The application of phonetic distribution normalisation to likelihood-maximising speech enhancement for robust ASR
Kleinschmidt, Tristan, Sridharan, Sridha, & Mason, Michael W. (2010) The application of phonetic distribution normalisation to likelihood-maximising speech enhancement for robust ASR. In Tabain, Marija, Fletcher, Janet, Grayden, David, Hajek, John, & Butcher, Andy (Eds.) Proceedings of the 13th Australasian International Conference on Speech Science and Technology, The Australasian Speech Science and Technology Association Inc., La Trobe University, Melbourne, Victoria, pp. 118-121.
Traditional speech enhancement methods optimise signal-level criteria such as signal-to-noise ratio, but such approaches are sub-optimal for noise-robust speech recognition. Likelihood-maximising (LIMA) frameworks on the other hand, optimise the parameters of speech enhancement algorithms based on state sequences generated by a speech recogniser for utterances of known transcriptions. Previous applications of LIMA frameworks have generated a set of global enhancement parameters for all model states without taking in account the distribution of model occurrence, making optimisation susceptible to favouring frequently occurring models, in particular silence. In this paper, we demonstrate the existence of highly disproportionate phonetic distributions on two corpora with distinct speech tasks, and propose to normalise the influence of each phone based on a priori occurrence probabilities. Likelihood analysis and speech recognition experiments verify this approach for improving ASR performance in noisy environments.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Speech Recognition, Speech Enhancement, Optimization Methods|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Institutes > Information Security Institute
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2010 The Australasian Speech Science and Technology Association Inc.|
|Deposited On:||28 Jan 2011 09:13|
|Last Modified:||01 Mar 2012 00:31|
Repository Staff Only: item control page