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
Department:Digital Security
Eurecom ref:2133
Copyright: © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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 = {} }
See also: