A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust scheme for distributed speech recognition over loss-prone packet channels
Speech Communication
VoIP: A comprehensive survey on a promising technology
Computer Networks: The International Journal of Computer and Telecommunications Networking
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Multimedia
A survey of packet loss recovery techniques for streaming audio
IEEE Network: The Magazine of Global Internetworking
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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.