Acoustic backing-off as an implementation of missing feature theory

Abstract

In this paper, we discuss acoustic backing-off as a method to improve automatic speech recognition robustness. Acoustic backing-off aims to achieve the same objective as the marginalization approach of missing feature theory: the detrimental influence of outlier values is effectively removed from the local distance computation in the Viterbi algorithm. The proposed method is based on one of the principles of robust statistical pattern matching: during recognition the local distance function (LDF) is modeled using a mixture of the distribution observed during training and a distribution describing observations not previously seen. In order to assess the effectiveness of the new method, we used artificial distortions of the acoustic vectors in connected digit recognition over telephone lines. We found that acoustic backing-off is capable of restoring recognition performance almost to the level observed for the undisturbed features, even in cases where a conventional LDF completely fails. These results show that recognition robustness can be improved using a marginalization approach, where making the distinction between reliable and corrupted feature values is wired into the recognition process. In addition, the results show that application of acoustic backing-off is not limited to feature representations based on filter bank outputs. Finally, the results indicate that acoustic backing-off is much less effective when local distortions are smeared over all vector elements. Therefore, the acoustic pre-processing steps should be chosen with care, so that the dispersion of distortions over all acoustic vector elements as a result of within-vector feature transformations is minimal.