular, we provide the word recognition accuracy of the de-noised utterances using white and car noise at 0, 5, 10, and 15 dB signal-to-noise ratio (SNR). Empirical results show that the utterances processed by pre-image iterations achieve a consistently better word recognition accuracy for both noise types and all SNR levels compared to the noisy data and the utterances processed by the generalized subspace speech enhancement method.

Share This Post