Speaker-Characterized Emotion Recognition using Online and Iterative Speaker Adaptation

Cognitive Computation

Volumn 4, Issue 4, 2012, Pages 398-408

  Kim, Jae Bok ^a   Park, Jeong Sik ^b   Oh, Yung Hwan ^c  

^a LG Electronics Inc   (South Korea)

^b Mokwon University   (South Korea)

^c Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

Human machine interaction; Maximum likelihood linear regression (MLLR); Speaker adaptation; Speaker characterized emotion model; Speech emotion recognition

Indexed keywords

EMOTION MODELS; HUMAN MACHINE INTERACTION; MAXIMUM LIKELIHOOD LINEAR REGRESSION; SPEAKER ADAPTATION; SPEECH EMOTION RECOGNITION;

SPEECH RECOGNITION;

ITERATIVE METHODS;

EID: 84870448053     PISSN: 18669956     EISSN: 18669964     Source Type: Journal    
DOI: 10.1007/s12559-012-9132-9     Document Type: Article

References (21)

1. Suprateek S, John D. Understanding mobile handheld device use and adoption. Commu ACM. 2003; 46: 35-40.
2. Ballagas R, Borchers J, Rohs M, Jennifer G. The smart phone: ubiquitous input device. IEEE Pervasive Comput. 2006; 5: 70-7.
3. Mark A, Streefkerk J. Interacting in desktop and mobile context: emotion, trust, and task performance. Ambient Intell. 2003; 2875: 119-32.
4. Pittermann J, Pittermann A, Minker W. Handing emotions in human-computer dialogues. Berlin: Springer; 2010. p. 19-42.
5. Park JS, Kim JH, Oh YH. Feature vector classification based speech emotion recognition for service robots. IEEE Trans Consum Electron. 2009; 55: 1590-6.
6. Ignacio LM, Carlos OR, Joaquin GR, Daniel R. Speaker dependent emotion recognition using prosodic supervectors. In: Proceedings of interspeech; 2009. pp. 1971-4.
7. Nwe TL, Foo SW, Silva LCD. Speech emotion recognition using hidden Markov models. Speech Commun. 2003; 41: 603-23.
8. Ververidis D, Kotropoulos C. Emotional speech recognition: resources, features, and methods. Speech Commun. 2006; 48: 1162-81.
9. Kwon O, Chan K, Hao J, Lee T. Emotion recognition by speech signals. In: Proceedings of Eurospeech; 2003. pp. 125-8.
10. Tato R, Santos R, Kompe R, Pardo JM. Emotional space improves emotion recognition. In: Proceedings of the international conference on spoken language processing (ICSLP); 2002. pp. 2029-32.
11. Huang R, Ma C. Toward a speaker-independent real time affect detection system. In: Proceedings of international conference on pattern recognition (ICPR); 2006. pp. 1204-7.
12. Leggetter CJ, Woodland PC. Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Comput Speech Lang. 1995; 9: 171-85.
13. Woodland PC, Pye D, Gales MJF. Iterative unsupervised adaptation using maximum likelihood linear regression. In: Proceedings of international conference on spoken language processing (ICSLP); 1996. pp. 1133-6.
14. Lee CH, Lin CH, Juang BH. A study on speaker adaptation of the parameters of continuous density hidden markov models. IEEE Trans Signal Process. 1991; 39: 806-14.
15. Matsui T, Furui S. N-best-based unsupervised speaker adaptation for speech recognition. Comput Speech Lang. 1998; 12: 41-50.
16. Anastasakos T, Balakrishnan SV. The use of confidence measures in unsupervised adaptation of speech recognizers. In: Proceedings of international conference on spoken language processing (ICSLP); 1998. pp. 2303-6.
17. Grimm M, Kroschel K, Mower E, Narayanan S. Primitives-based evaluation and estimation of emotions in speech. Speech Commun. 2007; 49: 787-800.
18. Jiang H. Confidence measures for speech recognition: a survey. Speech Commun. 2005; 45: 455-70.
19. Pitz M, Wessel F, Ney H. Improved MLLR speaker adaptation using confidence measures for conversational speech recognition. In: Proceedings of international conference on spoken language processing (ICSLP); 2000. pp. 548-51.
20. Gollan C, Bacchiani M. Confidence scores for acoustic model adaptation. In: Proceedings of international conference on acoustics, speech, and signal processing (ICASSP); 2008, pp. 4289-92.
21. Liberman M, Davis K, Grossman M, Martey N, Bell J. Emotional prosody speech and transcripts. In: Linguistic data consortium (LDC). Philadelphia: University of Pennsylvania; 2002.