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首页> 外文会议>Development of multimodal interfaces: Active listening and synchrony >Emotional Vocal Expressions Recognition Using the COST 2102 Italian Database of Emotional Speech
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Emotional Vocal Expressions Recognition Using the COST 2102 Italian Database of Emotional Speech

机译：使用COST 2102意大利情感语音数据库识别情感人声表达

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摘要

The present paper proposes a new speaker-independent approach to the classification of emotional vocal expressions by using the COST 2102 Italian database of emotional speech. The audio records extracted from video clips of Italian movies possess a certain degree of spontaneity and are either noisy or slightly degraded by an interruption making the collected stimuli more realistic in comparison with available emotional databases containing utterances recorded under studio conditions. The audio stimuli represent 6 basic emotional states: happiness, sarcasm/irony, fear, anger, surprise, and sadness. For these more realistic conditions, and using a speaker independent approach, the proposed system is able to classify the emotions under examination with 60.7% accuracy by using a hierarchical structure consisting of a Perceptron and fifteen Gaussian Mixture Models (GMM) trained to distinguish within each pair (couple) of emotions under examination. The best features in terms of high discriminative power were selected by using the Sequential Floating Forward Selection (SFFS) algorithm among a large number of spectral, prosodic and voice quality features. The results were compared with the subjective evaluation of the stimuli provided by human subjects.
机译：本文提出了一种新的独立于说话者的方法，通过使用COST 2102意大利情感语音数据库来对情感语音表达进行分类。从意大利电影的视频剪辑中提取的音频记录具有一定程度的自发性，并且由于干扰而变得嘈杂或略有降低，这使得与在工作室条件下记录的包含言语的可用情感数据库相比，收集到的刺激更加真实。音频刺激代表6种基本的情绪状态：幸福，嘲讽/讽刺，恐惧，愤怒，惊奇和悲伤。对于这些更现实的条件，并且使用独立于说话者的方法，该系统能够通过使用由Perceptron和15个经过训练的高斯混合模型（GMM）组成的分层结构，以60.7％的准确度对被测情绪进行分类。对（夫妇）正在检查的情绪。通过使用顺序浮动前向选择（SFFS）算法，在大量频谱，韵律和语音质量特征中选择了具有高判别能力的最佳特征。将结果与人类受试者提供的刺激的主观评估进行比较。

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