SPEAKER IDENTIFICATION IN EACH OF THE NEUTRAL AND SHOUTED TALKING ENVIRONMENTS BASED ON GENDER-DEPENDENT APPROACH USING SPHMMS

Ismail Shahin

References

  1. [1] S. Furui, Speaker-dependent-feature-extraction, recognition,and processing techniques, Speech Communication, 10, 1991,505–520.
  2. [2] S.E. Bou-Ghazale & J.H.L. Hansen, A comparative studyof traditional and newly proposed features for recognition ofspeech under stress, IEEE Transactions on Speech and AudioProcessing, 8 (4), 2000, 429–442.
  3. [3] G. Zhou, J.H.L. Hansen, & J.F. Kaiser, Nonlinear featurebased classification of speech under stress, IEEE Transactionson Speech and Audio Processing, 9 (3), 2001, 201–216.
  4. [4] Y. Chen, Cepstral domain talker stress compensation for robustspeech recognition, IEEE Transactions on ASSP, 36 (4), 1988,433–439.
  5. [5] I. Shahin, Improving speaker identification performance underthe shouted talking condition using the second-order hiddenMarkov models, EURASIP Journal on Applied Signal Process-ing, 5 (4), 2005, 482–486.
  6. [6] I. Shahin, Enhancing speaker identification performance un-der the shouted talking condition using second-order circularhidden Markov models, Speech Communication, 48 (8), 2006,1047–1055.
  7. [7] I. Shahin, Speaker identification in the shouted environmentusing suprasegmental hidden Markov models, Signal ProcessingJournal, 88 (11), 2008, 2700–2708.
  8. [8] I. Shahin, Employing second-order circular suprasegmentalhidden Markov models to enhance speaker identification per-formance in shouted talking environments, EURASIP Journalon Audio, Speech, and Music Processing, 2010, Article ID862138, 10 pages, doi:10.1155/2010/862138.
  9. [9] I. Shahin, Gender-dependent speaker identification undershouted talking condition, 3rd International Conference onCommunication, Computer and Power (ICCCP’09), Muscat,Oman, 2009, 332–336.
  10. [10] J. Adell, A. Benafonte, & D. Escudero, Analysis of prosodicfeatures: Towards modelling of emotional and pragmatic at-tributes of speech, XXI Congreso de la Sociedad Espa˜nolapara el Procesamiento del Lenguaje Natural, SEPLN, Granada,Spain, 2005.
  11. [11] T.S. Polzin & A.H. Waibel, Detecting emotions in speech,Cooperative Multimodal Communication, Second InternationalConference 1998, CMC, Tilburg, The Netherlands, 1998.
  12. [12] L.R. Rabiner & B.H. Juang, Fundamentals of speech recognition(Englewood Cliffs, NJ: Prentice Hall, 1993).
  13. [13] J.H.L. Hansen & S. Bou-Ghazale, Getting started withSUSAS: A speech under simulated and actual stress database,EUROSPEECH-97: International Conf. on Speech Commu-nication and Technology, Rhodes, Greece, 1997, 1743–1746.
  14. [14] http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC99S78.
  15. [15] H. Bao, M. Xu, & T.F. Zheng, Emotion attribute projectionfor speaker recognition on emotional speech, INTERSPEECH2007, Antwerp, Belgium, 2007, 758–761.
  16. [16] T. Vogt & E. Andre, Improving automatic emotion recognitionfrom speech via gender differentiation, Proceedings of LanguageResources and Evaluation Conference (LREC 2006), Genoa,Italy, 2006.
  17. [17] J. Dai, Isolated word recognition using Markov chain models,IEEE Transactions on Speech and Audio Processing Journal,3 (6), 1995, 458–463.
  18. [18] O.W. Kwon, K. Chan, J. Hao, & T.W. Lee, Emotion recog-nition by speech signals, 8th European Conference on SpeechCommunication and Technology 2003, Geneva, Switzerland,2003, 125–128.
  19. [19] I. Luengo, E. Navas, I. Hernaez, & J. Sanches, Automatic emo-tion recognition using prosodic parameters, INTERSPEECH2005, Lisbon, Portugal, 2005, 493–496.
  20. [20] T.H. Falk & W.Y. Chan, Modulation spectral features forrobust far-field speaker identification, IEEE Transactions onAudio, Speech and Language Processing, 18 (1), 2010, 90–100.
  21. [21] J.H.L. Hansen & B.D. Womack, Feature analysis and neu-ral network-based classification of speech under stress, IEEETransactions on Speech and Audio Processing Journal, 4 (4),1996, 307–313.
  22. [22] T. Kinnunen & H. Li, An overview of text-independent speakerrecognition: From features to supervectors, Speech Communi-cation, 52 (1), 2010, 12–40.
  23. [23] T.L. Nwe, S.W. Foo, & L.C. De Silva, Speech emotion recog-nition using hidden Markov models, Speech Communication,41 (4), 2003, 603–623.
  24. [24] D. Ververidis & C. Kotropoulos, Emotional speech recognition:Resources, features, and methods, Speech Communication,48 (9), 2006, 1162–1181.
  25. [25] L.T. Bosch, Emotions, speech and the ASR framework, SpeechCommunication, 40 (1–2), 2003, 213–225.90
  26. [26] W.H. Abdulla & N.K. Kasabov, Improving speech recognitionperformance through gender separation, Artificial Neural Net-works and Expert Systems International Conference (ANNES),Dunedin, New Zealand, 2001, 218–222.
  27. [27] H. Harb & L. Chen, Gender identification using a generalaudio classifier, International Conference on Multimedia andExpo 2003 (ICME ’03), 2003, II733–II736.

Important Links:

Go Back