Comparison of sensibilities of Japanese and Koreans in recognizing emotions from speech by using Bayesian networks

Jangsik, Cho; Kato, Shōhei

doi:10.1109/icsmc.2009.5346125

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…Speech has three components: prosody, tone, and phoneme. Past research indicates that the prosodic component has important non-verbal information such as emotional expressions (Cowie et al, 2001), (Scherer et al, 2003), (Cho et al, 2009). In accordance with our hypothesis, cognitive impairment was observed in the elderly (Taler and Phillips, 2007), (Taler et al, 2008).…”

Section: Prosodic Feature Extractionsupporting

confidence: 90%

STATISTICAL ANALYSIS OF THE SIGNAL AND PROSODIC SIGN OF COGNITIVE IMPAIRMENT IN ELDERLY-SPEECH - A Preliminary Study

Kato¹,

Suzuki²,

Kobayashi³

et al. 2011

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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This paper presents a novel approach for early detection of cognitive impairment in the elderly. Our approach incorporates the use of speech sound analysis and multivariate statistical techniques. In this paper, we focus on the prosodic features of speech. Fifty six Japanese subjects (22 males and 34 females between the ages of 64 and 90 years) participated in this study. Blind to clinical groups, we collected speech sounds from segments of dialogue during an HDS-R examination. The segments corresponds to speech sounds from answers to questions about time orientation and number backward counting. Ninety eight prosodic features were extracted from each of the speech sounds. These prosodic features consisted of spectral and pitch features (13), formant features (61), intensity features (22), and speech rate and response time (2). These features were refined by principal component analysis and/or feature selection. In addition, we calculated speech prosody-based cognitive impairment rating (SPCIR) by multiple linear regression analysis. The results indicate that a moderately significant correlation exists between the HDS-R score and the synthesis of several selected prosodic features. Consequently, the adjusted coefficient of determination (R 2 = 0.57) suggests that prosody-based speech sound analysis could potentially be used to detect cognitive impairment in elderly subjects.

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