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RANSAC-based training data selection for emotion recognition from spontaneous speech
(ACM, 2010)
Training datasets containing spontaneous emotional expressions are often imperfect due the ambiguities and difficulties of labeling such data by human observers. In this paper, we present a Random Sampling Consensus (RANSAC) ...
Improving automatic emotion recognition from speech signals
(International Speech Communications Association, 2009)
We present a speech signal driven emotion recognition system. Our system is trained and tested with the INTERSPEECH 2009 Emotion Challenge corpus, which includes spontaneous and emotionally rich recordings. The challenge ...
INTERSPEECH 2009 duygu tanıma yarışması değerlendirmesi
(IEEE, 2010)
Bu makalede INTERSPEECH 2009 Duygu Tanıma Yarışması sonuçlarını değerlendiriyoruz. Yarışmanın sunduğu problem doğal ve duygu bakımından zengin FAU Aibo konuşma kayıtlarının beş ve iki duygu sınıfına en doğru şekilde ...
Use of line spectral frequencies for emotion recognition from speech
(IEEE, 2010)
We propose the use of the line spectral frequency (LSF) features for emotion recognition from speech, which have not been been previously employed for emotion recognition to the best of our knowledge. Spectral features ...
RANSAC-based training data selection for speaker state recognition
(The International Speech Communications Association, 2011)
We present a Random Sampling Consensus (RANSAC) based training approach for the problem of speaker state recognition from spontaneous speech. Our system is trained and tested with the INTERSPEECH 2011 Speaker State ...
Formant position based weighted spectral features for emotion recognition
(Elsevier, 2011)
In this paper, we propose novel spectrally weighted mel-frequency cepstral coefficient (WMFCC) features for emotion recognition from speech. The idea is based on the fact that formant locations carry emotion-related ...
RANSAC-based training data selection on spectral features for emotion recognition from spontaneous speech
(Springer International Publishing, 2011)
Training datasets containing spontaneous emotional speech are often imperfect due the ambiguities and difficulties of labeling such data by human observers. In this paper, we present a Random Sampling Consensus (RANSAC) ...
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