Publications
1105 results found
Geiger JT, Eyben F, Schuller B, et al., 2013, Detecting Overlapping Speech with Long Short-Term Memory Recurrent Neural Networks, Publisher: ISCA, Pages: 1668-1672
, 2013, Proceedings of the 6th International Symposium on Attention in Cognitive Systems 2013, ISACS 2013, Publisher: arxiv.org, Springer
Han W, Li H, Ruan H, et al., 2013, Active Learning for Dimensional Speech Emotion Recognition, Publisher: ISCA, Pages: 2856-2859
Zhang Z, Deng J, Marchi E, et al., 2013, Active Learning by Label Uncertainty for Acoustic Emotion Recognition, Publisher: ISCA, Pages: 2841-2845
Cambria E, Schuller B, Xia Y, et al., 2013, New Avenues in Opinion Mining and Sentiment Analysis, IEEE Intelligent Systems Magazine, Vol: 28, Pages: 15-21
Cambria E, Schuller B, Liu B, et al., 2013, Knowledge-Based Approaches to Concept-Level Sentiment Analysis INTRODUCTION, IEEE INTELLIGENT SYSTEMS, Vol: 28, Pages: 12-14, ISSN: 1541-1672
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Eyben F, Weninger F, Marchi E, et al., 2013, Likability of human voices: A feature analysis and a neural network regression approach to automatic likability estimation, Publisher: IEEE
Weninger F, Geiger J, Wöllmer M, et al., 2013, The Munich Feature Enhancement Approach to the 2013 CHiME Challenge Using BLSTM Recurrent Neural Networks, Publisher: IEEE, Pages: 86-90
Geiger JT, Weninger F, Hurmalainen A, et al., 2013, The TUM+TUT+KUL Approach to the CHiME Challenge 2013: Multi-Stream ASR Exploiting BLSTM Networks and Sparse NMF, Publisher: IEEE, Pages: 25-30
Dunwell I, Lameras P, Stewart C, et al., 2013, Developing a Digital Game to Support Cultural Learning amongst Immigrants, Publisher: SASDG
Weninger F, Kirst C, Schuller B, et al., 2013, A Discriminative Approach to Polyphonic Piano Note Transcription using Non-negative Matrix Factorization, Publisher: IEEE, Pages: 6-10
Wöllmer M, Zhang Z, Weninger F, et al., 2013, Feature Enhancement by Bidirectional LSTM Networks for Conversational Speech Recognition in Highly Non-Stationary Noise, Publisher: IEEE, Pages: 6822-6826
Wöllmer M, Schuller B, Rigoll G, 2013, Probabilistic ASR Feature Extraction Applying Context-Sensitive Connectionist Temporal Classification Networks, Pages: 7125-7129
Newman S, Golan O, Baron-Cohen S, et al., 2013, ASC-Inclusion – Interactive Software to Help Children with ASC Understand and Express Emotions, Publisher: INSAR
Joder C, Weninger F, Virette D, et al., 2013, Integrating Noise Estimation and Factorization-based Speech Separation: a Novel Hybrid Approach, Publisher: IEEE, Pages: 131-135
Joder C, Weninger F, Virette D, et al., 2013, A Comparative Study on Sparsity Penalties for NMF-based Speech Separation: Beyond LP-Norms, Publisher: IEEE, Pages: 858-862
Joder C, Schuller B, 2013, Off-line Refinement of Audio-to-Score Alignment by Observation Template Adaptation, Publisher: IEEE, Pages: 206-210
Geiger JT, Hofmann M, Schuller B, et al., 2013, Gait-based Person Identification by Spectral, Cepstral and Energy-related Audio Features, Publisher: IEEE, Pages: 458-462
Zhang Z, Deng J, Schuller B, 2013, Co-Training Succeeds in Computational Paralinguistics, Publisher: IEEE, Pages: 8505-8509
Weninger F, Wagner C, Wöllmer M, et al., 2013, Speaker Trait Characterization in Web Videos: Uniting Speech, Language, and Facial Features, Publisher: IEEE, Pages: 3647-3651
Schuller B, Pokorny F, Ladstätter S, et al., 2013, Acoustic Geo-Sensing: Recognising Cyclists’ Route, Route Direction, and Route Progress from Cell-Phone Audio, Publisher: IEEE, Pages: 453-457
Schuller B, Paletta L, Sabouret N, 2013, Intelligent Digital Games for Empowerment and Inclusion – An Introduction, Publisher: SASDG
Schuller B, Marchi E, Baron-Cohen S, et al., 2013, ASC-Inclusion: Interactive Emotion Games for Social Inclusion of Children with Autism Spectrum Conditions, Publisher: SASDG
Schuller B, Friedmann F, Eyben F, 2013, Automatic Recognition of Physiological Parameters in the Human Voice: Heart Rate and Skin Conductance, Publisher: IEEE, Pages: 7219-7223
Wöllmer M, Weninger F, Geiger J, et al., 2013, Noise Robust ASR in Reverberated Multisource Environments Applying Convolutive NMF and Long Short-Term Memory, Computer Speech and Language, Special Issue on Speech Separation and Recognition in Multisource Environments, Vol: 27, Pages: 780-797
Weninger F, Eyben F, Schuller BW, et al., 2013, On the Acoustics of Emotion in Audio: What Speech, Music and Sound have in Common, Frontiers in Psychology, Emotion Science, Special Issue on Expression of emotion in music and vocal communication, Vol: 4, Pages: 1-12
Schuller B, 2013, Prosody and Phonemes: On the Influence of Speaking Style, Prosody and Iconicity, Editors: Hancil, Hirst, Publisher: Benjamins, Pages: 233-250
Eyben F, Weninger F, Squartini S, et al., 2013, Real-life Voice Activity Detection with LSTM Recurrent Neural Networks and an Application to Hollywood Movies, Publisher: IEEE, Pages: 483-487
Weninger F, Eyben F, Schuller BW, et al., 2013, On the Acoustics of Emotion in Audio: What Speech, Music and Sound have in Common, Frontiers in Psychology, Emotion Science, Special Issue on Expression of emotion in music and vocal communication, Vol: 4, Pages: 1-12
Weninger F, Staudt P, Schuller B, 2013, Words that fascinate the listener: Predicting affective ratings of on-line lectures, International Journal of Distance Education Technologies, Vol: 11, Pages: 110-123, ISSN: 1539-3100
In a large scale study on 843 transcripts of Technology, Entertainment and Design (TED) talks, the authors address the relation between word usage and categorical affective ratings of lectures by a large group of internet users. Users rated the lectures by assigning one or more predefined tags which relate to the affective state evoked in the audience (e. g., 'fascinating', 'funny', 'courageous', 'unconvincing' or 'long-winded'). By automatic classification experiments, they demonstrate the usefulness of linguistic features for predicting these subjective ratings. Extensive test runs are conducted to assess the influence of the classifier and feature selection, and individual linguistic features are evaluated with respect to their discriminative power. In the result, classification whether the frequency of a given tag is higher than on average can be performed most robustly for tags associated with positive valence, reaching up to 80.7% accuracy on unseen test data. Copyright © 2013, IGI Global.
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