Multi-resolution soft features for channel-robust distributed speech recognition
Valentin Ion, Reinhold Haeb‐Umbach · 2007
In this paper we introduce soft features of variable resolution for robust distributed speech recognition over channels exhibiting packet losses. The underlying rationale is that lost feature vectors can never be reconstructed perfectly and therefore reconstruction is carried out at a lower resolution than the resolution of the originally sent features. By doing so, enormous reductions in computational effort can be achieved at a graceful or even no degradation in word accuracy. In experiments conducted on the Aurora II database we obtained for example a reduction of a factor of 30 in computation time for the reconstruction of the soft features without an effect on the word error rate. The proposed method is fully compatible with the ETSI DSR standard, as there are no changes involved in the front-end processing and the transmission format. Index Terms: distributed speech recognition, error concealment, uncertainty decoding, soft feature