Algoritmos fonéticos para la detección de palabras fonéticamente similares en el español del centro de México

References

1. Anguita, J., Peillon, S., Hernando, J., y Bramoulle, A.
   (2004) Word confusability prediction in automatic speech recognition. Eighth International Conference on Spoken Language Processing, 1–4.
   [Google Scholar]
2. Bahl, Lalit R., Gennaro, S. V. De, Gopalakrishnan, P. S. and Mercer, Robert L.
   1989 First European Conference on Speech Communication and Technology. A fast approximate acoustic match for large vocabulary speech recognition.
   [Google Scholar]
3. Blanch, J. M. L.
   (1967) La influencia del sustrato en la fonética del español de México. Revista de Filología Española, 50, 145–161. 10.3989/rfe.1967.v50.i1/4.851
   https://doi.org/10.3989/rfe.1967.v50.i1/4.851 [Google Scholar]
4. Branting, L. K.
   (2003) A comparative evaluation of name-matching algorithms. Proceedings of the 9th international conference on Artificial intelligence and law, 224–232.
   [Google Scholar]
5. Caballero-Morales, S.-O.
   (2013) Recognition of emotions in mexican spanish speech: An approach based on acoustic modelling of emotion-specific vowels. The Scientific World Journal, 1–13.
   [Google Scholar]
6. Chavarría-Amezcua, M.-A.
   (2010) Manual de etiquetado fonético e imágenes acústicas de los alófonos del español de la Ciudad de México, para su uso en las tecnologías del habla (pp.70–187). Tesis de licenciatura, Facultad de Filosofía y Letras, UNAM.
   [Google Scholar]
7. Chen, J.-Y., Olsen, P. A., y Hershey, J. R.
   (2007) Word confusability-measuring hidden Markov model similarity. Eighth Annual Conference of the International Speech Communication Association, 2089–2092.
   [Google Scholar]
8. Christian, P.
   (1998) Soundex-can it be improved?Computers in Genealogy, 6, 215–221.
   [Google Scholar]
9. Cuétara, J.
   (2004) Fonética de la ciudad de México. Aportaciones desde las tecnologías del habla (pp.15–135). Tesis de maestría, Posgrado en Lingüística, UNAM.
   [Google Scholar]
10. Daniel, Y.
    (2004) Application of the Double Metaphone Algorithm to Amharic Orthography. International Conference of Ethiopian StudiesXV, 1–13.
    [Google Scholar]
11. Davis, S., y Mermelstein, P.
    (1980) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE transactions on acoustics, speech, and signal processing, 28, 357–366. 10.1109/TASSP.1980.1163420
    https://doi.org/10.1109/TASSP.1980.1163420 [Google Scholar]
12. Elmagarmid, A. K., Ipeirotis, P. G., y Verykios, V. S.
    (2007) Duplicate record detection: A survey. IEEE Transactions on knowledge and data engineering, 19, 1–16. 10.1109/TKDE.2007.250581
    https://doi.org/10.1109/TKDE.2007.250581 [Google Scholar]
13. Fernández, J. G.
    (2007) Fonética para profesores de español: de la teoría a la práctica. Madrid: Arco Libros.
    [Google Scholar]
14. Gadd, T.
    (1988) Fisching fore werds: phonetic retrieval of written text in information systems. Program, 22, 222–237. 10.1108/eb046999
    https://doi.org/10.1108/eb046999 [Google Scholar]
15. Gadd, T.
    (1990) PHONIX: The algorithm. Program, 24, 363–366. 10.1108/eb047069
    https://doi.org/10.1108/eb047069 [Google Scholar]
16. Gálvez, C.
    (2007) Identificación de nombres personales por medio de sistemas de codificación fonética. Encontros Bibli: Revista eletrônica de biblioteconomia e ciência da informação, 11, 105–116.
    [Google Scholar]
17. Goldrick, M., Vaughn, C., y Murphy, A.
    (2013) The effects of lexical neighbors on stop consonant articulation. The Journal of the Acoustical Society of America, 134, 172–177. 10.1121/1.4812821
    https://doi.org/10.1121/1.4812821 [Google Scholar]
18. Goldwater, S., Jurafsky, D., y Manning, C. D.
    (2010) Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates. Speech Communication, 52, 181–200. 10.1016/j.specom.2009.10.001
    https://doi.org/10.1016/j.specom.2009.10.001 [Google Scholar]
19. Gonzales-Cam, C.
    (2008) Algoritmos fonéticos en el desarrollo de un sistema de información de marcas y signos distintivos. Biblios: Revista de bibliotecología y Ciencias de la comunicación, 32, 2–8.
    [Google Scholar]
20. Grannis, S. J., Overhage, J. M., y McDonald, C. J.
    (2004) Real world performance of approximate string comparators for use in patient matching. Medinfo, 43–47.
    [Google Scholar]
21. Knuth, D. E.
    (1998) Sorting and Searching. The Art of Computer Programming, 3, 392–396.
    [Google Scholar]
22. Hernández-Mena, C. D., y Herrera-Camacho, J. A.
    (2013) Creación de un diccionario de pronunciación de nombres propios para uso en tecnologías del habla. Vigésima cuarta reunión internacional de otoño de comunicaciones, computación, electrónica, automatización, robótica y exposición industrial ROCyC’2013, 1–5.
    [Google Scholar]
23. Hernández-Mena, C. D., y Herrera-Camacho, J. A.
    (2014a) CIEMPIESS: A new open-sourced mexican spanish radio corpus. Ninth International Conference on Language Resources and Evaluation, 14, 371–375.
    [Google Scholar]
24. Hernández-Mena, C. D., Martınez-Gómez, N. N., y Herrera-Camacho, J.-A.
    (2014b) A Set of Phonetic and Phonological Rules for Mexican Spanish Revisited, Updated, Enhanced and Implemented. Advances in Computing Science. Center for Computing Research of IPN, 83, 61–71.
    [Google Scholar]
25. Hernández-Mena, C. D., Meza-Ruiz, I. V., y Herrera-Camacho, J. A.
    (2017) Automatic speech recognizers for Mexican Spanish and its open resources. Journal of Applied Research and Technology, 15(1), 259–270. 10.1016/j.jart.2017.02.001
    https://doi.org/10.1016/j.jart.2017.02.001 [Google Scholar]
26. Kondrak, G., y Dorr, B.
    (2004) Identification of confusable drug names: A new approach and evaluation methodology. Proceedings of the 20th international conference on Computational Linguistics, 952.
    [Google Scholar]
27. Krstev, C., Vitas, D., Maurel, D., y Tran, M.
    (2005) Multilingual ontology of proper names. 2nd Language y Technology Conference, LTC’05, 116–119.
    [Google Scholar]
28. Lambert, B. L., Lin, S.-J., Chang, K.-Y., y Gandhi, S. K.
    (1999) Similarity as a risk factor in drug-name confusion errors: the look-alike (orthographic) and sound-alike (phonetic) model. Medical care, 37, 1214–1225. 10.1097/00005650‑199912000‑00005
    https://doi.org/10.1097/00005650-199912000-00005 [Google Scholar]
29. Levenshtein, V. I.
    (1966) Binary codes capable of correcting deletions, insertions, and reversals. Soviet physics doklady, 10, 707–710.
    [Google Scholar]
30. Luce, P. A., y Pisoni, D. B.
    (1998) Recognizing spoken words: The neighborhood activation model. Ear and hearing, 19(1), 1. 10.1097/00003446‑199802000‑00001
    https://doi.org/10.1097/00003446-199802000-00001 [Google Scholar]
31. McDonald, D.
    (1996) Internal and external evidence in the identification and semantic categorization of proper names. Corpus processing for lexical acquisition, 21–39.
    [Google Scholar]
32. McQueen, J. M.
    (1991) The influence of the lexicon on phonetic categorization: stimulus quality in word-final ambiguity. Journal of Experimental Psychology: Human Perception and Performance, 17, 433.
    [Google Scholar]
33. Mills, D. L., Prat, C., Zangl, R., Stager, C. L., Neville, H. J., y Werker, J. F.
    (2004) Language experience and the organization of brain activity to phonetically similar words: ERP evidence from 14-and 20 month olds. Journal of Cognitive Neuroscience, 16, 1452–1464. 10.1162/0898929042304697
    https://doi.org/10.1162/0898929042304697 [Google Scholar]
34. Nye, P., y Gaitenby, J.
    (1973) Consonant intelligibility in synthetic speech and in a natural speech control (modified rhyme test results). Haskins Laboratories Status Report on Speech Research, 33, 77–91.
    [Google Scholar]
35. Pande, B., y Dhami, H.
    (2011) Application of natural language processing tools in stemming. International Journal of Computer Applications, 27, 14–19. 10.5120/3302‑4530
    https://doi.org/10.5120/3302-4530 [Google Scholar]
36. Parmar, V. P., y Kumbharana, C.
    (2014) Study Existing Various Phonetic Algorithms and Designing and Development of a working model for the New Developed Algorithm and Comparison by implementing it with Existing Algorithm(s). International Journal of Computer Applications, 98(19), 45–49. 10.5120/17295‑7795
    https://doi.org/10.5120/17295-7795 [Google Scholar]
37. Peereman, R.
    (1997) Orthographic and phonological neighborhoods in naming: Not all neighbors are equally influential in orthographic space. Journal of Memory and language, 37, 382–410. 10.1006/jmla.1997.2516
    https://doi.org/10.1006/jmla.1997.2516 [Google Scholar]
38. Philips, L.
    (1990) Hanging on the metaphone. Computer Language, 7(12), 39–44.
    [Google Scholar]
39. Philips, L.
    (2000) The double metaphone search algorithm. Cusers journal, 18, 38–43.
    [Google Scholar]
40. Pineda, L. A., Castellanos, H., Cuétara, J., Galescu, L., Juárez, J., Llisterri, J., Pérez, P., y Villaseñor, L.
    (2010) The Corpus DIMEx100: transcription and evaluation. Language Resources and Evaluation, 44, 347–370. 10.1007/s10579‑009‑9109‑9
    https://doi.org/10.1007/s10579-009-9109-9 [Google Scholar]
41. Pineda, L. A., Pineda, L. V., Cuétara, J., Castellanos, H., y López, I.
    (2004) DIMEx100: A new phonetic and speech corpus for Mexican Spanish. Iberamia, 3315, 974–984.
    [Google Scholar]
42. Pinto, D., Vilariño, D., Alemán, Y., Gómez, H., Loya, N., y Jiménez-Salazar, H.
    (2012) The Soundex phonetic algorithm revisited for SMS text representation. Text, Speech and Dialogue, 47–55. 10.1007/978‑3‑642‑32790‑2_5
    https://doi.org/10.1007/978-3-642-32790-2_5 [Google Scholar]
43. Pisoni, D. B., Nusbaum, H. C., Luce, P. A., y Slowiaczek, L. M.
    (1985) Speech perception, word recognition and the structure of the lexicon. Speech communication, 4, 75–95. 10.1016/0167‑6393(85)90037‑8
    https://doi.org/10.1016/0167-6393(85)90037-8 [Google Scholar]
44. Quilis, A.
    (1984) Métrica española. Barcelona: Ariel Barcelona.
    [Google Scholar]
45. Rahm, E., y Do, H. H.
    (2000) Data cleaning: Problems and current approaches. IEEE Data Eng. Bull, 23, 3–13.
    [Google Scholar]
46. Reddy, A. M., y Rose, R. C.
    (2008) Towards domain independence in machine aided human translation. Interspeech, 2358–2361.
    [Google Scholar]
47. Reyes-Barragán, M. A., Pineda, L. V., y Montes-y Gómez, M.
    (2009) INAOE at qast 2009: Evaluating the usefulness of a phonetic codification of transcriptions. CLEF Working Notes, 1–5.
    [Google Scholar]
48. Riley, M. D., y Roe, D. B.
    (1998) Confusable word detection in speech recognition. US: Patent No. 5,737,723, 7, 1–6.
    [Google Scholar]
49. Russell, R., y Odell, M.
    (1918) The Soundex Indexing System. Technical Report.
    [Google Scholar]
50. Stanier, A.
    (1990) How accurate is Soundex matching. Computers in Genealogy, 3, 286–288.
    [Google Scholar]
51. Taft, R.
    (1970) Special Report no. 1. Albany. New York: Bureau of Systems Development, New York State Identification and Intelligence Systems (NYSIIS).
    [Google Scholar]
52. UzZaman, N., y Khan, M.
    (2005) A double metaphone encoding for Bangla and its application in spelling checker. Natural Language Processing and Knowledge Engineering IEEE, 705–710.
    [Google Scholar]
53. Voran, S.
    (2013) Using articulation index band correlations to objectively estimate speech intelligibility consistent with the modified rhyme test. Applications of Signal Processing to Audio and Acoustics (WASPAA), 1–4.
    [Google Scholar]
54. Zobel, J., y Dart, P.
    (1996) Phonetic string matching: Lessons from information retrieval. Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, 166–172.
    [Google Scholar]