Log area ratio coefficients (LAR) derived from linear prediction coefficients (LPC) is a well known feature extraction technique used in speech applications. This paper presents a novel way to use the LAR feature in a speaker identification system. Here, instead of using the mel frequency cepstral coefficients (MFCC), the LAR feature is used in a Gaussian mixture model (GMM) based speaker identification system. An F-ratio feature analysis was conducted on both the LAR and MFCC feature vectors which showed the lower order LAR coefficients are superior to MFCC counterpart. The text-independent, closed-set speaker identification rate, as tested on the down-sampled version of TIMIT database, was improved from 96.73%, using the MFCC feature, to 98.81%, using the LAR features. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Chow, D., & Abdulla, W. H. (2004). Speaker identification based on log area ratio and Gaussian mixture models in narrow-band speech speech understanding / interaction. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3157, pp. 901–908). Springer Verlag. https://doi.org/10.1007/978-3-540-28633-2_95
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