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首页> 外文会议>Humaine Association Conference on Affective Computing and Intelligent Interaction >Determining the Smallest Emotional Unit for Level of Arousal Classification
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Determining the Smallest Emotional Unit for Level of Arousal Classification

机译：确定最小的情绪单位，以获得唤醒分类水平

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Most state-of-the-art emotion recognition methods are based on turn- and frame-level analysis independent from phonetic transcription. Currently "affective computing" community could not specify the smallest emotional standard unit which can be easily classified and determined by any "advanced" and "non-advanced" listener. It is known that, acoustic modeling on the smallest phonetic unit (phoneme) started a new era in automatic speech recognition: switch from speaker dependent isolated word recognition to speaker independent continuous speech recognition. In or current research we showed that phoneme can be used as as smallest unit for high and low arousal emotion classification task. We trained our classifications models on the VAM dataset material and evaluated them on speech samples from the DES dataset. For our experiments we employed two different emotion classification approaches: general (phonetic pattern independent) and phoneme-based (phonetic pattern dependent). Both classification approaches used MFFC features extracted on the frame level. Our experimental results impressively show that the proposed phoneme-based classification technique could increase emotion classification performance by about 9.68% absolute (15.98% relative). We showed that phoneme-level emotion models trained on "natural" emotions could provide impressive classification performance on dataset with acted affective content.
机译：大多数最先进的情感识别方法基于转弯和帧级别分析，与语音转录无关。目前“情感计算”社区无法指定最小的情绪标准单元，这些单位可以通过任何“高级”和“非高级”侦听器轻松分类和确定。众所周知，最小拼音单元（音素）上的声学建模在自动语音识别中启动了一个新的时代：从扬声器依赖于扬声器的分离字识别切换到扬声器独立的连续语音识别。在或目前的研究我们展示了音素可以用作高低唤起情绪分类任务的最小单位。我们在VAM数据集材料上培训了我们的分类模型，并在DES DataSet上的语音样本上进行评估。对于我们的实验，我们雇用了两种不同的情感分类方法：一般（语音模式独立）和基于音素的（依赖语音模式）。两种分类方法都在帧级别提取的MFFC功能。我们的实验结果令人印象深刻地表明，所提出的基于音素的分类技术可以将情绪分类性能提高约9.68％（相对）。我们展示了对“自然”情绪培训的音素级情感模型可以在数据集中提供令人印象深刻的分类性能，具有作用情感内容。

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