文章基本信息

标题：Quantile Acoustic Vectors vs. MFCC Applied to Speaker Verification
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作者：Mayorga-Ortiz Pedro ; Olguín-Espinoza J. Martín ; González-Arriaga O. Hugo 等
期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2014
卷号：11
DOI：10.5772/56256
语种：English
出版社：SAGE Publications
摘要：In this paper we describe speaker and command recognition related experiments, through quantile vectors and Gaussian Mixture Modelling (GMM). Over the past several years GMM and MFCC have become two of the dominant approaches for modelling speaker and speech recognition applications. However, memory and computational costs are important drawbacks, because autonomous systems suffer processing and power consumption constraints; thus, having a good trade-off between accuracy and computational requirements is mandatory. We decided to explore another approach (quantile vectors in several tasks) and a comparison with MFCC was made. Quantile acoustic vectors are proposed for speaker verification and command recognition tasks and the results showed very good recognition efficiency. This method offered a good trade-off between computation times, characteristics vector complexity and overall achieved efficiency.
关键词：Quantile Vectors; Gaussian Mixture Models (GMM); Speaker Verification