Recognition of nonprototypical emotions in reverberated and noisy speech by nonnegative matrix factorization

Eurasip Journal on Advances in Signal Processing

Volumn 2011, Issue , 2011, Pages

  Weninger, Felix ^a   Schuller, Björn ^a   Batliner, Anton ^b   Steidl, Stefan ^b   Seppi, Dino ^c  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^c UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION SCENARIO; AUTOMATIC SPEECH RECOGNITION; BACKGROUND NOISE; COMPREHENSIVE STUDIES; EMOTION RECOGNITION; NOISY SPEECH; NONNEGATIVE MATRIX FACTORIZATION; TASK-BASED; TEST SCENARIO;

FACTORIZATION; FEATURE EXTRACTION; MATRIX ALGEBRA; REVERBERATION;

SPEECH RECOGNITION;

EID: 79955034745     PISSN: 16876172     EISSN: 16876180     Source Type: Journal    
DOI: 10.1155/2011/838790     Document Type: Article

References (51)

1. Schuller B., Wllmer M., Moosmayr T., Rigoll G., Recognition of noisy speech: a comparative survey of robust model architecture and feature enhancement EURASIP Journal on Audio, Speech, and Music Processing 2009 2009
2. Kim E.-H., Hyun K.-H., Kwak Y.-K., Robustemotion recognition feature, frequency range of meaningful signal Procceings of the IEEE International Workshopon Robots and Human Interactive Communication(RO-MAN '05) 2005 Nashville, Tenn, USA
3. Tawari A., Trivedi M., Speech emotion analysis in noisy real-world environment Proceedings of the International Conference on Pattern Recognition (ICPR '10) August 2010 Istanbul, Turkey 4605 4608
4. Lee K.-K., Cho Y.-H., Park K.-S., Robust feature extraction for mobile-based speech emotion recognition system Intelligent Computing in Signal Processing and Pattern Recognition 2006 Berlin, Germany Springer 470 477 Lecture Notes in Control and Information Sciences (Pubitemid 44421600)
5. Yoon W.-J., Cho Y.-H., Park K.-S., A study of speech emotion recognition and its application tomobile services Ubiquitous Intelligence and Computing 2007 Berlin, Germany Springer 758 766 Lecture Notes In Computer Science
6. You M., Chen C., Bu J., Liu J., Tao J., Manifolds based emotion recognition in speech Computational Linguistics and Chinese Language Processing 2007 12 1 49 64
7. Grimm M., Kroschel K., Harris H., Paiva A., Prada R., Picard R. W., On the necessity and feasibility of detecting a driver's emotional state while driving Affective Computing and Intelligent Interaction 2007 Berlin, Germany Springer 126 138
8. Lugger M., Yang B., Wokurek W., Robust estimation of voice quality parameters under real world disturbances 1 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '06) 2006 Toulouse, France 1097 1100
9. You M., Chen C., Bu J., Liu J., Tao J., Emotion recognition from noisy speech Procceings of the IEEE International Conference on Multimedia and Expo (ICME '06) July 2006 Toronto, Canada 1653 1656 (Pubitemid 46680050)
10. Schuller B., Arsi D., Wallhoff F., Rigoll G., Emotion recognition in the noise applying large acoustic feature sets Proceedings of the Speech Prosody 2006 Dresden,Germany
11. Schuller B., Seppi D., Batliner A., Maier A., Steidl S., Towards more reality in the recognition of emotional speech 4 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '07) April 2007 Honolulu, Hawaii, USA 941 944
12. Schuller B., Steidl S., Batliner A., The INTERSPEECH 2009 emotion challenge Proceedings of the INTERSPEECH of Conference of the International Speech Communication Association (ISCA '09) September 2009 Brighton, UK 312 315
13. Pearce D., Hirsch H.-G., The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy Proceedings of the International Conference on Spoken Language Processing (ICSLP '00) October 2000 Beijing, China
14. Rennie S. J., Hershey J. R., Olsen P. A., Efficient model-based speech separation and denoising using non-negative subspace analysis Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '08) 2008 Las Vegas, Nev, USA 1833 1836
15. Wilson K. W., Raj B., Smaragdis P., Regularized non-negative matrix factorization with temporal dependencies for speech denoising Proceedings of the INTERSPEECH 2008 Brisbane, Australia
16. Wilson K. W., Raj B., Smaragdis P., Divakaran A., Speech denoising using nonnegative matrix factorization with priors Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '08) April 2008 Las Vegas, Nev, USA 4029 4032
17. Schmidt M. N., Olsson R. K., Single-channel speech separation using sparse non-negative matrix factorization Proceedings of the INTERSPEECH of the 9th International Conference on Spoken Language Processing (ICSLP '06) September 2006 2614 2617
18. Virtanen T., Cemgil A. T., Mixtures of gamma priorsfor non-negative matrix factorization based speech separation Proceedings of the International Conference on Independent Component Analysis and Signal Separation (ICA '09) 2009 Paraty, Brazil 646 653
19. Virtanen T., Spectral covariance in prior distributions of non-negative matrix factorization based speech separation Proceedings of the European Signal Processing Conference (EUSIPCO '09) 2009 Glasgow, Scotland
20. Schuller B., Weninger F., Wllmer M., Sun Y., Rigoll G., Non-negative matrix factorization as noiserobust feature extractor for speech recognition Proceedings of the International Conference on Acoustics, Speech and Signal (ICASSP '10) March 2010 Dallas, Tex, USA 4562 4565
21. Gemmeke J. F., Virtanen T., Noise robust exemplar-based connected digit recognition Proceedings of the International Conference on Acoustics, Speech and Signal (ICASSP '10) March 2010 Dallas, Tex, USA
22. Cho Y.-C., Choi S., Bang S.-Y., Non-negative component parts of sound for classification Proceedings of the International Symposium on Signal Processing and Information Technology (ISSPIT '03) 2003 Darmstadt, Germany 633 636
23. Schuller B., Weninger F., Discrimination of speech and non-linguistic vocalizations by non-negative matrix factorization Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '10) March 2010 Dallas, Tex, USA 5054 5057
24. Jeong K., Song J., Jeong H., NMF features for speech emotion recognition Proceedings of the International Conference on Hybrid Information Technology (ICHIT '09) 2009 New York, NY, USA ACM 368 374
25. Kim D., Lee S.-Y., Amari S.-I., Representative and discriminant feature extraction based on NMF foremotion recognition in speech Proceedings of the of the 16th International Conference on Neural Information Processing (ICONIP '09) 2009 Berlin, Germany Springer 649 656
26. Eggert J., Krner E., Sparse coding and NMF 4 Proceedings of the Neural Networks 2004 Dalian, China 2529 2533 (Pubitemid 40011434)
27. Lee D. D., Seung H. S., Learning the parts of objects by non-negative matrix factorization Nature 1999 401 6755 788 791
28. ten Bosch L., Driesen J., van hamme H., Boves L., Matouek V., Mautner P., On a computational model for language acquisition:Modeling cross-speaker generalisation Text, Speech and Dialogue 2009 5729 Berlin, Germany Springer 315 322 Lecture Notes in Computer Science
29. Schuller B., Lehmann A., Weninger F., Eyben F., Rigoll G., Blind enhancement of the rhythmic and harmonic sections by NMF: does it help? Proceedings of the International Conference on Acoustics (NAG/DAGA '09) 2009 Rotterdam, The Netherlands DEGA 361 364
30. Virtanen T., Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria IEEE Transactions on Audio, Speech and Language Processing 2007 15 3 1066 1074
31. Lee D. D., Seung H. S., Algorithms for nonnegative matrix factorization Proceedings of the Neural Information Processing Systems (NIPS '01) 2001 Vancouver,Canada 556 562
32. Wang W., Cichocki A., Chambers J. A., A multiplicative algorithm for convolutive non-negative matrix factorization based on squared euclidean distance IEEE Transactions on Signal Processing 2009 57 7 2858 2864
33. Smaragdis P., Discovering auditory objects through non-negativity constraints Proceedings of the Workshop on Statistical and Perceptual Audition (SAPA '04) 2004 Jeju, Republic of Korea
34. Smaragdis P., Convolutive speech bases and their application to supervised speech separation IEEE Transactions on Audio, Speech and Language Processing 2007 15 1 1 12
35. O'Grady P. D., Pearlmutter B. A., Discovering convolutive speech phones using sparseness and non-negativity Proceedings of the International Workshop on Independent Component Analysis (ICA '07) 2007 London, UK
36. Vlasenko B., Schuller B., Wendemuth A., Rigoll G., Paiva A., Prada R., Picard R. W., Frame vs. turn-level: emotion recognition from speech considering static and dynamic processing Affective Computing and Intelligent Interaction 2007 Berlin, Germany Springer 139 147
37. Schuller B., Eyben F., Rigoll G., Static and dynamic modelling for the recognition of non-verbal vocalisations in conversational speech Proceedings of the 4th IEEE Tutorial and Research Workshop on Perception and Interactive Technologies for Speech-Based Systems: Perception in Multimodal Dialogue Systems (PIT '08) 2008 Berlin, Germany Springer 99 110
38. Eyben F., Wllmer M., Schuller B., OpenEARintroducing the Munich open-source emotion and affect recognition toolkit Proceedings of the 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (ACII '09) September 2009 Amsterdam, The Netherlands
39. Eyben F., Wllmer M., Schuller B., OpenSMILEthe Munich versatile and fast open-source audio feature extractor Proceedings of the ACM International Conference on Multimedia (MM '10) October 2010 Florence, Italy ACM 1459 1462
40. Schuller B., Steidl S., Batliner A., The INTERSPEECH 2010 paralinguistic challenge Proceedings of the INTERSPEECH of Conference of the International Speech Communication Association (ISCA '10) September 2010 Makuhari, Japan 2794 2797
41. Kim H.-G., Burred J. J., Sikora T., How efficientis MPEG-7 for general sound recognition? Proceedings of the International Conference Convention of the Audio Engineering Society (AES '04) June 2004 London, UK
42. Steidl S., Automatic classification of emotion-related user states in spontaneous children's speech, Ph.D. thesis 2009 Berlin, Germany Logos. FAU Erlangen-Nuremberg
43. Batliner A., Kompe R., Kieling A., Mast M., Niemann H., Nth E., M = Syntax + Prosody: a syntactic-prosodic labelling scheme for large spontaneous speech databases Speech Communication 1998 25 4 193 222 (Pubitemid 128429910)
44. Steidl S., Schuller B., Batliner A., Seppi D., The hinterland of emotions: facing the open-microphone challenge Proceedings of the 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (ACII '09) September 2009 Amsterdam, The Netherlands IEEE 690 697
45. Schuller B., Batliner A., Steidl S., Seppi D., Recognising realistic emotions and affect in speech: state of the art and lessons learnt from the first challenge. to appear in Speech Communication, Special Issue on Sensing Emotion and AffectFacing Realism in Speech Processing
46. Sethu V., Ambikairajah E., Epps J., Speaker dependency of spectral features and speech production cues for automatic emotion classification Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '07) 2009 Taipei, Taiwan 4693 4696
47. Maier A., Hacker C., Steidl S., Nth E., Niemann H., Kropatsch W. G., Sablatnig R., Hanbury A., Robust parallel speech recognition in multiple energy bands Proceedings of the Annual Symposium of the German Association for Pattern Recognition (DAGM '05) August 2005 Vienna, Austria 133 140 Lecture Notes in Computer Science (Pubitemid 41520647)
48. ITU-T Recommendation P.56: Objective Measurementof Active Speech Level 1993 Geneva, Switzerland International Telecommunications Union (ITU)
49. Platt J. C., Fast training of support vector machines using sequential minimal optimization Advancesin Kernel Methods: Support Vector Learning 1999 Cambridge, Mass, USA MIT Press 185 208
50. Chawla N. V., Bowyer K. W., Hall L. O., Kegelmeyer W. P., SMOTE: synthetic minority over-sampling technique Journal of Artificial Intelligence Research 2002 16 321 357 (Pubitemid 43057176)
51. Witten I. H., Frank E., Data mining: Practical machine Learning Tools and Techniques 2005 2nd San Francisco, Calif, USA Morgan Kauffmann