Auditory-model based robust feature selection for speech recognition
2010 (English)In: Journal of the Acoustical Society of America, ISSN 0001-4966, E-ISSN 1520-8524, Vol. 127, no 2, EL73-EL79 p.Article in journal (Refereed) Published
It is shown that robust dimension-reduction of a feature set for speech recognition can be based on a model of the human auditory system. Whereas conventional methods optimize classification performance, the proposed method exploits knowledge implicit in the auditory periphery, inheriting its robustness. Features are selected to maximize the similarity of the Euclidean geometry of the feature domain and the perceptual domain. Recognition experiments using mel-frequency cepstral coefficients (MFCCs) confirm the effectiveness of the approach, which does not require labeled training data. For noisy data the method outperforms commonly used discriminant-analysis based dimension-reduction methods that rely on labeling. The results indicate that selecting MFCCs in their natural order results in subsets with good performance.
Place, publisher, year, edition, pages
2010. Vol. 127, no 2, EL73-EL79 p.
feature selection, auditory model, sensitivity matrix, speech recognition
IdentifiersURN: urn:nbn:se:kth:diva-11467DOI: 10.1121/1.3284545ISI: 000274322200010ScopusID: 2-s2.0-76349109466OAI: oai:DiVA.org:kth-11467DiVA: diva2:276949
QC 20100831 Uppdaterad från submitted till published (20100831)2009-11-132009-11-132012-09-14Bibliographically approved