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An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i> (Vol. 1, p. I). <a href=\"https://doi.org/10.1109/ICASSP.2006.1659984\">https://doi.org/10.1109/ICASSP.2006.1659984</a>"},"main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2006/IoHa06-2.pdf","open_access":"1"}],"language":[{"iso":"eng"}],"doi":"10.1109/ICASSP.2006.1659984","year":"2006","title":"An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features","author":[{"first_name":"Valentin","last_name":"Ion","full_name":"Ion, Valentin"},{"id":"242","first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold"}],"date_updated":"2022-01-06T06:51:10Z","intvolume":"         1","date_created":"2019-07-12T05:28:58Z","keyword":["distributed speech recognition","least mean squares methods","MAP estimate","maximum likelihood estimation","MMSE estimate","packet loss compensation scheme","packet switched communication","posteriori probability density function","robust error mitigation method","soft-features","speech recognition","table lookup","voice communication","wireless channels"],"type":"conference","department":[{"_id":"54"}],"publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)","abstract":[{"text":"Soft-feature based speech recognition, which is an example of uncertainty decoding, has been proven to be a robust error mitigation method for distributed speech recognition over wireless channels exhibiting bit errors. In this paper we extend this concept to packet-oriented transmissions. The a posteriori probability density function of the lost feature vector, given the closest received neighbours, is computed. In the experiments, the nearest frame repetition, which is shown to be equivalent to the MAP estimate, outperforms the MMSE estimate for long bursts. Taking the variance into account at the speech recognition stage results in superior performance compared to classical schemes using point estimates. A computationally and memory efficient implementation of the proposed packet loss compensation scheme based on table lookup is presented","lang":"eng"}]}]