Wissenschaftliche Zitierungen
Die Technologie von audEERING wird in zahlreichen Forschungsprojekten eingesetzt. Nachfolgend finden Sie eine Auswahl von Referenzen und Zitaten aus verschiedenen Bereichen. Bitte beachten Sie auch unsere eigenen wissenschaftlichen Veröffentlichungen.
- Singh, N., Singh, N., & Dhall, A. (2017). Continuous Multimodal Emotion Recognition Approach for AVEC 2017. arXiv preprint arXiv:1709.05861.
- Vielzeuf, V., Pateux, S., & Jurie, F. (2017, November). Temporal multimodal fusion for video emotion classification in the wild. In Proceedings of the 19th ACM International Conference on Multimodal Interaction (pp. 569-576). ACM.
- Tao, F., & Liu, G. (2018, April). Advanced LSTM: A study about better time dependency modeling in emotion recognition. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2906-2910). IEEE.
- Tian, L., Muszynski, M., Lai, C., Moore, J. D., Kostoulas, T., Lombardo, P., ... & Chanel, G. (2017, Oktober). Erkennen induzierter Emotionen von Kinobesuchern: Are induced and perceived emotions the same? In Affective Computing and Intelligent Interaction (ACII), 2017 Seventh International Conference on (pp. 28-35). IEEE.
- Gamage, K. W., Sethu, V., & Ambikairajah, E. (2017, Oktober). Modeling variable length phoneme sequences-A step towards linguistic information for speech emotion recognition in wider world. In Affective Computing and Intelligent Interaction (ACII), 2017 Seventh International Conference on (pp. 518-523). IEEE.
- Knyazev, B., Shvetsov, R., Efremova, N., & Kuharenko, A. (2017). Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video. arXiv preprint arXiv:1711.04598.
- Gaus, Y. F. A., Meng, H., & Jan, A. (2017, June). Decoupling Temporal Dynamics for Naturalistic Affect Recognition in a Two-Stage Regression Framework. In Cybernetics (CYBCONF), 2017 3rd IEEE International Conference on (pp. 1-6). IEEE.
- Cambria, E., Hazarika, D., Poria, S., Hussain, A., & Subramaanyam, R. B. V. (2017). Benchmarking multimodal sentiment analysis. arXiv preprint arXiv:1707.09538.
- Torres, J. M. M., & Stepanov, E. A. (2017, August). Enhanced face/audio emotion recognition: video and instance level classification using ConvNets and restricted Boltzmann Machines. In Proceedings of the International Conference on Web Intelligence (S. 939-946). ACM.
- Siegert, I., Lotz, A. F., Egorow, O., & Wendemuth, A. (2017, September). Improving Speech-Based Emotion Recognition by Using Psychoacoustic Modeling and Analysis-by-Synthesis. In International Conference on Speech and Computer (S. 445- 455). Springer, Cham.
- Huang, C. W., & Narayanan, S. S. (2017, Juli). Deep convolutional recurrent neural network with attention mechanism for robust speech emotion recognition. In Multimedia and Expo (ICME), 2017 IEEE International Conference on (pp. 583- 588). IEEE.
- Dhall, A., Goecke, R., Joshi, J., Wagner, M., & Gedeon, T. (2013, Dezember). Emotion recognition in the wild challenge 2013. In Proceedings of the 15th ACM on International conference on multimodal interaction (pp. 509-516). ACM.
- Dhall, A., Goecke, R., Joshi, J., Sikka, K., & Gedeon, T. (2014, November). Emotion recognition in the wild challenge 2014: Baseline, data and protocol. In Proceedings of the 16th International Conference on Multimodal Interaction (S. 461-466). ACM.
- Liu, M., Wang, R., Li, S., Shan, S., Huang, Z., & Chen, X. (2014, November). Combining multiple kernel methods on riemannian manifold for emotion recognition in the wild. In Proceedings of the 16th International Conference on Multimodal Interaction (pp. 494-501). ACM.
- Dhall, A., Ramana Murthy, O. V., Goecke, R., Joshi, J., & Gedeon, T. (2015, November). Video- und bildbasierte Emotionserkennung - Herausforderungen in der freien Wildbahn: Emotiw 2015. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (pp. 423-426). ACM.
- Savran, A., Cao, H., Shah, M., Nenkova, A., & Verma, R. (2012, Oktober). Combining video, audio and lexical indicators of affect in spontaneous conversation via particle filtering. In Proceedings of the 14th ACM international conference on Multimodal interaction (pp. 485-492). ACM.
- Poria, S., Cambria, E., & Gelbukh, A. F. (2015, September). Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis. In EMNLP (pp. 2539-2544).
- Zheng, W., Xin, M., Wang, X., & Wang, B. (2014). A novel speech emotion recognition method via incomplete sparse least square regression. IEEE Signal Processing Letters, 21(5), 569-572.
- Bhattacharya, A., Wu, W., & Yang, Z. (2012). Quality of experience evaluation of voice communication: an affect-based approach. Human-centric Computing and Information Sciences, 2(1), 7.
- Bone, D., Lee, C. C., & Narayanan, S. (2014). Robust unsupervised arousal rating: A rule-based framework withknowledge-inspired vocal features. IEEE transactions on affective computing, 5(2), 201-213.
- Liu, M., Wang, R., Huang, Z., Shan, S., & Chen, X. (2013, Dezember). Partial least squares regression on grassmannian manifold for emotion recognition. In Proceedings of the 15th ACM on International conference on multimodal interaction (pp. 525-530). ACM.
- Audhkhasi, K., & Narayanan, S. (2013). A globally-variant locally-constant model for fusion of labels from multiple diverse experts without using reference labels. IEEE transactions on pattern analysis and machine intelligence, 35(4), 769-783.
- Mariooryad, S., & Busso, C. (2013). Exploring cross-modality affective reactions for audiovisual emotion recognition. IEEE Transactions on affective computing, 4(2), 183-196.
- Chen, J., Chen, Z., Chi, Z., & Fu, H. (2014, November). Emotion recognition in the wild with feature fusion and multiple kernel learning. In Proceedings of the 16th International Conference on Multimodal Interaction (pp. 508-513). ACM.
- Rosenberg, A. (2012). Classifying Skewed Data: Importance Weighting to Optimize Average Recall. In Interspeech (pp. 2242-2245).
- Sun, R., & Moore, E. (2011). Untersuchung von glottalen Parametern und Teager-Energieoperatoren in der Emotionserkennung. Affective Computing and Intelligent Interaction, 425-434.
- Sun, B., Li, L., Zuo, T., Chen, Y., Zhou, G., & Wu, X. (2014, November). Combining multimodal features with hierarchical classifier fusion for emotion recognition in the wild. In Proceedings of the 16th International Conference on Multimodal Interaction (S. 481-486). ACM.
- Mariooryad, S., & Busso, C. (2015). Correcting time-continuous emotional labels by modeling the reaction lag of evaluators. IEEE Transactions on Affective Computing, 6(2), 97-108.
- Ivanov, A., & Riccardi, G. (2012, März). Kolmogorov-Smirnov test for feature selection in emotion recognition from speech. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on (pp. 5125-5128). IEEE.
- Mariooryad, S., & Busso, C. (2013, September). Analysis and compensation of the reaction lag of evaluators in continuous emotional annotations. In Affective Computing and Intelligent Interaction (ACII), 2013 Humaine Association Conference on (pp. 85-90). IEEE.
- Alonso-Martín, F., Malfaz, M., Sequeira, J., Gorostiza, J. F., & Salichs, M. A. (2013). Ein multimodales System zur Erkennung von Emotionen während der Mensch-Roboter-Interaktion. Sensors, 13(11), 15549-15581.
- Moore, J. D., Tian, L., & Lai, C. (2014, April). Word-level emotion recognition using high-level features. In International Conference on Intelligent Text Processing and Computational Linguistics (S. 17-31). Springer Berlin Heidelberg.
- Cao, H., Verma, R., & Nenkova, A. (2015). Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech. Computer Speech & Language, 29(1), 186-202.
- Mariooryad, S., & Busso, C. (2014). Compensating for speaker or lexical variabilities in speech for emotion recognition. Speech Communication, 57, 1-12.
- Wu, C. H., Lin, J. C., & Wei, W. L. (2014). Survey on audiovisual emotion recognition: databases, features, and data fusion strategies. APSIPA transactions on signal and information processing, 3, e12.
- Busso, C., Mariooryad, S., Metallinou, A., & Narayanan, S. (2013). Iterative feature normalization scheme for automatic emotion detection from speech. IEEE transactions on Affective computing, 4(4), 386-397.
- Galanis, D., Karabetsos, S., Koutsombogera, M., Papageorgiou, H., Esposito, A., & Riviello, M. T. (2013, Dezember). Classification of emotional speech units in call centre interactions. In Cognitive Infocommunications (CogInfoCom), 2013 IEEE 4th International Conference on (pp. 403-406). IEEE.
- Sidorov, M., Brester, C., Minker, W., & Semenkin, E. (2014, Mai). Speech-Based Emotion Recognition: Feature Selection by Self-Adaptive Multi-Criteria Genetic Algorithm. In LREC (pp. 3481-3485).
- Oflazoglu, C., & Yildirim, S. (2013). Erkennung von Emotionen aus türkischer Sprache anhand akustischer Merkmale. EURASIP Journal on Audio, Speech, and Music Processing, 2013(1), 26.
- Kaya, H., & Salah, A. A. (2016). Combining modality-specific extreme learning machines for emotion recognition in the wild. Journal on Multimodal User Interfaces, 10(2), 139-149.
- Amer, M. R., Siddiquie, B., Richey, C., & Divakaran, A. (2014, Mai). Emotion detection in speech using deep networks. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on (pp. 3724-3728). IEEE.
- Poria, S., Chaturvedi, I., Cambria, E., & Hussain, A. (2016, Dezember). Convolutional MKL based multimodal emotion recognition and sentiment analysis. In Data Mining (ICDM), 2016 IEEE 16th International Conference on (pp. 439-448). IEEE.
- Kaya, H., Çilli, F., & Salah, A. A. (2014, November). Ensemble CCA for continuous emotion prediction. In Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge (pp. 19-26). ACM.
- Mariooryad, S., Lotfian, R., & Busso, C. (2014, September). Building a naturalistic emotional speech corpus by retrieving expressive behaviors from existing speech corpora. In INTERSPEECH (pp. 238-242).
- Busso, C., Parthasarathy, S., Burmania, A., Abdel-Wahab, M., Sadoughi, N., & Provost, E. M. (2017). MSP-IMPROV: An acted corpus of dyadic interactions to study emotion perception. IEEE Transactions on Affective Computing, 8(1), 67-80.
- Jin, Q., Li, C., Chen, S., & Wu, H. (2015, April). Speech emotion recognition with acoustic and lexical features. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (pp. 4749-4753). IEEE.
- Peng, S. O. N. G., Yun, J. I. N., Li, Z. H. A. O., & Minghai, X. I. N. (2014). Speech emotion recognition using transfer learning. IEICE TRANSACTIONS on Information and Systems, 97(9), 2530-2532.
- Huang, D. Y., Zhang, Z., & Ge, S. S. (2014). Speaker state classification based on fusion of asymmetric simple partial least squares (SIMPLS) and support vector machines. Computer Speech & Language, 28(2), 392-419.
- Sun, Y., Wen, G., & Wang, J. (2015). Gewichtete spektrale Merkmale basierend auf lokalen Hu-Momenten für die Sprach-Emotionserkennung. Biomedizinische Signalverarbeitung und Steuerung, 18, 80-90.
- Kaya, H., Gürpinar, F., Afshar, S., & Salah, A. A. (2015, November). Contrasting and combining least squares based learners for emotion recognition in the wild. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (S. 459-466). ACM.
- Banda, N., & Robinson, P. (2011, November). Noise analysis in audio-visual emotion recognition. In Proceedings of the International Conference on Multimodal Interaction (pp. 1-4).
- Chen, S., & Jin, Q. (2015, Oktober). Multi-modal dimensional emotion recognition using recurrent neural networks. In Proceedings of the 5th International Workshop on Audio/Visual Emotion Challenge (pp. 49-56). ACM.
- Audhkhasi, K., Sethy, A., Ramabhadran, B., & Narayanan, S. S. (2012, März). Creating ensemble of diverse maximum entropy models. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on (pp. 4845-4848). IEEE.
- Lubis, N., Sakti, S., Neubig, G., Toda, T., Purwarianti, A., & Nakamura, S. (2016). Emotionen und ihre Auslöser im menschlichen Sprachdialog: Recognition and analysis. In Situated Dialog in Speech-Based Human-Computer Interaction (pp. 103-110). Springer International Publishing.
- Song, P., Jin, Y., Zha, C., & Zhao, L. (2014). Speech emotion recognition method based on hidden factor analysis. Electronics Letters, 51(1), 112-114.
- Dhall, A., Goecke, R., Joshi, J., Wagner, M., & Gedeon, T. (2013, Dezember). Emotion recognition in the wild challenge (EmotiW) challenge and workshop summary. In Proceedings of the 15th ACM on International conference on multimodal interaction (pp. 371-372). ACM.
- Chen, L., Yoon, S. Y., Leong, C. W., Martin, M., & Ma, M. (2014, November). Eine erste Analyse von strukturierten Videointerviews mit Hilfe von multimodaler Emotionserkennung. In Proceedings of the 2014 workshop on Emotion Representation and Modelling in Human-Computer-Interaction-Systems (pp. 1-6). ACM.
- Brester, C., Semenkin, E., Sidorov, M., & Minker, W. (2014). Self-adaptive multi-objective genetic algorithms for feature selection. In Proceedings of International Conference on Engineering and Applied Sciences Optimization (pp. 1838-1846).
- Tian, L., Lai, C., & Moore, J. (2015, April). Recognizing emotions in dialogues with disfluencies and non-verbal vocalisations. In Proceedings of the 4th Interdisciplinary Workshop on Laughter and Other Nonverbal Vocalisations in Speech (Vol. 14, S. 15).
- Lopez-Otero, P., Docio-Fernandez, L., & Garcia-Mateo, C. (2014). iVectors for continuous emotion recognition. Training, 45, 50.
- Bojanic, M., Crnojevic, V., & Delic, V. (2012, September). Application of neural networks in emotional speech recognition. In Neural Network Applications in Electrical Engineering (NEUREL), 2012 11th Symposium on (pp. 223-226). IEEE.
- Kim, J. C., & Clements, M. A. (2015). Multimodal affect classification at various temporal lengths. IEEE Transactions on Affective Computing, 6(4), 371-384.
- Bone, D., Lee, C. C., Potamianos, A., & Narayanan, S. S. (2014). An investigation of vocal arousal dynamics in child-psychologist interactions using synchrony measures and a conversation-based model. In INTERSPEECH (pp. 218-222).
- Day, M. (2013, Dezember). Emotion recognition with boosted tree classifiers. In Proceedings of the 15th ACM on International conference on multimodal interaction (pp. 531-534). ACM.
- Sidorov, M., Ultes, S., & Schmitt, A. (2014, Mai). Comparison of Gender and Speaker-adaptive Emotion Recognition. In LREC (pp. 3476-3480).
- Tian, L., Moore, J. D., & Lai, C. (2015, September). Emotion recognition in spontaneous and acted dialogues. In Affective Computing and Intelligent Interaction (ACII), 2015 International Conference on (pp. 698-704). IEEE.
- Sun, B., Li, L., Zhou, G., Wu, X., He, J., Yu, L., ... & Wei, Q. (2015, November). Combining multimodal features within a fusion network for emotion recognition in the wild. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (pp. 497-502). ACM.
- Ellis, J. G., Lin, W. S., Lin, C. Y., & Chang, S. F. (2014, Dezember). Predicting evoked emotions in video. In Multimedia (ISM), 2014 IEEE International Symposium on (pp. 287-294). IEEE.
- Brester, C., Semenkin, E., Kovalev, I., Zelenkov, P., & Sidorov, M. (2015, Mai). Evolutionäre Merkmalsauswahl für die Emotionserkennung in der mehrsprachigen Sprachanalyse. In Evolutionary Computation (CEC), 2015 IEEE Congress on (pp. 2406-2411). IEEE.
- Zhang, B., Provost, E. M., Swedberg, R., & Essl, G. (2015, Januar). Predicting Emotion Perception Across Domains: A Study of Singing and Speaking. In AAAI (S. 1328-1335).
- Brester, C., Sidorov, M., & Semenkin, E. (2014). Speech-based emotion recognition: Application of collective decision making concepts. In Proceedings of the 2nd International Conference on Computer Science and Artificial Intelligence (ICCSAI2014) (pp. 216-220).
- Cao, H., Savran, A., Verma, R., & Nenkova, A. (2015). Akustische und lexikalische Repräsentationen zur Vorhersage von Affekten in spontanen Gesprächen. Computer Speech & Language, 29(1), 203-217.
- Sidorov, M., Brester, C., Semenkin, E., & Minker, W. (2014, September). Speaker state recognition with neural network-based classification and self-adaptive heuristic feature selection. In Informatics in Control, Automation and Robotics (ICINCO), 2014 11th International Conference on (Vol. 1, pp. 699-703). IEEE.
- Tickle, A., Raghu, S., & Elshaw, M. (2013). Emotionale Erkennung aus dem Sprachsignal für einen virtuellen Bildungsagenten. In Journal of Physics: Conference Series (Vol. 450, No. 1, p. 012053). IOP Publishing.
- Vinciarelli, A., & Mohammadi, G. (2014). A survey of personality computing. IEEE Transactions on Affective Computing, 5(3), 273-291.
- Pohjalainen, J., Räsänen, O., & Kadioglu, S. (2015). Methoden der Merkmalsauswahl und ihre Kombinationen bei der hochdimensionalen Klassifizierung von Sprechersympathie, Verständlichkeit und Persönlichkeitsmerkmalen. Computer Speech & Language, 29(1), 145-171.
- Ivanov, A. V., Riccardi, G., Sporka, A. J., & Franc, J. (2011). Recognition of Personality Traits from Human Spoken Conversations. In INTERSPEECH (pp. 1549-1552).
- Chastagnol, C., & Devillers, L. (2012). Personality traits detection using a parallelized modified SFFS algorithm. computing, 15, 16.
- Alam, F., & Riccardi, G. (2013, August). Comparative study of speaker personality traits recognition in conversational and broadcast news speech. In INTERSPEECH (pp. 2851-2855).
- Alam, F., & Riccardi, G. (2014, Mai). Fusion of acoustic, linguistic and psycholinguistic features for speaker personality traits recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on (pp. 955-959). IEEE.
- Wagner, J., Lingenfelser, F., & André, E. (2012). A Frame Pruning Approach for Paralinguistic Recognition Tasks. In INTERSPEECH (pp. 274-277).
- Feese, S., Muaremi, A., Arnrich, B., Troster, G., Meyer, B., & Jonas, K. (2011, Oktober). Discriminating individually considerate and authoritarian leaders by speech activity cues. In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on (pp. 1460-1465). IEEE.
- Liu, G., & Hansen, J. H. (2014). Supra-segmental feature based speaker trait detection. In Proc. Odyssey.
- Liu, C. J., Wu, C. H., & Chiu, Y. H. (2013, Oktober). BFI-based speaker personality perception using acoustic-prosodic features. In Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific (pp. 1-6). IEEE.
- Grünerbl, A., Muaremi, A., Osmani, V., Bahle, G., Oehler, S., Tröster, G., ... & Lukowicz, P. (2015). Smartphone-basierte Erkennung von Zuständen und Zustandsänderungen bei Patienten mit bipolarer Störung. IEEE Journal of Biomedical and Health Informatics, 19(1), 140-148.
- Gravenhorst, F., Muaremi, A., Bardram, J., Grünerbl, A., Mayora, O., Wurzer, G., ... & Tröster, G. (2015). Mobiltelefone als medizinische Geräte in der Behandlung psychischer Störungen: ein Überblick. Personal and Ubiquitous Computing, 19(2), 335-353.
- Cummins, N., Joshi, J., Dhall, A., Sethu, V., Goecke, R., & Epps, J. (2013, Oktober). Diagnose von Depressionen anhand von Verhaltenssignalen: ein multimodaler Ansatz. In Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge (pp. 11-20). ACM.
- Alghowinem, S., Goecke, R., Wagner, M., Epps, J., Breakspear, M., & Parker, G. (2012, Mai). From Joyous to Clinically Depressed: Mood Detection Using Spontaneous Speech. In FLAIRS Conference.
- Joshi, J., Goecke, R., Alghowinem, S., Dhall, A., Wagner, M., Epps, J., ... & Breakspear, M. (2013). Multimodale assistive Technologien für die Diagnose und Überwachung von Depressionen. Journal on Multimodal User Interfaces, 7(3), 217-228.
- Alghowinem, S., Goecke, R., Wagner, M., Epps, J., Breakspear, M., & Parker, G. (2013, Mai). Detecting depression: a comparison between spontaneous and read speech. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 7547-7551). IEEE.
- Cummins, N., Epps, J., Sethu, V., Breakspear, M., & Goecke, R. (2013, August). Modellierung der spektralen Variabilität für die Klassifizierung von depressiver Sprache. In Interspeech (S. 857-861).
- Alghowinem, S., Goecke, R., Wagner, M., Epps, J., Gedeon, T., Breakspear, M., & Parker, G. (2013, Mai). Eine vergleichende Studie verschiedener Klassifikatoren zur Erkennung von Depressionen in spontaner Sprache. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 8022-8026). IEEE.
- Gupta, R., Malandrakis, N., Xiao, B., Guha, T., Van Segbroeck, M., Black, M., ... & Narayanan, S. (2014, November). Multimodale Vorhersage von affektiven Dimensionen und Depression in Mensch-Computer-Interaktionen. In Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge (pp. 33-40). ACM.
- Karam, Z. N., Provost, E. M., Singh, S., Montgomery, J., Archer, C., Harrington, G., & Mcinnis, M. G. (2014, Mai). Ökologisch valide Langzeit-Stimmungsüberwachung von Personen mit bipolarer Störung mittels Sprache. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on (pp. 4858-4862). IEEE.
- Mitra, V., Shriberg, E., McLaren, M., Kathol, A., Richey, C., Vergyri, D., & Graciarena, M. (2014, November). The SRI AVEC-2014 evaluation system. In Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge (pp. 93-101). ACM.
- Sidorov, M., & Minker, W. (2014, November). Emotionserkennung und Depressionsdiagnose durch akustische und visuelle Merkmale: A multimodal approach. In Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge (pp. 81-86). ACM.
- Kaya, H., & Salah, A. A. (2014, November). Eyes whisper depression: A cca based multimodal approach. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 961-964). ACM.
- Hönig, F., Batliner, A., Nöth, E., Schnieder, S., & Krajewski, J. (2014, September). Automatische Modellierung von depressiver Sprache: relevante Merkmale und Relevanz des Geschlechts. In INTERSPEECH (pp. 1248-1252).
- Alghowinem, S., Goecke, R., Wagner, M., Epps, J., Parker, G., & Breakspear, M. (2013). Charakterisierung depressiver Sprache für die Klassifizierung. In Interspeech (pp. 2534-2538).
- Asgari, M., Shafran, I., & Sheeber, L. B. (2014, September). Inferring clinical depression from speech and spoken utterances. In Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on (pp. 1-5). IEEE.
- Lopez-Otero, P., Docio-Fernandez, L., & Garcia-Mateo, C. (2014, Mai). Eine Studie über akustische Merkmale für die Klassifizierung von depressiver Sprache. In Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention on (pp. 1331-1335). IEEE.
- Lopez-Otero, P., Dacia-Fernandez, L., & Garcia-Mateo, C. (2014, März). A study of acoustic features for depression detection. In Biometrics and Forensics (IWBF), 2014 International Workshop on (pp. 1-6). IEEE.
- Nasir, M., Baucom, B. R., Georgiou, P., & Narayanan, S. (2017). Predicting couple therapy outcomes based on speech acoustic features. PloS one, 12(9), e0185123.
- Rao, H., Clements, M. A., Li, Y., Swanson, M. R., Piven, J., & Messinger, D. S. (2017). Paralinguistic Analysis of Children's Speech in Natural Environments. In Mobile Health (pp. 219-238). Springer, Cham.
- Chowdhury, S. A. (2017). Computational modeling of turn-taking dynamics in spoken conversations (Dissertation, Universität Trient).
- Silber-Varod, V., Lerner, A., & Jokisch, O. (2017). Automatic Speaker's Role Classification with a Bottom-up Acoustic Feature Selection. In Proc. GLU 2017 International Workshop on Grounding Language Understanding (pp. 52-56).
- Rehg, J., Abowd, G., Rozga, A., Romero, M., Clements, M., Sclaroff, S., ... & Rao, H. (2013). Decodierung des Sozialverhaltens von Kindern. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3414-3421).
- Wagner, J., Lingenfelser, F., Baur, T., Damian, I., Kistler, F., & André, E. (2013, Oktober). The social signal interpretation (SSI) framework: multimodal signal processing and recognition in real-time. In Proceedings of the 21st ACM international conference on Multimedia (pp. 831-834). ACM.
- Black, M. P., Katsamanis, A., Baucom, B. R., Lee, C. C., Lammert, A. C., Christensen, A., ... & Narayanan, S. S. (2013). Auf dem Weg zur Automatisierung eines Systems zur Kodierung des menschlichen Verhaltens bei der Interaktion von Ehepaaren anhand von akustischen Sprachmerkmalen. Speech Communication, 55(1), 1-21.
- Lee, C. C., Katsamanis, A., Black, M. P., Baucom, B. R., Christensen, A., Georgiou, P. G., & Narayanan, S. S. (2014). Computing vocal entrainment: A signal-derived PCA-based quantification scheme with application to affect analysis in married couple interactions. Computer Speech & Language, 28(2), 518-539.
- Black, M., Georgiou, P. G., Katsamanis, A., Baucom, B. R., & Narayanan, S. S. (2011, August). " You made me do it": Classification of Blame in Married Couples' Interactions by Fusing Automatically Derived Speech and Language Information. In Interspeech (pp. 89-92).
- Lubold, N., & Pon-Barry, H. (2014, November). Acoustic-prosodic entrainment and rapport in collaborative learning dialogues. In Proceedings of the 2014 ACM workshop on Multimodal Learning Analytics Workshop and Grand Challenge (pp. 5-12). ACM.
- Neiberg, D., & Gustafson, J. (2011). Predicting Speaker Changes and Listener Responses with and without Eye-Contact. In INTERSPEECH (pp. 1565-1568).
- Wagner, J., Lingenfelser, F., & André, E. (2013). Verwendung phonetischer Muster zur Erkennung sozialer Hinweise in natürlichen Gesprächen. In INTERSPEECH (pp. 168-172).
- Avril, M., Leclère, C., Viaux, S., Michelet, S., Achard, C., Missonnier, S., ... & Chetouani, M. (2014). Soziale Signalverarbeitung zur Untersuchung der Eltern-Kind-Interaktion. Frontiers in Psychology, 5, 1437.
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