Dobrišek, Simon; Gajšek, Rok; Mihelič, France; Pavešić, Nikola; Štruc, Vitomir
Towards efficient multi-modal emotion recognition Journal Article
In: International Journal of Advanced Robotic Systems, vol. 10, no. 53, 2013.
The paper presents a multi-modal emotion recognition system exploiting audio and video (i.e., facial expression) information. The system first processes both sources of information individually to produce corresponding matching scores and then combines the computed matching scores to obtain a classification decision. For the video part of the system, a novel approach to emotion recognition, relying on image-set matching, is developed. The proposed approach avoids the need for detecting and tracking specific facial landmarks throughout the given video sequence, which represents a common source of error in video-based emotion recognition systems, and, therefore, adds robustness to the video processing chain. The audio part of the system, on the other hand, relies on utterance-specific Gaussian Mixture Models (GMMs) adapted from a Universal Background Model (UBM) via the maximum a posteriori probability (MAP) estimation. It improves upon the standard UBM-MAP procedure by exploiting gender information when building the utterance-specific GMMs, thus ensuring enhanced emotion recognition performance. Both the uni-modal parts as well as the combined system are assessed on the challenging multi-modal eNTERFACE'05 corpus with highly encouraging results. The developed system represents a feasible solution to emotion recognition that can easily be integrated into various systems, such as humanoid robots, smart surveillance systems and alike.
Gajšek, Rok; Štruc, Vitomir; Mihelič, France
In: Proceedings of the International Conference on Pattern Recognition (ICPR), pp. 4133-4136, IAPR Istanbul, Turkey, 2010.
The information of the psycho-physical state of the subject is becoming a valuable addition to the modern audio or video recognition systems. As well as enabling a better user experience, it can also assist in superior recognition accuracy of the base system. In the article, we present our approach to multi-modal (audio-video) emotion recognition system. For audio sub-system, a feature set comprised of prosodic, spectral and cepstrum features is selected and support vector classifier is used to produce the scores for each emotional category. For video sub-system a novel approach is presented, which does not rely on the tracking of specific facial landmarks and thus, eliminates the problems usually caused, if the tracking algorithm fails at detecting the correct area. The system is evaluated on the eNTERFACE database and the recognition accuracy of our audio-video fusion is compared to the published results in the literature.
Gajšek, Rok; Štruc, Vitomir; Mihelič, France
In: Proceedings of Text, Speech and Dialogue (TSD), pp. 275-282, Springer-Verlag, Berlin, Heidelberg, 2010.
The standard features used in emotion recognition carry, besides the emotion related information, also cues about the speaker. This is expected, since the nature of emotionally colored speech is similar to the variations in the speech signal, caused by different speakers. Therefore, we present a gradient descent derived transformation for the decoupling of emotion and speaker information contained in the acoustic features. The Interspeech ’09 Emotion Challenge feature set is used as the baseline for the audio part. A similar procedure is employed on the video signal, where the nuisance attribute projection (NAP) is used to derive the transformation matrix, which contains information about the emotional state of the speaker. Ultimately, different NAP transformation matrices are compared using canonical correlations. The audio and video sub-systems are combined at the matching score level using different fusion techniques. The presented system is assessed on the publicly available eNTERFACE’05 database where significant improvements in the recognition performance are observed when compared to the stat-of-the-art baseline.
Gajšek, Rok; Štruc, Vitomir; Dobrišek, Simon; Mihelič, France
In: Speech and intelligence: proceedings of Interspeech 2009, pp. 1967-1970, Brighton, UK, 2009.
The paper discuses the usage of linear transformations of Hidden Markov Models, normally employed for speaker and environment adaptation, as a way of extracting the emotional components from the speech. A constrained version of Maximum Likelihood Linear Regression (CMLLR) transformation is used as a feature for classification of normal or aroused emotional state. We present a procedure of incrementally building a set of speaker independent acoustic models, that are used to estimate the CMLLR transformations for emotion classification. An audio-video database of spontaneous emotions (AvID) is briefly presented since it forms the basis for the evaluation of the proposed method. Emotion classification using the video part of the database is also described and the added value of combining the visual information with the audio features is shown.
Gajšek, Rok; Štruc, Vitomir; Mihelič, France; Podlesek, Anja; Komidar, Luka; Sočan, Gregor; Bajec, Boštjan
Multi-modal emotional database: AvID Journal Article
In: Informatica (Ljubljana), vol. 33, no. 1, pp. 101-106, 2009.
This paper presents our work on recording a multi-modal database containing emotional audio and video recordings. In designing the recording strategies a special attention was payed to gather data involving spontaneous emotions and therefore obtain a more realistic training and testing conditions for experiments. With specially planned scenarios including playing computer games and conducting an adaptive intelligence test different levels of arousal were induced. This will enable us to both detect different emotional states as well as experiment in speaker identification/verification of people involved in communications. So far the multi-modal database has been recorded and basic evaluation of the data was processed.
Gajšek, Rok; Štruc, Vitomir; Dobrišek, Simon; Žibert, Janez; Mihelič, France; Pavešić, Nikola
In: Biometric ID management and multimodal communication, pp. 114-121, Springer-Verlag, Berlin, Heidelberg, 2009.
The paper presents our initial attempts in building an audio video emotion recognition system. Both, audio and video sub-systems are discussed, and description of the database of spontaneous emotions is given. The task of labelling the recordings from the database according to different emotions is discussed and the measured agreement between multiple annotators is presented. Instead of focusing on the prosody in audio emotion recognition, we evaluate the possibility of using linear transformations (CMLLR) as features. The classification results from audio and video sub-systems are combined using sum rule fusion and the increase in recognition results, when using both modalities, is presented.
Gajšek, Rok; Podlesek, Anja; Komidar, Luka; Sočan, Grekor; Bajec, Boštjan; Štruc, Vitomir; Bucik, Valentin; Mihelič, France
AvID: audio-video emotional database Inproceedings
In: Proceedings of the 11th International Multi-conference Information Society (IS'08), pp. 70-74, Ljubljana, Slovenia, 2008.