Graduate School and Research Center in Digital Sciences

N-Best parallel maximum likelihood beamformers for robust speech recognition

Brayda, Luca; Wellekens, Christian J; Omologo, Maurizio

EUSIPCO 2006, European Signal Processing Conference, September 4-8, 2006, Firenze, Italy

This work aims at improving speech recognition in noisy environments using a microphone array. The proposed approach is based on a preliminary generation of N-best hypotheses. The use of an adaptive maximum likelihood beamformer (the Limabeam algorithm), applied in parallel to each hypothesis, leads to an updated set of transcriptions, among which the maximally likely to clean speech models is selected. Results show that this method improves recognition accuracy over both Delay and Sum Beamforming and Unsupervised Limabeam especially at low SNRs. Results also show that it can recover the recognition errors made in the first recognition step.

Document Bibtex

Title:N-Best parallel maximum likelihood beamformers for robust speech recognition
Type:Conference
Language:English
City:Firenze
Country:ITALY
Date:
Department:Digital Security
Eurecom ref:2133
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Bibtex: @inproceedings{EURECOM+2133, year = {2006}, title = {{N}-{B}est parallel maximum likelihood beamformers for robust speech recognition}, author = {{B}rayda, {L}uca and {W}ellekens, {C}hristian {J} and {O}mologo, {M}aurizio}, booktitle = {{EUSIPCO} 2006, {E}uropean {S}ignal {P}rocessing {C}onference, {S}eptember 4-8, 2006, {F}irenze, {I}taly}, address = {{F}irenze, {ITALY}}, month = {09}, url = {http://www.eurecom.fr/publication/2133} }
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