Advertisement

Speech and Handwriting Recognition

  • Francesco CamastraEmail author
  • Alessandro Vinciarelli
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

What the reader should know to understand this chapter \(\bullet \) Hidden Markov models (Chap.  10). \(\bullet \) Language models (Chap.  10). \(\bullet \) Bayes decision theory (Chap.  3).

Keywords

Hide Markov Model Discrete Cosine Transform Language Model Speech Recognition System Word Error Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    D. Abberley, S. Renals, D. Ellis, and T. Robinson. The THISL SDR system at TREC-8. In Proceedings of 8 \(^{th}\) Text Retrieval Conference, pages 699–706, 1999.Google Scholar
  2. 2.
    D. Attwater, M. Edgington, P. Durston, and S. Whittaker. Practical issues in the application of speech technology to network and customer service applications. Speech Communication, 31(4):279–291, 2000.Google Scholar
  3. 3.
    R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, 1999.Google Scholar
  4. 4.
    L.R. Bahl, V. De Gennaro, P.S. Gopalakrishnan, and R.L. Mercer. A fast approximate acoustic match for large vocabulary speech recognition. IEEE Transactions on Speech and Audio Processing, 1(1):59–67, 1993.Google Scholar
  5. 5.
    H. Bourlard and N. Morgan. Connectionist Speech Recognition - A Hybrid Approach. Kluwer, 1994.Google Scholar
  6. 6.
    R.M. Bozinovic and S.N. Srihari. Off-line cursive script word recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(1):69–83, January 1989.Google Scholar
  7. 7.
    H. Bunke, M. Roth, and E.G. Schukat-Talamazzini. Off-line cursive handwriting recognition using hidden Markov models. Pattern Recognition, 28(9):1399–1413, September 1995.Google Scholar
  8. 8.
    Horst Bunke, M. Roth, and E.G. Schukat-Talamazzini. Off-line recognition of cursive script produced by a cooperative writer. In Proceedings of International Conference on Pattern Recognition, pages 383–386, 1994.Google Scholar
  9. 9.
    W. Byrne, D. Doermann, M. Franz, S. Gustman, J. Hajic, D. Oard, M. Picheny, J. Psutka, B. Ramabhadran, D. Soergel, T. Ward, and Wei-Jing Zhu. Automatic recognition of spontaneous speech for access to multilingual oral history archives. IEEE Transactions on Speech and Audio Processing, 12(4):420–435, 2004.Google Scholar
  10. 10.
    E. Chang, F. Seide, H.M. Meng, Zhuoran Chen, Yu Shi, and Yuk-Chi Li. A system for spoken query information retrieval on mobile devices. IEEE Transactions on Speech and Audio Processing, 10(8):531–541, 2002.Google Scholar
  11. 11.
    M.Y. Chen and A. Kundu. An alternative to variable duration HMM in handwritten word recognition. In Proceedings of International Workshop on Frontiers in Handwriting Recognition, 1993.Google Scholar
  12. 12.
    M.Y. Chen, A. Kundu, and J. Zhou. Off-line handwritten word recognition using a hidden Markov model type stochastic network. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(5):481–496, May 1994.Google Scholar
  13. 13.
    W. Chen, P. Gader, and H. Shi. Lexicon-driven handwritten word recognition using optimal linear combinations of order statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(1):77–82, January 1999.Google Scholar
  14. 14.
    J. Chu-Carroll and B. Carpenter. Vector based natural language call routing. Computational Linguistics, 25(3):361–388, 1999.Google Scholar
  15. 15.
    F.S. Cohen. Markov random fields for image modelling e analysis. In U. Desai, editor, Modelling and Applications of Stochastic Processes, pages 243–272. Kluwer Academic Press, 1986.Google Scholar
  16. 16.
    S. Deligne, S. Dharanipragada, R. Gopinath, B. Maison, P. Olsen, and H. Printz. A robust high accuracy speech recognition system for mobile applications. IEEE Transactions on Speech and Audio Processing, 10(8):551–561, 2002.Google Scholar
  17. 17.
    V. Di Lecce, A. Dimauro, Guerriero, S. Impedovo, G. Pirlo, and A. Salzo. A new hybrid approach for legal amount recognition. In Proceedings of International Workshop on Frontiers in Handwriting Recognition, pages 199–208, Amsterdam, 2000.Google Scholar
  18. 18.
    G. Dimauro, S. Impedovo, and G. Pirlo. Automatic recognition of cursive amounts on italian bank-checks. In S. Impedovo, editor, Progress in Image Analysis and Processing III, pages 323–330. World Scientific, 1994.Google Scholar
  19. 19.
    G. Dimauro, S. Impedovo, G. Pirlo, and A. Salzo. Bankcheck recognition systems: re-engineering the design process. In A. Downton and S. Impedovo, editors, Progress in Handwriting Recognition, pages 419–425.Google Scholar
  20. 20.
    G. Dimauro, S. Impedovo, G. Pirlo, and A. Salzo. Automatic bankcheck processing: A new engineered system. In Automatic Bankcheck Processing, pages 5–42. World Scientific Publishing, 1997.Google Scholar
  21. 21.
    S. Edelman, T. Flash, and S. Ullman. Reading cursive handwriting by alignment of letter prototypes. International Journal of Computer Vision, 5(3):303–331, March 1990.Google Scholar
  22. 22.
    A. El Yacoubi, J.M. Bertille, and Gilloux M. Conjoined location and recognition of street names within a postal address delivery line. In Proceedings of International Conference on Document Analysis and Recognition, volume 1, pages 1024–1027, Montreal, 1995.Google Scholar
  23. 23.
    A. El-Yacoubi, M. Gilloux, R. Sabourin, and C.Y. Suen. An HMM,-based approach for off-line unconstrained handwritten word modeling and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(8):752–760, August 1999.Google Scholar
  24. 24.
    John T. Favata. General word recognition using approximate segment-string matching. In Proceedings of International Conference on Document Analysis and Recognition, volume 1, pages 92–96, Ulm, 1997.Google Scholar
  25. 25.
    M. Franz, J.S. McCarley, and R.T. Ward. Ad hoc, cross-language and spoken document information retrieval at IBM. In Proceedings of 8 \(^{th}\) Text Retrieval Conference, pages 391–398, 1999.Google Scholar
  26. 26.
    M.J. Gales, D.Y. Kim, P.C. Woodland, H.Y. Chan, D. Mrva, R. Sinha, and S.A. Tranter. Progress in the CU-HTK boradcast news transcription system. IEEE Transactions on Audio, Speech and Language Processing, 14(5):1513–1525, 2006.Google Scholar
  27. 27.
    J.S. Garofolo, C.G.P. Auzanne, and E.M. Voorhees. The TREC spoken document retrieval track: A success story. In Proceedings of 8\(^{th}\) Text Retrieval Conference, pages 107–129, 1999.Google Scholar
  28. 28.
    J.L. Gauvain, Y. de Kercadio, L. Lamel, and G. Adda. The LIMSI SDR system for TREC-8. In Proceedings of 8 \(^{th}\) Text Retrieval Conference, pages 475–482, 1999.Google Scholar
  29. 29.
    C. Gerber. Found in translation. Military Information Technology, 10(2), 2006.Google Scholar
  30. 30.
    P.S. Gopalakrishnan, L.R. Bahl, and R.L. Mercer. A tree search strategy for large vocabulary continuous speech recognition. In Proceedings of the IEEE International Conference on Acoustic, Speech and Signal Processing, pages 572–575, 1995.Google Scholar
  31. 31.
    A. Gorin, G. Riccardi, and J. Wright. How may I help you? Speech Communication, 23(2):113–127, 1997.Google Scholar
  32. 32.
    N. Gorski, V. Anisimov, E. Augustin, O. Baret, D. Price, and J.C. Simon. A2iA check reader: A family of bank check recognition systems. In Proceedings of International Conference on Document Analysis and Recognition, volume 1, pages 523–526, Bangalore, 1999.Google Scholar
  33. 33.
    D. Graff, C. Cieri, S. Strassel, and N. Martey. The TDT-3 text and speech corpus. In Proceedings of Topic Detection and Tracking Workshop, 2000.Google Scholar
  34. 34.
    D. Guillevic and C.Y. Suen. HMM word engine recognition. In Proceedings of International Conference on Document Analysis and Recognition, volume 2, pages 544–547, Ulm, 1997.Google Scholar
  35. 35.
    T. Hain, L. Burget, J. Dines, G. Garau, M. Karafiat, M. Lincoln, J. Vepa, and V. Wan. The AMI meeting transcription system: progress and performance. In IEEE International Conference on Acoustics, Speech and Signal Processing, 2007.Google Scholar
  36. 36.
    B. Han, R. Nagarajan, R. Srihari, and M. Srikanth. TREC-8 experiments at SUNY at Buffalo. In Proceedings of 8 \(^{th}\) Text Retrieval Conference, pages 591–596, 1999.Google Scholar
  37. 37.
    J.H.L. Hansen, R. Huang, B. Zhou, M. Seadle, J.R. Deller, A.R. Gurijala, M. Kurimo, and P. Angkititrakul. Speechfind: Advances in spoken document retrieval for a national gallery of the spoken word. IEEE Transactions on Speech and Audio Processing, 13(5):712–730, 2005.Google Scholar
  38. 38.
    Q. Huang and S. Cox. Task-independent call-routing. Speech Communication, 48(3–4):374–389, 2006.Google Scholar
  39. 39.
    X. Huang, A. Acero, and H.-W. Hon. Spoken Language Processing: A Guide to Theory, Algorithm and System Development. Prentice-Hall, 2001.Google Scholar
  40. 40.
    F. Jelinek. Statistical Methods for Speech Recognition. MIT Press, 1997.Google Scholar
  41. 41.
    S.E. Johnson, P. Jourlin, K. Spärck-Jones, and P.C. Woodland. Spoken document retrieval for TREC-8 at Cambridge University. In Proceedings of 8 \(^{th}\) Text Retrieval Conference, pages 197–206, 1999.Google Scholar
  42. 42.
    D. Jurafsky and J.H. Martin. Speech and Language Processing: an Introduction to Natural Processing Computational Linguistics, and Speech Recognition. Prentice-Hall, 2000.Google Scholar
  43. 43.
    G. Kaufmann and H. Bunke. Automated reading of cheque amounts. Pattern Analysis and Applications, 3:132–141, march 2000.Google Scholar
  44. 44.
    T. Kawahara, M. Hasegawa, K. Shitaoka, T. Kitade, and H. Nanjo. Automatic indexing of lecture presentations using unsupervised learning of presumed discourse markers. IEEE Transactions on Speech and Audio Processing, 12(4):409–419, 2004.Google Scholar
  45. 45.
    G. Kim and V. Govindaraju. Handwritten word recognition for real time applications. In Proceedings of International Conference on Document Analysis and Recognition, volume 1, pages 24–27, Montreal, 1995.Google Scholar
  46. 46.
    G. Kim and V. Govindaraju. A lexicon driven approach to handwritten word recognition for real time application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4):366–379, 1997.Google Scholar
  47. 47.
    S. Knerr, E. Augustin, O. Baret, and D. Price. Hidden Markov model based word recognition and its application to legal amount reading on French checks. Computer Vision and Image Understanding, 70(3):404–419, June 1998.Google Scholar
  48. 48.
    W. Kraaij, R. Pohlmann, and D. Hiemstra. Twenty-one at TREC-8 using language technology for information retrieval. In Proceedings of 8 \(^{th}\) Text Retrieval Conference, pages 285–300, 1999.Google Scholar
  49. 49.
    A. Kundu, Y. He, and M.Y. Che. Alternatives to variable duration HMM in handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1275–1280, November 1998.Google Scholar
  50. 50.
    H.-K.J. Kuo and L. Chin-Hui. Discriminative training of natural language call routers. IEEE Transactions on Speech and Audio Processing, 11(1):24–35, 2003.Google Scholar
  51. 51.
    M. Kurimo. Thematic indexing of spoken documents by using self-organizing maps. Speech Communication, 38(1–2):29–45, 2002.Google Scholar
  52. 52.
    C.H. Lee, B. Carpenter, W. Chou, J. Chu-Carroll, W. Reichl, A. Saad, and Q. Zhou. On natural language call routing. Speech Communication, 31(4):309–320, 2000.Google Scholar
  53. 53.
    D. Li, W. Kuansan, A. Acero, H. Hsiao-Wuen, J. Droppo, C. Boulis, W. Ye-Yi, D. Jacoby, M. Mahajan, C. Chelba, and X.D. Huang. Distributed speech processing in miPad’s multimodal user interface. IEEE Transactions on Speech and Audio Processing, 10(8):605–619, 2002.Google Scholar
  54. 54.
    S. Madhvanath, E. Kleinberg, V. Govindaraju, and S.N. Srihari. The HOVER system for rapid holistic verification of off-line handwritten phrases. In Proceedings of International Conference on Document Analysis and Recognition, volume 2, pages 855–859, Ulm, 1997.Google Scholar
  55. 55.
    S. Madhvanath, E. Kleinberg, and V. Govindaraju. Holistic verification of handwritten phrases. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999.Google Scholar
  56. 56.
    U. Marti and H. Bunke. Towards general cursive script recognition. In Proceedings of International Workshop on Frontiers in Handwriting Recognition, pages 379–388, Korea, 1998.Google Scholar
  57. 57.
    U.-V. Marti and H. Bunke. A full english sentence database for off-line handwriting recognition. In Proceedings of International Conference on Document Analysis and Recognition, volume 1, pages 705–708, Bangalore, 1999.Google Scholar
  58. 58.
    U.V. Marti and H. Bunke. Handwritten sentence recognition. In Proceedings of International Conference on Pattern Recognition, volume 3, pages 467–470, Barcelona, 2000.Google Scholar
  59. 59.
    U.V. Marti and H. Bunke. Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system. International Journal of Pattern Recognition and Artificial Intelligence, 2001.Google Scholar
  60. 60.
    U.V. Marti and H. Bunke. The IAM-database: an English sentence database for offline handwriting recognition. International Journal of Document Analysis and Recognition, 5(1):39–46, january 2002.Google Scholar
  61. 61.
    U. Marti, G. Kaufmann, and Bunke H. Cursive script recognition with time delay neural networks using learning hints. In W. Gerstner, A. Gernoud, M. Hasler, and J.D. Nicoud, editors, Artificial Neural Networks - ICANN97, pages 973–979. Springer Verlag, 1997.Google Scholar
  62. 62.
    S. Matsoukas, J.L. Gauvain, G. Adda, T. Colthurst, C.L. Kao, O. Kimball, L. Lamel, F. Lefevre, J.Z. Ma, J. Makhoul, L. Nguyen, R. Prasad, R. Schwartz, H. Schwenk, and B. Xiang. Advances in transcription of broadcast news and conversational telephone speech within the combined EARS BBN/LIMSI. IEEE Transactions on Audio, Speech and Language Processing, 14(5):1541–1556, 2006.Google Scholar
  63. 63.
    M. Mohamed and P. Gader. Handwritten word recognition using segmentation-free hidden Markov modeling and segmentation-based dynamic programming techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(5):548–554, May 1996.Google Scholar
  64. 64.
    S. Möller, J. Krebber, and P. Smeele. Evaluating the speech output component of a smart-home system. Speech Communication, 48(1):1–27, 2006.Google Scholar
  65. 65.
    C. Olivier, T. Paquet, M. Avila, and Y. Lecourtier. Recognition of handwritten words using stochastic models. In Proceedings of International Conference on Document Analysis and Recognition, volume 1, pages 19–23, Montreal, 1995.Google Scholar
  66. 66.
    M. Padmanabhan, G. Saon, J. Huang, B. Kingsbury, and L. Mangu. Automatic speech recognition performance on a voicemail transcription task. IEEE Transactions on Speech and Audio Processing, 10(7):433–442, 2002.Google Scholar
  67. 67.
    T. Paquet and Y. Lecourtier. Recognition of handwritten sentences using a restricted lexicon. Pattern Recognition, 26(3):391–407, 1993.Google Scholar
  68. 68.
    J. Park, V. Govindaraju, and S.N. Srihari. Efficient word segmentation driven by unconstrained handwritten phrase recognition. In Proceedings of International Conference on Document Analysis and Recognition, volume 1, pages 605–608, Bangalore, 1999.Google Scholar
  69. 69.
    R. Plamondon and S.N. Srihari. On-line and off-line handwriting recognition: A comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1):63–84, 2000.Google Scholar
  70. 70.
    D. Ponceleon and S. Srinivasan. Automatic discovery of salient segments in imperfect speech transcripts. In ACM Conference on Information and Knowledge Management, pages 490–497, 2001.Google Scholar
  71. 71.
    D. Ponceleon and S. Srinivasan. Structure and content based segmentation of speech transcripts. In ACM Conference on Research and Development in Information Retrieval (SIGIR), pages 404–405, 2001.Google Scholar
  72. 72.
    L.R. Rabiner and B.H. Juang. Fundamentals of Speech Recognition. Prentice-Hall, 1993.Google Scholar
  73. 73.
    G. Saon. Cursive word recognition using a random field based hidden Markov model. International Journal of Document Analysis and Recognition, 1(1):199–208, 1999.Google Scholar
  74. 74.
    G. Seni, V. Kripasundar, and R.K. Srihari. Generalizing edit distance to incorporate domain information: Handwritten text recognition as a case study. Pattern Recognition, 29(3):405–414, 1996.Google Scholar
  75. 75.
    A.W. Senior. Off-Line Cursive Handwriting Recognition Using Recurrent Neural Network. PhD thesis, University of Cambridge, UK, 1994.Google Scholar
  76. 76.
    A.W. Senior and A.J. Robinson. An off-line cursive handwriting recognition system. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3):309–321, March 1998.Google Scholar
  77. 77.
    M. Shridar, G. Houle, and Kimura F. Handwritten word recognition using lexicon free and lexicon directed word recognition algorithms. In Proceedings of International Conference on Document Analysis and Recognition, volume 2, pages 861–865, Ulm, 1997.Google Scholar
  78. 78.
    A. Singhal, S. Abney, M. Bacchiani, M. Collins, D. Hindle, and F. Pereira. AT&T at TREC-8. In Proceedings of 8\(^{th}\) Text Retrieval Conference, pages 317–330, 1999.Google Scholar
  79. 79.
    R.K. Srihari. Use of lexical and syntactic techniques in recognizing handwritten text. In Proceedings of ARPA workshop on Human Language Technology, pages 403–407, 1994.Google Scholar
  80. 80.
    R.K. Srihari and C. Baltus. Incorporating syntactic constraints in recognizing handwritten sentences. In Proceedings of International Joint Conference on Artificial Intelligence, pages 1262–1267, 1993.Google Scholar
  81. 81.
    S.N. Srihari. Handwritten address interpretation: a task of many pattern recognition problems. International Journal of Pattern Recognition and Artificial Intelligence, 14(5):663–674, 2000.Google Scholar
  82. 82.
    T. Steinherz, E. Rivlin, and N. Intrator. Off-line cursive script word recognition - a survey. International Journal on Document Analysis and Recognition, 2(2):1–33, 1999.Google Scholar
  83. 83.
    A. Stolcke, B. Chen, H. Franco, V.R. Rao Gadde, M. Graciarena, M.Y. Hwang, K. Kirchhoff, A. Mandal, N. Morgan, X. Lei, T. Ng, M. Ostendorf, K. Sönmez, A. Venkataraman, D. Vergyri, W. Wang, J. Zheng, and Q. Zhu. Recent innovations in speech-to-text transcriptions at SRI-ICSI-UW. IEEE Transactions on Audio, Speech and Language Processing, 14(5):1729–1744, 2006.Google Scholar
  84. 84.
    Lee S.W., editor. Advances in Handwriting Recognition. World Scientific Publishing Company, 1999.Google Scholar
  85. 85.
    O.D. Trier, A.K. Jain, and T. Taxt. Feature extraction methods for character recognition-A survey. Pattern Recognition, 10(4):641–662, 1996.Google Scholar
  86. 86.
    G. Tur, R. Schapire, and D. Hakkani-Tr. Active learning for spoken language understanding. In IEEE International Conference on Acoustics, Speech and Signal Processing, 2003.Google Scholar
  87. 87.
    I. Varga, S. Aalburg, B. Andrassy, S. Astrov, J.G. Bauer, C. Beaugeant, C. Geissler, and H. Hoge. ASR in mobile phones - an industrial approach. IEEE Transactions on Speech and Audio Processing, 10(8):562–569, 2002.Google Scholar
  88. 88.
    A. Vinciarelli. A survey on off-line cursive word recognition. Pattern Recognition, 35(7):1433–1446, 2002.Google Scholar
  89. 89.
    A. Vinciarelli. Noisy text categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(12):1882–1895, 2005.Google Scholar
  90. 90.
    A. Vinciarelli, S. Bengio, and H. Bunke. Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6):709–720, 2004.Google Scholar
  91. 91.
    W. Wang, A. Brakensiek, A. Kosmala, and G. Rigoll. HMM based high accuracy off-line cursive handwriting recognition by a baseline detection error tolerant feature extraction approach. In Proceedings of International Workshop on Frontiers in Handwriting Recognition, pages 209–218, Amsterdam, 2000.Google Scholar
  92. 92.
    B.A. Yanikoglu and P.A. Sandon. Off line cursive handwriting recognition using neural networks. In Proceedings of SPIE Conference on Applications of Artificial Neural Networks, 1993.Google Scholar
  93. 93.
    B.A. Yanikoglu and P.A. Sandon. Off-line cursive handwriting recognition using style parameters. Tech. Rep. PCS-TR93-192 Dartmouth College, 1993.Google Scholar
  94. 94.
    S. Young, D. Kershaw, J. Odell, D. Ollason, V. Valtchev, P. Woodland. The HTK book. http://htk.eng.cam.ac.uk/docs/docs/shtml, 2000
  95. 95.
    M. Zimmermann and H. Bunke. Automatic segmentation of the IAM off-line database for handwritten english text. In Proceedings of 16 \(^{th}\) International Conference on Pattern Recognition, volume IV, pages 35–39, 2002.Google Scholar
  96. 96.
    M. Zimmermann, J.-C. Chappelier, and H. Bunke. Offline grammar-based recognition of handwritten sentences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5):818–821, 2006.Google Scholar
  97. 97.
    V. Zue, S. Seneff, J.R. Glass, J. Polifroni, C. Pao, T.J. Hazen, and L. Hetherington. Juplter: a telephone-based conversational interface for weather information. IEEE Transactions on Speech and Audio Processing, 8(1):85–96, 2000.Google Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Department of Science and TechnologyParthenope University of NaplesNaplesItaly
  2. 2.School of Computing Science and the Institute of Neuroscience and PsychologyUniversity of GlasgowGlasgowUK

Personalised recommendations