Emotional information processing based on feature vector enhancement and selection for human–computer interaction via speech

• Published in: Telecommunication systems Vol. 60; no. 2; pp. 201 - 213
• Main Authors: Park, Jeong-sik, Kim, Ji-hwan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2015; Springer Nature B.V
• Subjects: Acoustic noise; Acoustics; Analysis; Artificial Intelligence; Business and Management; Computer Communication Networks; Data processing; Electric communication systems; Emotions; Harmony (Music); Human-computer interaction; Information processing; Interfaces; IT in Business; Mathematical analysis; Multimedia; Noise; Normal distribution; Probability Theory and Stochastic Processes; Smartphones; Speech; Studies; Telecommunications systems; Vectors (mathematics); Voice recognition; Adaptive comb filter; Feature vector classification; Emotional information processing; Speech emotion recognition; Human–computer interaction
• ISSN: 1018-4864; 1572-9451
• DOI: 10.1007/s11235-015-0023-8

This paper proposes techniques for enhancement and selection of emotional feature vectors to correctly process emotional information from users’ spoken data. In real-world devices, speech signals may contain emotional information that is distorted or anomalous owing to environmental noises and the acoustic similarities between emotions. To correctly enhance harmonics of the noise-contaminated speech and thereby utilize them as emotional features, we propose a modified adaptive comb filter, in which the frequency response of the conventional comb filter is re-estimated on the basis of speech presence probability. In addition, to eliminate acoustically anomalous emotional data, we propose a feature vector classification scheme. In this approach, emotional feature vectors are categorized as either discriminative or indiscriminative in an iterative manner, and then only the discriminative vectors are selected for emotional information processing. In emotion recognition experiments using noise-contaminated emotional speech data, our approach exhibited superior performance over the conventional approaches.