GLOTTAL FEATURES FOR SPEECH-BASED COGNITIVE LOAD CLASSIFICATION

被引：13

作者：

Yap, Tet Fei ^{[1
,2
]}

Epps, Julien ^{[1
,2
]}

Choi, Eric H. C. ^{[2
]}

Ambikairajah, Eliathamby ^{[1
,2
]}

机构：

[1] Univ New S Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia

[2] Natl ICT Australia, ATP Res Lab, Eveleigh, 2015, Australia

来源：

2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2010年

关键词：

cognitive load; glottal features; GMM classification; voice quality;

D O I：

10.1109/ICASSP.2010.5494987

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Cognitive load measurement is important when designing adaptive interfaces that optimize the performance of users working on high mental load tasks. Recent research on automatic speech-based measurement system indicates that cognitive load information is more prominent in the frequency region below 1 kHz. This study investigates the effects of cognitive load on glottal parameters (open quotient, normalized amplitude quotient and speed quotient), and proposes a system employing these parameters as features for cognitive load classification. Analysis of the glottal parameter distributions suggests that an increase in cognitive load can be related to a more creaky voice quality. Additionally, three-class classification results show that score-level fusion of systems based on the glottal features and baseline features (MFCCs, pitch, intensity and shifted delta cepstra) improves the baseline accuracy from 79% to 84%.

引用

页码：5234 / 5237

页数：4

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