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- W1976301511 abstract "Speech Emotion Recognition has become more and more important in the development of human-machine interactions. Long term features for the entire sentence are usually obtained by using statistical functions on the low-level descriptors such as pitch or energy at a frame level. In this paper, unlike the former statistical method, we propose a set of new long term features, denoted by LTF-HMS, based on HHT method. Compared to those statistic features based on the FFT, we tested what an important role the LTF-HMS plays in the experiment and the Berlin Emotional Database is applied as our corpus. The experimental results demonstrate that 39.5% of misclassification samples in shorter sentences and 25% misclassification samples in longer sentences caused by using FFT are identified correctly after integrating the LTF-HMS features." @default.
- W1976301511 created "2016-06-24" @default.
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- W1976301511 date "2012-07-01" @default.
- W1976301511 modified "2023-09-25" @default.
- W1976301511 title "HHT based long term feature extracting method for speech emotion classification" @default.
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- W1976301511 doi "https://doi.org/10.1109/icalip.2012.6376625" @default.
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