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Text Independent Methods for Speech Segmentation

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Nonlinear Speech Modeling and Applications (NN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3445))

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Abstract

This paper describes several text independent speech segmentation methods. State-of-the-art applications and the prospected use of automatic speech segmentation techniques are presented, including the direct applicability of automatic segmentation in recognition, coding and speech corpora annotation, which is a central issue in today’s speech technology. Moreover, a novel parametric segmentation algorithm will be presented and performance will be evaluated by comparing its effectiveness against other text independent speech segmentation methods proposed in literature.

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References

  1. Altosaar, T., Karjalainen, M.: Event-Based Multiple Resolution Analysis of Speech Signals. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, New-York, pp. 327–330 (1988)

    Google Scholar 

  2. Andre-Obrecht, R.: A New Statistical Approach for the Automatic Segmentation of Continuous Speech Signals. IEEE Transactions on Acoustics, Speech Signal Processing 36, 29–40 (1988)

    Article  Google Scholar 

  3. Atal, B.S.: Efficient Coding of LPC Parameters by Temporal Decomposition. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, pp. 81–84 (1983)

    Google Scholar 

  4. Aversano, G.: Phone Level Automatic Speech Segmentation. A Text-Independent Segmentation Algorithm and a Software Tool for Speech Annotation and Analysis. Ph.D. Thesis, Università di Salerno. Italy (2004)

    Google Scholar 

  5. Aversano, G., Esposito, A.: Automatic Parameter Estimation for a Context-Independent Speech Segmentation Algorithm. In: Sojka, P., Kopecek, I., Pala, K. (eds.) Text Speech and Dialogue, 5th International Conference. LNCS (LNAI), pp. 293–300. Springer, Heidelberg (2002)

    Google Scholar 

  6. Aversano, G., Esposito, A., Esposito, A., Marinaro, M.: A New Text-Independent Method for Phoneme Segmentation. In: Ewing, R.L., et al. (eds.) Proceedings of the IEEE International Workshop on Circuits and Systems, vol. 2, pp. 516–519 (2001)

    Google Scholar 

  7. Backfried, G., Rainoldi, R., Riedler, J.: Automatic Language Identification in Broadcast News. In: Proceedings of International Joint Conference on Neural Networks, vol. 2, pp. 1406–1410 (2002)

    Google Scholar 

  8. Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes: Theory and Applications. Prentice-Hall, Englewood Cliffs (1993)

    Google Scholar 

  9. Basseville, M.: Distance Measures for Signal Processing and Pattern Recognition. Signal Processing 18, 349–369 (1989)

    Article  MathSciNet  Google Scholar 

  10. Baudoin, G., Capman, F., Cernocky, J., El Chami, F., Charbit, M., Chollet, G., Petrovska- Delacretaz, D.: Advances in Very Low Bit-rate Speech Coding using Recognition and Synthesis. In: Sojka, P., Kopecek, I., Pala, K. (eds.) Text Speech and Dialogue, 5th International Conference. LNCS (LNAI), pp. 269–276. Springer, Heidelberg (2002)

    Google Scholar 

  11. Beringer, N., Neff, M.: Regional Pronunciation Variants for Automatic Segmentation. In: Proceedings of the 2nd International Conference on Language Resources and Evaluation, Athens, Greece (2000)

    Google Scholar 

  12. Beringer, N., Schiel, F.: The Quality of Multilingual Automatic Segmentation Using German MAUS. In: Proceedings of the 6th Int. Conference on Spoken Language Processing, Beijing, China, pp. 728–731 (2000)

    Google Scholar 

  13. Beringer, N., Schiel, F.: Independent Automatic Segmentation of Speech by Pronunciation Modeling. In: Proceedings of the 14th Int. Congress of Phonetic Sciences, San Francisco, pp. 1653–1656 (1999)

    Google Scholar 

  14. Beulen, K., Ney, H.: Automatic Question Generation for Decision Tree Based State Tying. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 805–808 (1998)

    Google Scholar 

  15. Beulen, K., Bransch, E., Ney, H.: State Tying for Context Dependent Phoneme Models. In: Proceedings of European Conference on Speech Communication and Technology, pp. 1179–1182 (1997)

    Google Scholar 

  16. Binnenpoorte, D., Goddijn, S., Cucchiarini, C.: How to Improve Human and Machine Transcriptions of Spontaneous Speech. In: ISCA/IEEE Workshop on Spontaneous Speech Processing and Recognition, Tokyo, pp. 147–150 (2003)

    Google Scholar 

  17. Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)

    Google Scholar 

  18. Brugnara, F., Falavigna, D., Omologo, M.: Automatic Segmentation and Labeling of Speech Based on Hidden Markov Models. Speech Communication 12, 357–370 (1993)

    Article  Google Scholar 

  19. Brugnara, F., De Mori, A., Giuliani, D., Omologo, M.: Improved Connected Digit Recognition Using Spectral Variation Functions. In: Proceedings of International Conference on Spoken Language Processing, pp. 627–630 (1992)

    Google Scholar 

  20. Chang, S., Shastri, L., Greenberg, S.: Automatic Phonetic Transcription of Spontaneous Speech (American English). In: Proceedings of the 6th International Conference on Spoken Language Processing, Beijing, China, pp. 330–333 (2000)

    Google Scholar 

  21. Church, K.W.: Speech and Language Processing: Where Have We Been and Where Are We Going? In: Proceedings of the 8th European Conference on Speech Communication and Technology - Eurospeech 2003, Geneva, Switzerland, pp. 1–4 (2003)

    Google Scholar 

  22. Daoudi, K., Fohr, D., Antoine, C.: Continuous Multi-Band Speech Recognition using Bayesian Networks. In: Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop, Trento, Italy (2001)

    Google Scholar 

  23. Deshayes, J., Picard, D.: Off-line Statistical Analysis in Change-point Models Using Nonparametric and Likelihood Methods. In: Basseville, M., Beneviste, A. (eds.) Detection of Abrupt Changes in Signals and Dynamical Systems, Springer, New-York (1986)

    Google Scholar 

  24. du Jeu, C., Charbit, M., Chollet, G.: Very-low-rate Speech Compression by Indexation of Polyphones. In: Proceedings of the 8th European Conference on Speech Communication and Technology - Eurospeech 2003, Geneva, Switzerland, pp. 1085–1088 (2003)

    Google Scholar 

  25. Eisen, B., Tillman, H.G.: Consistency of Judgments in Manual Labeling of Phonetic Segments: The Distinction between Clear and Nnclear Cases. In: Proceedings of ICSLP, Banf., Canada, pp. 871–874 (1992)

    Google Scholar 

  26. Eisen, B.: Reliability of Speech Segmentation and Labeling at Different Levels of Transcription. In: Proceedings of the 3rd European Conference on Speech Communication and Technology, Eurospeech 1993, Berlin, Germany, pp. 673–676 (1991)

    Google Scholar 

  27. Esposito, A.: The Importance of Data for Training Intelligent Devices. In: Apolloni, B., Kurfess, F. (eds.) From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data, pp. 229–250. Kluwer Academic/Plenum Publishers (2002)

    Google Scholar 

  28. Esposito, A., Pannacci, L., Perfetti, R., Russo, R.C.: Speech Segmentation by Parametric Filtering: Two New Distortion Measures and Experimental Evaluation, Technical Report n. IIASS-1 2000, International Institute for Advanced Scientific Studies, Vietri sul Mare (SA), Italy (2000)

    Google Scholar 

  29. Fairbanks, G., Everitt, W., Jaeger, R.: Method for Time or Frequency Compression Expansion of Speech. IEEE Transactions on Audio and Electro-acoustics AU-2, 7–12 (1954)

    Google Scholar 

  30. Faundez-Zanuy, M., Vallverdù-Bayes, F.: Speech Segmentation Using Multilevel Hybrid Filters. In: Proceedings of European Signal Processing Conference (EUSIPCO), pp. 1003–1006 (1996)

    Google Scholar 

  31. Finster, H.: Automatic speech segmentation using neural network and phonetic transcription. In: Proceedings of International Conference on Neural Networks, vol. 4, pp. 734–736 (1992)

    Google Scholar 

  32. Flammia, G., Dalsgaard, P., Andersen, O., Lindberg, B.: Segment Based Variable Frame Rate Speech Analysis and Recognition Using Spectral Variation Function. In: Proceedings of International Conference on Spoken Language Processing, pp. 983–986 (1992)

    Google Scholar 

  33. Furuichi, C., Aizawa, K., Inoue, K.: Speech Recognition Using Stochastic Phonemic Segment Model Based on Phoneme Segmentation. Systems and Computers in Japan 31(10), 1111–1119 (2000)

    Article  Google Scholar 

  34. Garofolo, J. S., Lamel, L. F., Fisher, W. M., Fiscus, J. G., Pallett, D. S., Dahlgren, N. L.: The DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus. CDROM (1992); NTIS order number PB91-100354

    Google Scholar 

  35. Gemello, R., Albesano, D., Mana, F.: CSELT Hybrid HMM/Neural Networks Technology for Continuous Speech Recognition. In: Proceedings of IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 5, pp. 103–108 (2000)

    Google Scholar 

  36. Glass, J.R.: A Probabilistic Framework for Segment -Based Speech Recognition. Computer Speech and Language 17, 137–152 (2003)

    Article  Google Scholar 

  37. Glass, J.R., Zue, V.W.: Multilevel Acoustic Segmentation of Continuous Speech. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, pp. 429–432 (1988)

    Google Scholar 

  38. Gómez, J.A., Castro, M.J.: Automatic Segmentation of Speech at the Phonetic Level. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 672–680. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  39. Gray, R.M., Buzo, A., Gray, A., Matsuyama, Y.: Distortion Measures for Speech Processing. IEEE Transactions on Acoustics, Speech Signal Processing 28, 367–376 (1980)

    Article  MATH  Google Scholar 

  40. Green, D., Swets, J.: Signal Detection Theory and Psychophysics. John Wiley and Sons, Chichester (1996)

    Google Scholar 

  41. Greenberg, S.: Strategies for Automatic Multi-Tier Annotation of Spoken Language Corpora. In: Proceedings of the 8th European Conference on Speech Communication and Technology - Eurospeech 2003, Geneva, Switzerland, pp. 45–48 (2003)

    Google Scholar 

  42. Greenberg, S.: The Switchboard Transcription Project. Technical Report # 24, Center for Language and Speech Processing, Johns Hopkins University, Baltimore USA (1997)

    Google Scholar 

  43. Hermansky, H.: Analysis in Automatic Recognition of Speech. In: Chollet, G., Di Benedetto, M., Esposito, A., Marinaro, M. (eds.) Speech Processing, Recognition and Artificial Neural Networks, 3rd International School on Neural Nets ”Eduardo R. Caianiello”, pp. 115–137. Springer, Heidelberg (1999)

    Google Scholar 

  44. Hermansky, H.: Auditory Modeling in Automatic Recognition of Speech. In: Proceedings of the ESCA Workshop on the Auditory Basis of Speech Perception, Keele, Sweden (1996)

    Google Scholar 

  45. Hermansky, H., Morgan, N.: RASTA Processing of Speech. IEEE Transactions. Speech and Audio Processing 2(4), 578–589 (1994)

    Article  Google Scholar 

  46. Hermansky, H.: Perceptual Linear Predictive (PLP) Analysis of Speech. Journal of Acoustical Society of America 87(4), 1738–1752 (1990)

    Article  Google Scholar 

  47. Horak, P.: Automatic Speech Segmentation Based on DTW with the Application of the Czech TTS System. In: Keller, E., Bailly, G., Monaghan, A., Terken, J., Huckwale, M. (eds.) Improvements in Speech Synthesis, pp. 331–340. John Wiley and Sons Ltd., Chichester (2001)

    Google Scholar 

  48. Jankowski, C., Kalyanswamy, A., Basson, S., Spitz, J.: NTIMIT: A Phonetically Balanced, Continuous Speech, Telephone Bandwidth Speech Database. In: Proceedings of ICASSP, pp. 109–112 (1990)

    Google Scholar 

  49. Jeong, C.G., Jeong, H.: Automatic Phone Segmentation and Labeling of Continuous Speech. Speech Communication 20, 291–311 (1997)

    Article  Google Scholar 

  50. Kanthak, S., Ney, H.: Multilingual Acoustic Modeling Using Graphemes. In: Proceedings of European Conference on Speech Communication and Technology, vol. 2, pp. 1145–1148 (2003)

    Google Scholar 

  51. Kolokolov, A.S.: Preprocessing and Segmentation of the Speech Signal in the Frequency Domain for Speech Recognition. Automation and Remote Control 64(6), 985–994 (2003)

    Article  MathSciNet  Google Scholar 

  52. Laroche, J.: Time and Pitch Scale Modification of Audio Signals. In: Kahrs, M., Brandenburg, K. (eds.) Applications of Digital Signal Processing to Audio and Acoustics, Kluwer Academic Publishers, Dordrecht (1998)

    Google Scholar 

  53. Laureys, T., Demuynck, K., Duchateau, J., Wambacq, P.: An Improved Algorithm for the Automatic Segmentation of Speech Corpora. In: González Rodriguez, M., Paz Suárez Araujo, C. (eds.) Proceedings of Third International Conference on Language Resources and Evaluation, pp. 1564–1567 (2002)

    Google Scholar 

  54. Lavielle, M.: Detection of Changes in the Spectrum of Multidimensional Process. IEEE Transactions on Signal Processing 41, 742–749 (1993)

    Article  MATH  Google Scholar 

  55. Le Cerf, P., Demuynck, K., Duchateau, J., Van Compernolle, D.: Pseudo-Segment Based Speech Recognition Using Neural Recurrent Whole-Word Recognizers. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. 609–612 (1994)

    Google Scholar 

  56. Li, B.N.L., Liu, J.N.K.: A Comparative Study of Speech Segmentation for Automatic Multi-Lingual Recognition. In: Proceedings of Second ACM Hong Kong Postgraduate Research Conference (1999), http://www.cse.cuhk.edu.hk/~acm-hk/activity/pg/polyu-nlli.pdf

  57. Li, T.H., Gibson, J.D.: Speech Analysis and Segmentation by Parametric Filtering. IEEE Transactions on Speech and Audio Processing, Vol 4(3), 203–213 (1996)

    Article  Google Scholar 

  58. Li, T.H., Gibson, J.D.: Time-Correlation Analysis of Non-stationary Signals with Application to Speech Processing. In: Proceedings of International Symposium on Time- Frequency &Time-Scale Analysis, Paris, France, pp. 449–452 (1996)

    Google Scholar 

  59. Lin, M.-T., Lee, C.-K., Lin, C.-Y.: Consonant/Vowel Segmentation for Mandarin Syllable Recognition. Computer Speech and Language 23, 207–222 (1999)

    Article  Google Scholar 

  60. Malfrère, F., Deroo, O., Dutoit, T., Ris, C.: Phonetic Alignment: Speech Synthesis-Based vs. Viterbi-Based. Speech Communication 40(4), 503–515 (2003)

    Article  Google Scholar 

  61. Makhoul, J.: Spectral Linear Prediction: Properties and Applications. IEEE Transactions ASSP 23(5), 283–296 (1975)

    Article  Google Scholar 

  62. Mirghafori, N.: A Multi-Band Approach to Automatic Speech Recognition. Ph.D. thesis, University of California, Berkeley (December 1988), ch. 4. Reprinted as ICSI Technical Report, TR-99-04, Berkeley, CA (1999)

    Google Scholar 

  63. Mitchell, C.D., Harper, M.P., Jamieson, L.H.: Using Explicit Segmentation to Improve HMM Phone Recognition. In: Proceedings of International Conference on Acoustic, Speech and Signal Processing, pp. 229–232 (I995)

    Google Scholar 

  64. Park, E.-Y., Kim, S.-H., Chung, J.-H.: Automatic Speech Synthesis Unit Generation with MLP Based Postprocessor against Auto-segmented Phoneme Errors. In: Proceedings of International Joint Conference on Neural Networks, vol. 5, pp. 2985–2990 (1999)

    Google Scholar 

  65. Parson, T.: Voice and Speech Processing. McGraw-Hill, New York (1986)

    Google Scholar 

  66. Parzen, E.: Time Series, Statistics and Information. In: Brillinger, D., Caines, P., Geweke, J., Parzen, E., Rosenblatt, M., Taqqu, M.S. (eds.) New Directions in Time Series Analysis, Part I. The IMA Volumes in Mathematics and its Applications. Series, vol. 45, Springer, New York (1992)

    Google Scholar 

  67. Pellom, B.L., Hansen, J.H.L.: Automatic Segmentation of Speech Recorded in UnknownNoisy Channel Characteristics. Speech Communication 25, 97–116 (1998)

    Article  Google Scholar 

  68. Petek, B., Andersen, O., Dalsgaard, P.: On the Robust Automatic Segmentation of Spontaneous Speech. In: Proceedings of International Conference on Spoken Language Processing, pp. 913–916 (1996)

    Google Scholar 

  69. Peterson, W., Birdsall, T., Fox, W.: The Theory of Signal Detectability. IEEE Transactions on Information Theory 4(4), 171–212 (1954)

    Article  MathSciNet  Google Scholar 

  70. Picone, J.: Continuous Speech Recognition Using Hidden Markov Models. IEEE ASSP Magazine, 26–41 (1990)

    Google Scholar 

  71. Prasad, V.K., Nagarajan, T., Mutrhy, H.A.: Automatic Segmentation of Continuous Speech Using Phase Group Delay Functions. Speech Communication 42, 429–446 (2004)

    Article  Google Scholar 

  72. Quackenbush, S.R., Barnwell, T.P., Clements, M.A.: Objective Measures of Speech Quality. Prentice Hall, Englewood Cliffs (1988)

    Google Scholar 

  73. Rabiner, L., Juang, B.-H.: Fundamentals of Speech Recognition. Prentice-Hall, Inc., Upper Saddle River (1993)

    Google Scholar 

  74. Rabiner, L.R., Juang, B.H.: An Introduction to Hidden Markov Models. IEEE ASSP Magazine, 4–16 (1986)

    Google Scholar 

  75. Raymond, W.D., et al.: An Analysis of Transcription Consistency in Spontaneous Speech from the Buckeye Corpus. In: Proceedings of ICSLP 2002, Denver, USA (2002)

    Google Scholar 

  76. Schiel, F.: Automatic Phonetic Transcription of Non-Prompted Speech. In: Proceedings of the 14th International Congress on Phonetic Sciences, San Francisco, pp. 607–610 (1999)

    Google Scholar 

  77. Schillo, C., Fink, G.A., Kummert, F.: Grapheme Based Recognition for Large Vocabularies. In: Procceedings of International Conference on Spoken Processing, pp. 129–132 (2000)

    Google Scholar 

  78. Sharma, M., Mammone, R.: Automatic Speech Segmentation Using Neural Tree Networks. In: Proceedings of IEEE Workshop on Neural Networks for Signal Processing, pp. 282–290 (1995)

    Google Scholar 

  79. Segura-Luna, J.C., Soler, J.M., Peinado, A.M., Sanchez, V., Rubio, A.: Signal Segmentation into Spectral Homogeneous Units. In: Proceedings of European Signal Processing Conference, pp. 1251–1254 (1990)

    Google Scholar 

  80. Silverman, H.F., Morgan, D.P.: The Application of Dynamic Programming to Connected Speech Recognition. IEEE ASSP Magazine, 6–25 (1990)

    Google Scholar 

  81. Stephens, S.S., Volkman, J.: The Relation of Pitch to Frequency. American Journal of Psychology 53(3), 329–353 (1940)

    Article  Google Scholar 

  82. Suontasuta, J., Hakkinen, J.: Decision Tree Based Text-to-Mapping for Speech Recognition. In: Procceedings of International Conference on Spoken Processing, pp. 199–202 (2000)

    Google Scholar 

  83. Svendsen, T., Soong, F.K.: On Automatic Segmentation of Speech Signals. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Dallas, pp. 77–80 (1987)

    Google Scholar 

  84. Torkolla, K.: An Efficient Way to Learn English Grapheme-to-Phoneme Rules Automatically. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 199–202 (1993)

    Google Scholar 

  85. van Hemert, J.P.: Automatic Segmentation of Speech. IEEE Transactions on Signal Processing 39(4), 1008–1012 (1991)

    Article  Google Scholar 

  86. Vidal, E., Marzal, A.: A Review and New Approaches for Automatic Segmentation of Continuous Speech Signals. In: Torress, L., et al. (eds.) Signal Processing V: Theories and Applications, pp. 43–53. Elsevier Publisher, New-York (1990)

    Google Scholar 

  87. Vorstermans, A., Martens, J.P., Van Coile, B.: Automatic Segmentation and Labeling of Multi-lingual Speech Data. Speech Communication 19(4), 271–293 (1996)

    Article  Google Scholar 

  88. Wei, B., Gibson, J.D.: A New Discrete Spectral Modeling Method and an Application to CELP Coding. IEEE Signals Processing Letters 10(4), 101–103 (2003)

    Article  Google Scholar 

  89. Wei, B., Gibson, J.D.: Comparison of Distance Measure in Discrete Spectral Modeling. In: Proceedings of IEEE Digital Signal Processing Workshop, pp. 1–4 (2000)

    Google Scholar 

  90. Wendt, C., Petropulu, A.P.: Pitch Determination and Speech Segmentation Using the Discrete Wavelet Transform. In: Proccedings of IEEE International Symposium on Circuits and Systems, vol. 2, pp. 45–48 (1996)

    Google Scholar 

  91. Wesenick, M.B., Kipp, A.: Estimating the Quality of Phonetic Transcriptions and Segmentations of Speech Signals. In: Proceedings of ICSLP 1996, Philadelphia, USA, pp. 129–132 (1996)

    Google Scholar 

  92. Wester, M., Kessens, J.M., Cucchiarini, C., Strik, H.: Comparison between Expert Listeners and Continuous Speech Recognizers in Selecting Pronunciation Variants. In: Proceedings of the 14th Int. Congress of Phonetic Sciences, San Francisco, pp. 723–726 (1999)

    Google Scholar 

  93. Wokurek, W.: Corpus Based Evaluation of Entropy Rate Speech Segmentation. In: Proceedings of 14th International Congress of Phonetic Sciences, pp. 1217–1220 (1999)

    Google Scholar 

  94. Young, S.J., Woodland, P.C.: State Clustering in Hidden Markov Model-Based Continuous Speech Recognition. Computer Speech and Language 8, 369–383 (1994)

    Article  Google Scholar 

  95. Zue, V.W., Glass, J.R., Philips, M., Seneff, S.: Acoustic Segmentation and Phonetic Classification in the Summit System. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, pp. 389–392 (1989)

    Google Scholar 

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Authors and Affiliations

  1. Dipartimento di Psicologia, Seconda Università di Napoli, Via Vivaldi 43, Caserta, Italy

    Anna Esposito

  2. IIASS, Via Pellegrino 19, 84019, Vietri sul Mare, INFM Salerno, Italy

    Anna Esposito & Guido Aversano

  3. École Nationale Supérieure des Télécommunications (ENST), 46 rue Barrault, 75634, Paris cedex 13, France

    Guido Aversano

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  1. Anna Esposito
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  2. Guido Aversano
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Editors and Affiliations

  1. CNRS LTCI/TSI Paris, 46 rue Barrault, 75634, Paris Cedex 13, France

    Gérard Chollet

  2. Department of Psychology, Second University of Naples, and IIASS, Via Pellegrino 19, 84019, Vietri sul Mare, SA, Italy

    Anna Esposito

  3. Escola Universitària Politècnica de Mataró, Universitat Politècnica de Catalunya, Barcelona, Spain

    Marcos Faundez-Zanuy

  4. Dipartimento di Fisica “E.R. Caianiello”, Università degli Studi di Salerno, Via S. Allende, 84081, Baronissi, SA, Italy

    Maria Marinaro

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Esposito, A., Aversano, G. (2005). Text Independent Methods for Speech Segmentation. In: Chollet, G., Esposito, A., Faundez-Zanuy, M., Marinaro, M. (eds) Nonlinear Speech Modeling and Applications. NN 2004. Lecture Notes in Computer Science(), vol 3445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11520153_12

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