IEICE TRANSACTIONS on Information
Missing Feature Theory Applied to Robust Speech Recognition over IP Network
Summary :
This paper addresses problems involved in performing speech recognition over mobile and IP networks. The main problem is speech data loss caused by packet loss in the network. We present two missing-feature-based approaches that recover lost regions of speech data. These approaches are based on the reconstruction of missing frames or on marginal distributions. For comparison, we also use a packing method, which skips lost data. We evaluate these approaches with packet loss models, i.e., random loss and Gilbert loss models. The results show that the marginal-distributed-based technique is most effective for a packet loss environment; the degradation of word accuracy is only 5% when the packet loss rate is 30% and only 3% when mean burst loss length is 24 frames in the case of DSR front-end. The simple data imputation method is also effective in the case of clean speech.
- Publication
- IEICE TRANSACTIONS on Information Vol.E87-D No.5 pp.1119-1126
- Publication Date
- 2004/05/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Speech Dynamics by Ear, Eye, Mouth and Machine)
- Category
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Toshiki ENDO, Shingo KUROIWA, Satoshi NAKAMURA, "Missing Feature Theory Applied to Robust Speech Recognition over IP Network" in IEICE TRANSACTIONS on Information,
vol. E87-D, no. 5, pp. 1119-1126, May 2004, doi: .
Abstract: This paper addresses problems involved in performing speech recognition over mobile and IP networks. The main problem is speech data loss caused by packet loss in the network. We present two missing-feature-based approaches that recover lost regions of speech data. These approaches are based on the reconstruction of missing frames or on marginal distributions. For comparison, we also use a packing method, which skips lost data. We evaluate these approaches with packet loss models, i.e., random loss and Gilbert loss models. The results show that the marginal-distributed-based technique is most effective for a packet loss environment; the degradation of word accuracy is only 5% when the packet loss rate is 30% and only 3% when mean burst loss length is 24 frames in the case of DSR front-end. The simple data imputation method is also effective in the case of clean speech.
URL: https://global.ieice.org/en_transactions/information/10.1587/e87-d_5_1119/_p
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@ARTICLE{e87-d_5_1119,
author={Toshiki ENDO, Shingo KUROIWA, Satoshi NAKAMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Missing Feature Theory Applied to Robust Speech Recognition over IP Network},
year={2004},
volume={E87-D},
number={5},
pages={1119-1126},
abstract={This paper addresses problems involved in performing speech recognition over mobile and IP networks. The main problem is speech data loss caused by packet loss in the network. We present two missing-feature-based approaches that recover lost regions of speech data. These approaches are based on the reconstruction of missing frames or on marginal distributions. For comparison, we also use a packing method, which skips lost data. We evaluate these approaches with packet loss models, i.e., random loss and Gilbert loss models. The results show that the marginal-distributed-based technique is most effective for a packet loss environment; the degradation of word accuracy is only 5% when the packet loss rate is 30% and only 3% when mean burst loss length is 24 frames in the case of DSR front-end. The simple data imputation method is also effective in the case of clean speech.},
keywords={},
doi={},
ISSN={},
month={May},}
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TY - JOUR
TI - Missing Feature Theory Applied to Robust Speech Recognition over IP Network
T2 - IEICE TRANSACTIONS on Information
SP - 1119
EP - 1126
AU - Toshiki ENDO
AU - Shingo KUROIWA
AU - Satoshi NAKAMURA
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2004
AB - This paper addresses problems involved in performing speech recognition over mobile and IP networks. The main problem is speech data loss caused by packet loss in the network. We present two missing-feature-based approaches that recover lost regions of speech data. These approaches are based on the reconstruction of missing frames or on marginal distributions. For comparison, we also use a packing method, which skips lost data. We evaluate these approaches with packet loss models, i.e., random loss and Gilbert loss models. The results show that the marginal-distributed-based technique is most effective for a packet loss environment; the degradation of word accuracy is only 5% when the packet loss rate is 30% and only 3% when mean burst loss length is 24 frames in the case of DSR front-end. The simple data imputation method is also effective in the case of clean speech.
ER -
English
