Feature and Signal Enhancement for Robust Speaker Identification of G.729 Decoded Speech

Autor:	Ravi P. Ramachandran, Sachin Shetty, Kalpesh Raval, Brett Y. Smolenski
Rok vydání:	2012
Předmět:	Computer science business.industry Speech recognition Speech coding Pattern recognition Mixture model Identification (information) Feature (computer vision) Spectral envelope Distortion Codec Affine transformation Artificial intelligence business
Zdroj:	Neural Information Processing ISBN: 9783642344992 ICONIP (5)
DOI:	10.1007/978-3-642-34500-5_41
Popis:	For wireless remote access security, there is an emerging need for biometric speaker identification systems (SID) to be robust to speech coding distortion. This paper presents results on a Gaussian mixture model (GMM) based SID system that is trained on clean speech and tested on the decoded speech of the G.729 codec. To mitigate the performance loss due to mismatched training and testing conditions, five robust features, two enhancement approaches and three fusion strategies are used. The first enhancement method is feature compensation based on the affine transform. The second is the McCree signal enhancement approach based on the spectral envelope information in the G.729 bit stream. Ensemble systems using decision level, score fusion and Borda count are studied. The best performance is obtained by performing signal enhancement, feature compensation and decision level fusion. This results in an identification success rate (ISR) of 89.8%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5401e8f116cec386d9e971f8af3ea9b0 https://doi.org/10.1007/978-3-642-34500-5_41 Zobrazit plný text záznamu