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Grammatical Inference and Automatic Speech Recognition

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Speech Recognition and Coding

Part of the book series: NATO ASI Series ((NATO ASI F,volume 147))

Abstract

Grammatical Inference (GI) is a well established discipline dealing with theory and methods for learning grammars from training data. GI concepts and techniques are reviewed in this paper, along with their applications in Automatic Speech Recognition and Understanding.

Work supported in part by the Spanish CICYT under grant TIC 1026/92-CO2. Part of the first author’s work was carried out during a stay at AT&T Bell-Labs. A more complete version of this paper is in [42]

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References

  1. E.M. Gold: “Language Identification in the Limit”. Inf. and Control, Vol. 10, 447–474, 1967.

    Article  MATH  Google Scholar 

  2. K.S. Fu: “Grammatical Inference: Introduction and Survey”. Parts 1 & 2, IEEE Trans. SMC, 55, 95–11, 409-423, 1975.

    MATH  Google Scholar 

  3. K.S. Fu: “Syntactic Pattern Recognition and Applications”. Prentice Hall, 1982

    Google Scholar 

  4. R. González and M. Thomason: “Syntactic Pattern Recognition. An Introduction”. Addison-Wesley, 1978.

    Google Scholar 

  5. L. Miclet: “Grammatical Inference”. In “Syntactic and Structural Pattern Recognition and applications”.H. Bunke, A. Sanfeliu (eds.), 237–290. World Scientific, 1990.

    Google Scholar 

  6. F. Casacuberta: “Some Relations Among Stochastic Finite State Networks Used in Automatic Speech Recognition”. IEEE Trans. PAM?, 12, 7, 1990.

    Google Scholar 

  7. F. Jelinek: “Up from Trigrams: The Struggle for Improved Language Models” EUROESPEECH’91.

    Google Scholar 

  8. J.E. Hoptcroft and J.D. Ullman: “Introduction to Automata Theory, Languages and Computation”. Addison-Wesley, 1979.

    Google Scholar 

  9. F.J. Maryanski, T.L. Booth. “Inference of Finite-State Probabilistic Grammars”. IEEE Trans, on Computation, 26, 531–536, 1977.

    MathSciNet  Google Scholar 

  10. R. Chandhuri, A.N.V. Rhao. “Aproximating Grammar Probabilities: Solution of a Conjecture”. JACM, 33, 4, 702–705, 1986.

    Article  Google Scholar 

  11. F.R. Jelinek, J.D. Lafferty, R.L. Mercer: “Basic Methods of Probabilistic Context-Free Grammars” in “Speech Recognition and Understanding”. P. Laface and R. DeMori Eds.. Springer Verlag, 345–360, 1992.

    Google Scholar 

  12. H. Ney: “Stochastic Grammars and Pattern Recognition” in “Speech Recognition and Understanding”. P. Laface and R. DeMori. Eds. Springer Verlag, 319–344. 1992

    Google Scholar 

  13. F. Casacuberta: “Growth Tranformations for Probabilistic Functions of Stochastic Grammars”. To be published, 1993

    Google Scholar 

  14. D. Angluin: “On the Complexity of Minimun Inference of Regular Sets” Inf. & Control, 39, 337–350, 1978.

    Article  MathSciNet  MATH  Google Scholar 

  15. E.M. Gold: ”Complexity of Automaton Identification from Given Data”. Inf. and Control, 37, 302–320, 1978.

    Article  MathSciNet  MATH  Google Scholar 

  16. D. Angluin & C.H. Smith: “Inductive Inference: Theory and Methods”. Comp. Surveys, 15, N°3, 46–62, 1983.

    Article  MathSciNet  Google Scholar 

  17. D. Angluin: Inductive Inference of Formal Languages from Positive Data”. Inf. & Control, 45, 117–135, 1980.

    Article  MathSciNet  MATH  Google Scholar 

  18. D. Angluin: “Inference of Reversible Languages” J.ACM, 29, 3, 741–765, 1982.

    Article  MathSciNet  MATH  Google Scholar 

  19. V. Radhakrishnan and G. Nagaraja:: ““Inference of Regular Grammars via Skeletons”. IEEE Trans SMC, 17, 6, 11–21, 1987.

    MathSciNet  Google Scholar 

  20. P. Garcia and E. Vidal: “Inference of K-Testable Languages In the Strict Sense and Application to Syntactic Pattern Recognition”. IEEE Trans, on PAM?, 12, 9, 920–925, 1990.

    Article  Google Scholar 

  21. A.W. Biermann and J.A. Feldmann: “On the Synthesis of Finite-State Machines from Samples of their behavior” IEEE Trans. Compt., C-21, 592–597.

    Google Scholar 

  22. L. Miclet: “Regular Inference with a Tail-Clustering Method”. IEEE Trans. SMC, 10, 737–743. 1980.

    MathSciNet  Google Scholar 

  23. S. Muggleton: “Induction of Regular Languages from Positive Examples” Tech. Rep, Turing Institute Research Memoranda, Glasgow, 1984.

    Google Scholar 

  24. H. Rulot and E. Vidal: “Modelling (sub) string-lenght-based constraints through a Grammatical Inference Methods”, Devijver and Kittler, eds. (Springer, Berlin).

    Google Scholar 

  25. P. García, E. Vidal and F. Casacuberta: “Local Languages, the Successor Method, and a step towards a General methodology for the Inference of Regular Grammars”. IEEE Trans. PAMI, 9, 6, 841–845. 1987.

    Article  Google Scholar 

  26. R.M. Wharton: “Approximate Language Identification”. Inf. and Control, 26, 236–255.

    Google Scholar 

  27. L.G. Valiant: “A Theory of the learnable”. Communications of the ACM, 27, 11, 1134–1142, 1984.

    Article  MATH  Google Scholar 

  28. B.K. Natarajan.: “Machine Learning. A theoretical approach”. Morgan Kaufmann, 1991.

    Google Scholar 

  29. M.G. Thomason, E. Granum and R,E, Blake: “Experiments in dynamic programming inference of Markov netwrks whit strings representing speech data”. Patt. Recog, 19, 5, 343–351, 1986.

    Article  Google Scholar 

  30. A. Falaschi: “Phonetic Structure inference of phonemic HMM” in “Speech Recognition and Understanding. Recent Advances”. Ed. P. Laface and R. deMori. Springer-Verlag, 71–76, 1992.

    Google Scholar 

  31. P. Lockwood, M.Blanchet: “An Algorithm for the Dynamic Inference of Hidden Markov Models (DIHMM)”. ICASSP’92. Vol. II. 251–254.

    Google Scholar 

  32. G.D. Forney: “The Viterbi algorithm”. IEEE proc. N 3, 268–278, 1973.

    Article  MathSciNet  Google Scholar 

  33. E. Vidal, H. Rulot, J.M. Valiente, G. Andreu.: “Application of the Error-Correcting Grammatical Inference Algorithm (ECGI) to Planar Shape Recognition”. ICGI’93, Essex, April, 1993.

    Google Scholar 

  34. H. Rulot, N. Prieto, E. Vidal: “Learning Accurate Finite-State Structural Models of Words through the ECGI algorithm”. ICASSP’89 proc, Vol. 1, 643–646.

    Google Scholar 

  35. H. Rulot: “ECGI: Un Algoritmo de Inferencia Gramatical Mediante correccion de Errores”. Doctoral dissertation, Universitat de València. 1992.

    Google Scholar 

  36. F. Casacuberta, E. Vidal, B. Mas, H. Rulot: “Learning the Structure of HMM’s Trrough Grammatical Inference Techniques”. ICASSP’90 proc., 717–720.

    Google Scholar 

  37. E. Vidal, H. Rulot, J.M. Valiente, G. Andreu: “Font-Independent Mixed-Size Digit Recognition Through the Error-Correctinh Grammatical Inference Algorithm (ECGI)”. ICPR’92, 334–337. 1992.

    Google Scholar 

  38. Y. Zalcstein: “Locally Testable Languages”. Jour. Comp. Sys. Sci., 6, 151–167, 1972.

    Article  MathSciNet  MATH  Google Scholar 

  39. E. Segarra,: “Una Aproximación Inductiva a la Comprension del Discurso Continuo”. PhD diss., Universidad Politecnica de Valencia. 1993.

    Google Scholar 

  40. A. Guerrero, E. Segarra and P. Garcia.: “Utilizatión de un Modelo de Error basado en la Extension Semicontinua del Algoritmo k-EE en tareas de Reconocimiento del Habla”. V Simposium de Reconocimiento de Formas y Analisis de Imagenes, 196–203, 1992.

    Google Scholar 

  41. P. García, E. Segarra, E. Vidal and I. Galiano: “On the use of the Morphic Generator Grammatical Inference (MGGI) methodology in automatic speech recognition”. IJPRAI, 4, 667–685, 1990.

    Google Scholar 

  42. E. Vidal, F. García and F. Casacuberta: “Grammatical Inference and Applications to Automatic Speech Recognition and Understanding”. Tech. Rep. DSIC Polyt. Univ. of Valencia, 1993.

    Google Scholar 

  43. J. Oncina and P. Garcia: “Inferring Regular Languages in Polynomial Updated Time”. In Pattern Recognition and Image Analysis. N. Perez de la Blanca, A. Sanfeliu and E. Vidal (eds) Series in Machine Perception and Artificial Intelligence, Vol-1, 49–61. World Scientific Pub, 1992.

    Google Scholar 

  44. K.J. Lang: “Random DFA’s can be Approximately Learned from Sparse Uniform Examples”. COLT’92.

    Google Scholar 

  45. M. Perles, M.O. Rabin, and E. Shamir: “The theory of definite automata” IEEE Trans. EC-12, 233–243, 1963.

    Google Scholar 

  46. R.C. Berwick and S. Pilato: “Learning Syntax by Automata Induction”. Machine Learning 2, 9–38, 1987.

    Google Scholar 

  47. J. Oncina: “Aprendizaje de Lenguajes Regulares y Funciones Subsecuenciales” Ph.D. Dis., Univ. Polit, de Valencia. 1991.

    Google Scholar 

  48. J. Oncina, P. Garcia and E. Vidal: “Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks”. IEEE Trans, on PAM?, May 1993.

    Google Scholar 

  49. A. Stoike and S. Omohundro: “Hidden Markov Model Induction by Bayesian Model Merging”. To appear in: C.L. Giles, S.J. Hanson, and J.D. Cowan, eds., Advances in Neural Information Processing Systems 5, San Mateo, CA, Morgan Kaufman, 1993.

    Google Scholar 

  50. X.D. Huang, Y. Ariki, M.A. Jack: “Hidden Markov Models for Speech Recognition” Edinburgh Univ. Press. 1990.

    Google Scholar 

  51. J. Ziv & N. Merhav.: “Estimating the Number of States of a Finite-State Source” IEEE Trans. IT, 38, 1, 1992.

    Google Scholar 

  52. K. Lari and S.J. Young: “The Estimation of Stochastic context-free grammars using the Inside-Outside Algorithm”. Comp. Speech Lang., 4, 35–36, 1990.

    Article  Google Scholar 

  53. Y. Sakakibara: “Efficient Learning of Context-Free Grammars from Positive Structural Examples”. Inf. and Comput. 97, 23–60, 1992.

    Article  MathSciNet  MATH  Google Scholar 

  54. E. Mäkinen: “On the Structural Grammatical Inference Problem for some Classes of Context-Free Grammars”. Inf. Procc. Letters 41, 1–5, 1992.

    Article  Google Scholar 

  55. J.K. Baker: “Trainable Grammars for Speeach Recognition” In Speach Communication Papers, 97th Meeting of the ASA (Klatt, D. H. and Wolf, J.J. eds), 547–550.

    Google Scholar 

  56. K. Lari and S. J. Young: “Aplications of Stochastic Context-Free Grammars Using the Inside-Outside Algorithm”. CSL, 5, 237–257, 1991.

    Google Scholar 

  57. F. Pereira and Y. Schabes: “Inside-Outside Reestimation from Partially Bracketed Corpora”. 30 Annual Meeting of the ACL, 128–135, 1992.

    Google Scholar 

  58. J. Oncina, P. Garcia and E. Vidal: “Transducer Learning in Pattern Recognition” ICPR. Proc. 1992.

    Google Scholar 

  59. J. Berstel. “Transductions and Context-Free Langages”. Teubner, Stuggart, 1979.

    Google Scholar 

  60. P.F. Brown et al.: “A Stochastical Approach to Machine Translation” Comput. Linguistics 16, 2, 1990.

    Google Scholar 

  61. E. Vidal, R. Pieraccini and E. Levin: “Learning Associations Between Grammars: a New Approach to Natural Language Understanding” EUROSPEECH-93, proc. 1993.

    Google Scholar 

  62. T.M. Cover and J.A. Thomas: “Elements of information theory” John Wiley, 1991.

    Google Scholar 

  63. G. Bordel: “Language Modelling using k-TS Grammars” DSIC Research Report, 11/40/93, 1993.

    Google Scholar 

  64. J. E. Diaz, A. J. Rubio, A. M. Peinado, E. Segarra, N. Prieto and F. Casacuberta. “Development of task oriented Spanish Speech Corpora” Eurospeech 93.

    Google Scholar 

  65. N. Prieto, E. Vidal: “Learning Languages Model Through the ECGI method”. Speech Com., 11, 299–309, 1992.

    Article  Google Scholar 

  66. P. J. Price: “Evaluation of Spoken Language Systems: the ATIS Domain”. Proc. of 3rd. DARPA Workshop on SNL, 91–95, Hidden Valley (PA), June 1990.

    Google Scholar 

  67. R. Gansner, E. Koutsofios, S.C. North and K.P. Vo: “A Technique for Drawing Directed Graphs”, IEEE Trans. Sofware Eng., March 1993.

    Google Scholar 

  68. R. Pieraccini, E. Levin: “A Learning Approach to Natural Language Understanding” NATO-ASI on ASR 1993.

    Google Scholar 

  69. E. Sanchís and N. Prieto: “kIncorporación de modelos acústico-fonéticos y semúnticos en un sistema de reconocimiento del discurso continuo” Tech. Report,DSIC (in preparation)

    Google Scholar 

  70. A. Castellanos, E. Vidal and J. Oncina: “Language Understanding and Subsequential Tansducer Learning”. First ICGI, Proc. Univer. of Essex, April 1993.

    Google Scholar 

  71. J. A. Feldman, G. Lakoff, A. Stoike and S. Hollbach Weber: “Miniature Language Acquisition: A touchstone for cognitive science” International Computer Science Institute. TR-90-009. 1990.

    Google Scholar 

  72. L.R. Bahl, P.F. Brown, P.V. de Souza, R.L. Mercer and M.A. Picheney: “Acoustic Markov Models used in the Tangóra Speech Recognition System”. Proc. ICASSP’90, 497–500.

    Google Scholar 

  73. L.R. Bahl, J.R. Bellegarda, P.V. de Souza, P.S. Gopalakrishnan, D. Nahamoo and M.A. Picheney: “A New Class of Fenonic Markov Word Models for Large Vocabulary Continuous Speech Recognition”. Proc. ICASSP′91, 177–180.

    Google Scholar 

  74. L.R. Bahl, J.R. Bellegarda, P.V. de Souza, P.S. Gopalakrishnan, D. Nahamoo, M.A. Picheny:Multonic Markov Word Models for Large Vocabulary Continuous Speech Recognition”. IEEE Trans on Speech and Audio Processing. Vol. 1(3), 334–344. 1993.

    Article  Google Scholar 

  75. [75] D.J. Pepper, M.A. Clements “On the phonetic structure of a large HMM”. ICASSP’91, 465–468,.

    Google Scholar 

  76. D. Jouvet, L. Manuary, M. Monné: “Automatic adjustments of the structure of Markov models for speech recognition applications”. EUROSPEECH’91, 927–930.

    Google Scholar 

  77. J. Takami, S. Sagayama: “A succesive state splitting algorithm for efficient allophone modeling”. Proc. ICASSP, J-573/576, 1992.

    Google Scholar 

  78. I. Galiano, F. Casacuberta, E. Sanchís. “On the Structure of Subword units for a Speaker Independent Continuous Speech Task” EUROSPEECH’91.

    Google Scholar 

  79. I. Galiano: “Decodificación Acústico-Fonética en Castellano mediante una metofdología de Inferencia Gramatical basadaen Generadores Mórficos”. Ph.D dissertation. Univ. Politécnica de Valencia. Nov. 1992.

    Google Scholar 

  80. I. Galiano, E. Sanchís, I. Torres, F. Casacuberta: “Acoustic-Phonetic Decoding of Spanish Continuous Speech” IJPRAI, To be published, 1993.

    Google Scholar 

  81. I. Galiano, F. Casacuberta: “Experiments on Spanish Phone Recognition using automatically Derived Phonemeic Baseforms”. EUROSPEECH′93.

    Google Scholar 

  82. E. Sanchís, F. Casacuberta. “Learning Structural Models of Sublexical Units”, in “Speech Recognition and Understanding Recent Advances, Trends and Applications”. NATO ASI Series.Editor P. Laface Springer-Verlag, 525–530, 1991.

    Google Scholar 

  83. E. Sanchis, F. Casacuberta, I. Galiano. “Learning Structural models of Subwords Units through Grammatical Inference Techniques”. International Conference on Acoustic, Speech and Signal Processing. Toronto (Canadá).1991.

    Google Scholar 

  84. E. Segarra, I. Galiano, F. Casacuberta: “A Semicontinuous extension of the Morphic Generator Grammatical Inference Methodology”. IAPR Workshop of Structural and Syntactic Pattern Recognition, Bern, 1992.

    Google Scholar 

  85. T. Yu. Medvedev: “On the Class of Events Representable in a Finite Automaton” (translated from Russian), in Sequential Machines-Selected Papers, ed. E. F. Moor, Addison-Wesley, 227–315. 1964.

    Google Scholar 

  86. J. Oncina: “Inference of Probabilistic Automata”. Tech. Rep. DSIC Pol. Univ. of Valencia, 1993.

    Google Scholar 

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Vidal, E., Casacuberta, F., García, P. (1995). Grammatical Inference and Automatic Speech Recognition. In: Ayuso, A.J.R., Soler, J.M.L. (eds) Speech Recognition and Coding. NATO ASI Series, vol 147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57745-1_27

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