Multi-flow block interleaving applied to distributed speech recognition over IP networks

Abstract Interleaving has shown to be a useful technique to provide robustdistributed speech recognition over IP networks. This is due toits ability to disperse consecutive losses. However, this ability isrelated to the delay introduced by the interleaver. In this work,we propose a novel multi-flow block interleaver which exploitsthe presence of several streams and allows to reduce the involveddelay. Experimental results have shown that this interleaver ap-proximates the performance of end-to-end interleavers but with afraction of their delay. As disadvantage, this interleaver must beplaced in a common node where more than one flow are available. Index Terms : distributed speech recognition, IP networks, inter-leaving, active networks. 1. Introduction Since its beginning, Internet has been growing in size, incorporat-ing many new networks, as well as in functionality, adding newservices. As many other features have been integrated into In-ternet, such as mailing, instant messaging, telephony and so on,speech enabled services (SES) are also being incorporated. Theseservices provide ubiquitous speech recognition, allowing multipleusers to remotely access and share high performance recognitionengines.A very attractive approach to speech recognition over IP net-works is the distributed speech recognition (DSR) solution [1]. Asmany other services over Internet, it is based on a client-serverarchitecture. On one hand, a simple and low power client (

[1]  Chris Heegard,et al.  A Theory of Interleavers , 1997 .

[2]  Ángel M. Gómez,et al.  A source model mitigation technique for distributed speech recognition over lossy packet channels , 2003, INTERSPEECH.

[3]  Jim Kurose,et al.  Packet Loss Correlation in the MBone Multicast Networ Experimental Measurements and Markov Chain Models , 1995 .

[4]  Mari Ostendorf,et al.  Graceful degradation of speech recognition performance over packet-erasure networks , 2002, IEEE Trans. Speech Audio Process..

[5]  Antonio Rubio,et al.  Statistical-based reconstruction methods for speech recognition in IP networks , 2004 .

[6]  Ángel M. Gómez,et al.  Combining Media-Specific FEC and Error Concealment for Robust Distributed Speech Recognition Over Loss-Prone Packet Channels , 2006, IEEE Transactions on Multimedia.

[7]  Ben P. Milner,et al.  Analysis and compensation of packet loss in distributed speech recognition using interleaving , 2003, INTERSPEECH.

[8]  Juan J. Ramos-Muñoz,et al.  Low Delay Multiflow Block Interleavers for Real-Time Audio Streaming , 2005, ICN.

[9]  Ben P. Milner,et al.  Robust speech recognition over mobile and IP networks in burst-like packet loss , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  G.J. Minden,et al.  A survey of active network research , 1997, IEEE Communications Magazine.

[11]  David Pearce,et al.  The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions , 2000, INTERSPEECH.

[12]  Darren Pearce,et al.  Enabling new speech driven services for mobile devices: An overview of the ETSI standards activities , 2000 .