Reducing bandwidth for robust distributed speech recognition in conditions of packet loss

This paper proposes a method to reduce the bandwidth requirements for a distributed speech recognition (DSR) system, with minimal impact on recognition performance. Bandwidth reduction is achieved by applying a wavelet decomposition to feature vectors extracted from speech using an auditory-based front-end. The resulting vectors undergo vector quantisation and are then combined in pairs for transmission over a statistically modeled channel that is subject to packet burst loss. Recognition performance is evaluated in the presence of both background noise and packet loss. When there is no packet loss, results show that the proposed method can reduce the bandwidth required to 50% of the bandwidth required for the system in which the proposed method is not used, without compromising recognition performance. The bandwidth can be further reduced to 25% of the baseline for a slight decrease in recognition performance. Furthermore, in the presence of packet loss, the proposed method for bandwidth reduction, when combined with a suitable redundancy scheme, gives a 29% reduction in bandwidth, when compared to the recognition performance of an established packet loss mitigation technique.

► Reduced bandwidth for robust distributed speech recognition in noise and packet loss.
► Without packet loss, no compromise on recognition for a 50% bandwidth reduction.
► Without packet loss, 75% bandwidth reduction and small reduction in recognition.
► With packet loss, a bandwidth reduction of 29% compared to the baseline.
► A trade-off between bandwidth requirements and recognition performance is possible.