Title :
Improved classification of acoustic features via primal weight vectors
Author :
Lawson, Aaron ; Harris, David ; Battles, Brandon
Author_Institution :
RADC Inc., Rome, NY, USA
Abstract :
This paper presents a new variation of the Support Vector Machine (SVM) technique for speaker identification in audio. Primal Weight Vectors (PWV) provide a discriminative framework to distinguish between SVM models. Here we discriminate between Gaussian Mixture Model Super Vector (GSV) models, which represent one of the leading SVM based approaches to speaker identification. The PWV-GSV combination has demonstrated a consistent performance advantage over the state of the art GSV classifier on a variety of conditions.
Keywords :
Gaussian processes; acoustic signal processing; feature extraction; signal classification; speaker recognition; support vector machines; Gaussian mixture model super vector model; PWV framework; PWV-GSV classifier; SVM technique; acoustic feature classification; audio feature classification; primal weight vectors; speaker identification; support vector machine technique; Acoustics; Adaptation models; Kernel; Speaker recognition; Support vector machine classification; Vectors; acoustic features; primal weight vectors; speaker recognition; support vector machines;
Conference_Titel :
Applications of Signal Processing to Audio and Acoustics (WASPAA), 2011 IEEE Workshop on
Conference_Location :
New Paltz, NY
Print_ISBN :
978-1-4577-0692-9
Electronic_ISBN :
1931-1168
DOI :
10.1109/ASPAA.2011.6082346
