Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper ThBCT9.24
Emotional Speech Classification based on Multi View Characterization
Mahdhaoui, Ammar, Univ. Pierre & Marie Curie
Chetouani, M., Inst. des Systèmes Intelligents et Robotique
Emotional speech classification is a key problem in social interaction analysis. Traditional emotional speech classification
methods are completely supervised and require large amounts of labeled data. In addition, various feature sets are usually
used to characterize the emotional speech signals. Therefore, we propose a new co-training algorithm based on multiview
features. More specifically, we adopt different features for the characterization of speech signals to form different
views for classification, so as to extract as much discriminative information as possible. We then use the co-training algorithm
to classify emotional speech with only few annotations. In this article, a dynamic weighted co-training algorithm is
developed to combine different features (views) to predict the common class variable. Experiments prove the validity and
effectiveness of this method compared to self-training algorithm.
13:30-16:30, Paper ThBCT9.25
Image Inpainting using Structure-Guided Priority Belief Propagation and Label Transformations
Hsin, Heng-Feng, National Chung Cheng Univ.
Leou, Jin-Jang, National Chung Cheng Univ.
Lin, Cheng-Shian, National Chung Cheng Univ.
Chen, Hsuan-Ying, National Chung Cheng Univ.
In this study, an image in painting approach using structure-guided priority belief propagation (BP) and label transformations
is proposed. The proposed approach contains five stages, namely, Markov random field (MRF) node determination,
structure map generation, label set enlargement by label transformations, image in painting by priority-BP optimization,
and overlapped region composition. Based on experimental results obtained in this study, as compared with three comparison
approaches, the proposed approach provides the better image in painting results.
13:30-16:30, Paper ThBCT9.26
Comparison of Syllable/Phone HMM based Mandarin TTS
Duan, Quansheng, Tsinghua Univ.
Kang, Shiyin, Tsinghua Univ.
Shuang, Zhiwei, IBM Res. - China
Wu, Zhiyong, Tsinghua Univ.
Cai, Lianhong, Tsinghua Univ.
Qin, Yong, IBM Res. - China
The performance of HMM-based text to speech (TTS) system is affected by the basic modeling units and the size of
training data. This paper compares two HMM based Mandarin TTS systems using syllable and phone as basic units respectively
with 1000, 3000 and 5000 sentences’ training data. Two female speakers’ corpora are used as training data for
evaluation. For both corpora, the system using syllable as basic unit outperforms the system using phone as basic unit
with 3000 and 5000 sentences’ training data.
13:30-16:30, Paper ThBCT9.27
QRS Complex Detection by Non Linear Thresholding of Modulus Maxima
Jalil, Bushra, Univ. de Bourgogne
Laligant, Olivier, Univ. de Bourgogne
Fauvet, Eric, Univ. de Bourgogne
Beya, Ouadi, Univ. de Bourgogne
Electrocardiogram (ECG) signal is used to analyze the cardiovascular activity in the human body and has a primary role
in the diagnosis of several heart diseases. The QRS complex is the most distinguishable component in the ECG. Therefore,
the accuracy of the detection of QRS complex is crucial to the performance of subsequent machine learning algorithms
for cardiac disease classification. The aim of the present work is to detect QRS wave from ECG signals. Wavelet transform
filtering is applied to the signal in order to remove baseline drift, followed by QRS localization. By using the property of
R peak, having highest and prominent amplitude, we have applied thresholding technique based on the median absolute
deviation(MAD) of modulus maximas to detect the complex. In order to evaluate the algorithm, the analysis has been
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