Skip to main content

Free tools and resources for Brazilian Portuguese speech recognition


An automatic speech recognition system has modules that depend on the language and, while there are many public resources for some languages (e.g., English and Japanese), the resources for Brazilian Portuguese (BP) are still limited. This work describes the development of resources and free tools for BP speech recognition, consisting of text and audio corpora, phonetic dictionary, grapheme-to-phone converter, language and acoustic models. All of them are publicly available and, together with a proposed application programming interface, have been used for the development of several new applications, including a speech module for the OpenOffice suite. Performance tests are presented, comparing the developed BP system with a commercial software. The paper also describes an application that uses synthesis and speech recognition together with a natural language processing module dedicated to statistical machine translation. This application allows the translation of spoken conversations from BP to English and vice versa. The resources make easier the adoption of BP speech technologies by other academic groups and industry.


  1. 1.

    Rabiner L, Juang B (1993) Fundamentals of speech recognition. PTR Prentice Hall, Englewood Cliffs

    Google Scholar 

  2. 2.

    Huang X, Acero A, Hon H (2001) Spoken language processing. Prentice-Hall, New York

    Google Scholar 

  3. 3.

    Dutoit T (2001) An introduction to text-to-speech synthesis. Kluwer Academic, Dordrecht

    Google Scholar 

  4. 4.

    Taylor P (2009) Text-to-speech synthesis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  5. 5.

    Allen J, Hunnicutt MS, Klatt DH, Armstrong RC, Pisoni DB (1987) From text to speech: the MITalk system. Cambridge University Press, Cambridge

    Google Scholar 

  6. 6.

    Odell J, Mukerjee K (2007) Architecture, user interface, and enabling technology in Windows Vista’s speech systems. IEEE Trans Comput 56(9):1156–1168

    MathSciNet  Article  Google Scholar 

  7. 7. Visited in June 2010

  8. 8.

    Schramm M, Freitas L, Zanuz A, Barone D (2000) A Brazilian Portuguese language corpus development. In: International conference on spoken language processing, vol 2, pp 579–582

    Google Scholar 

  9. 9.

    Teruszkin R, Vianna F (2006) Implementation of a large vocabulary continuous speech recognition system for Brazilian Portuguese. J Commun Inf Syst 21:204–218

    Google Scholar 

  10. 10.

    Ynoguti CA, Violaro F (2008) A Brazilian Portuguese speech database. In: XXVI simpósio Brasileiro de telecomuniçacões

    Google Scholar 

  11. 11.

    Neto J, Meinedo H, Viveiros M, Cassaca R, Martins C, Caseiro D (2008) Broadcast news subtitling system in Portuguese. In: IEEE international conference on acoustics, speech, and signal processing

    Google Scholar 

  12. 12.

    Neto J, Martins C, Meinedo H, Almeida L (1997) The design of a large vocabulary speech corpus for Portuguese. In: Proceedings of the European conference on speech technology

    Google Scholar 

  13. 13.

    Paul D, Baker J (1992) The design for the Wall Street Journal-based CSR corpus. In: Proceedings of the international conference on spoken language processing

    Google Scholar 

  14. 14.

    Ribeiro ITM, Duarte I, Matos G (1998) Corpus de diálogo CORAL. In: III encontro para o processamento computacional da língua Portuguesa escrita e Falada

    Google Scholar 

  15. 15.

    Trancoso I, Martins R, Moniz H, Silva A, Ribeiro M (2008) The LECTRA corpus: Classroom lecture transcriptions in European Portuguese. In: Language resources and evaluation conference

    Google Scholar 

  16. 16.

    Valtchev V, Odell JJ, Woodland PC, Young SJ (1997) MMIE training of large vocabulary recognition systems. Speech Commun 22(4):303–314

    Article  Google Scholar 

  17. 17. Visited in June 2010

  18. 18.

    Vandewalle P, Kovacevic J, Vetterli M (2009) Reproducible research in signal processing—what, why, and how. IEEE Signal Process Mag 26:37–47

    Article  Google Scholar 

  19. 19.

    Couto I, Neto N, Tadaiesky V, Klautau A, Maia R (2010) An open source HMM-based text-to-speech system for Brazilian Portuguese. In: 7th international telecommunications symposium

    Google Scholar 

  20. 20. Visited in June 2010

  21. 21.

    Neto N, Sousa E, Macedo V, Adami A, Klautau A (2005) Desenvolvimento de software livre usando reconhecimento e síntese de voz: O estado da arte para o Português Brasileiro. In: 6th forum internacional software livre

    Google Scholar 

  22. 22. Visited in June 2010

  23. 23.

    Santos S, Alcaim A (2002) Um sistema de reconhecimento de voz contínua dependente da tarefa em língua portuguesa. Rev Soc Brasil Telecomun 17(2):135–147

    Google Scholar 

  24. 24.

    Fagundes R, Sanches I (2003) Uma nova abordagem foneticofonologica em sistemas de reconhecimento de fala espontinea. Rev Soc Brasil Telecomun 95:225–239

    Google Scholar 

  25. 25.

    Silva E, Baptista L, Fernandes H, Klautau A (2005) Desenvolvimento de um sistema de reconhecimento automático de voz contínua com grande vocabulário para o Português Brasileiro. In: XXV congresso da sociedade Brasileira de computação

    Google Scholar 

  26. 26.

    Abad A, Trancoso I, Neto N, Ribeiro M (2009) Porting an European Portuguese broadcast news recognition system to Brazilian Portuguese. In: Interspeech, Brighton, UK

    Google Scholar 

  27. 27.

    Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–86

    Article  Google Scholar 

  28. 28.

    Juang H, Rabiner R (1991) Hidden Markov models for speech recognition. Technometrics 33:251–272

    MathSciNet  Article  Google Scholar 

  29. 29.

    Deshmukh N, Ganapathiraju A, Picone J (1999) Hierarchical search for large-vocabulary conversational speech recognition. IEEE Signal Process Mag 84–107

  30. 30.

    Jevtić N, Klautau A, Orlitsky A (2001) Estimated rank pruning and Java-based speech recognition. In: Automatic speech recognition and understanding workshop

    Google Scholar 

  31. 31.

    Ladefoged P (2001) A course in phonetics, 4th edn. Harcourt Brace, New York

    Google Scholar 

  32. 32. Visited in June 2010

  33. 33.

    Antoniol G, Fiutem R, Flor R, Lazzari G (1993) Radiological reporting based on voice recognition. In: Human–computer interaction. Lecture notes in computer science, vol 753. Springer, Berlin, pp 242–253

    Google Scholar 

  34. 34.

    Lee CH, Gauvain JL (1993) Speaker adaptation based on MAP estimation of HMM parameters. In: IEEE ICASSP, pp 558–561

    Google Scholar 

  35. 35.

    Ralf SG, Kompe R (2000) A combined MAP + MLLR approach for speaker adaptation. Proc Sony Res Forum 9:9–14

    Google Scholar 

  36. 36.

    Bellman R (1957) Dynamic programming. Princeton University Press, Princeton

    Google Scholar 

  37. 37.

    Young S, Ollason D, Valtchev V, Woodland P (2006) The HTK book, version 3.4. Cambridge University Engineering Department, Cambridge

    Google Scholar 

  38. 38.

    Stolcke A (2002) SRILM—an extensible language modeling toolkit. In: International conference on spoken language processing

    Google Scholar 

  39. 39.

    Lee A (2009) The Julius book, 0.0.2 ed., rev 4.1.2

    Google Scholar 

  40. 40.

    Caseiro D, Trancoso I, Oliveira L, Viana C (2002) Grapheme-to-phone using finite-state transducers. In: In IEEE workshop on speech synthesis

    Google Scholar 

  41. 41.

    Teixeira A, Oliveira C, Moutinho L (2006) On the use of machine learning and syllable information in European Portuguese grapheme-phone conversion. In: Computational processing of the Portuguese language. Lecture notes in computer science, vol 3960. Springer, Berlin, pp 212–215

    Chapter  Google Scholar 

  42. 42.

    Silva D, de Lima A, Maia R, Braga D, de Moraes JF, de Moraes JA, Resende F Jr (2006) A rule-based grapheme-phone converter and stress determination for Brazilian Portuguese natural language processing. In: VI international telecommunications symposium

    Google Scholar 

  43. 43.

    Faria A (2003) Applied phonetics: Portuguese text-to-speech. Tech Rep. University of California

  44. 44. Visited in June 2010

  45. 45.

    Silva P, Neto N, Klautau A (2009) Novos recursos e utilização de adaptação de locutor no desenvolvimento de um sistema de reconhecimento de voz para o Português Brasileiro. In: XXVII simpósio Brasileiro de telecomuniçacões

    Google Scholar 

  46. 46.

    Cirigliano RJ, Monteiro C, de F Barbosa FL, Resende FL Jr, Couto L, Moraes J (2005) Um conjunto de 1000 frases foneticamente balanceadas para o Português Brasileiro obtido utilizando a abordagem de algoritmos genéticos. In: XXII simpósio Brasileiro de telecomuniçacões

    Google Scholar 

  47. 47.

    Neto N, Silva P, Klautau A, Adami A (2008) Spoltech and OGI-22 baseline systems for speech recognition in Brazilian Portuguese. In: International conference on computational processing of Portuguese language—PROPOR

    Google Scholar 

  48. 48.

    Weimar F, Barone D, Adami A (2010) A baseline system for continuous speech recognition of Brazilian Portuguese using the West Point Brazilian Portuguese speech corpus. In: International conference on computational processing of Portuguese language

    Google Scholar 

  49. 49.

    Davis S, Merlmestein P (1980) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans ASSP 357–366

  50. 50.

    Woodland P, Young S (1993) The HTK tied-state continuous speech recognizer. In: Proceedings of the Eurospeech’93, Berlim

    Google Scholar 

  51. 51.

    Welch LR (2003) Hidden Markov models and the Baum–Welch algorithm. IEEE Inf Theory Soc Newslett 53:10–12

    Google Scholar 

  52. 52.

    Silva E, Pantoja M, Celidnio J, Klautau A (2004) Modelos de linguagem n-grama para reconhecimento de voz com grande vocabulários. In: III workshop em tecnologia da informação e da linguagem humana

    Google Scholar 

  53. 53.

    Chen SF, Goodman J (1999) An empirical study of smoothing techniques for language modeling. Comput Speech Lang 13:359–394

    Article  Google Scholar 

  54. 54.

    Kneser R, Ney H (1995) Improved backing-off for M-gram language modeling. In: IEEE international conference on acoustics, speech and signal processing, pp 181–184

    Google Scholar 

  55. 55. Visited in June 2010

  56. 56. Visited in June 2010

  57. 57.

    Hosn C, Baptista LAN, Imbiriba T, Klautau A (2006) New resources for Brazilian Portuguese: results for grapheme-to-phoneme and phone classification. In: VI international telecommunications symposium

    Google Scholar 

  58. 58.

    Lander T, Cole R, Oshika B, Noel M (1995) The OGI 22 language telephone speech corpus. In: Proceedings of the Eurospeech, Madrid

    Google Scholar 

  59. 59.

    Colen WD, Batista P (2010) Veja mampe, sem as mpos! SpeechOO, uma extenspo de ditado para o In: 11th fórum internacional software livre

    Google Scholar 

  60. 60. Visited in June 2010

  61. 61.

    Koehn P, Och FJ, Marcu D (2003) Statistical phrase-based translation. In: Proceedings of the human language technology, pp 127–133

    Google Scholar 

  62. 62. Visited in June 2010

  63. 63. Visited in June 2010

  64. 64. Visited in June 2010

  65. 65.

    Aziz W, Pardo T, Paraboni I (2009) Fine-tuning in Portuguese–English statistical machine translation. In: 7th Brazilian symposium in information and human language technology

    Google Scholar 

  66. 66.

    Koehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B, Shen W, Moran C, Zens R, Dyer C, Bojar O, Constantin A, Herbst E (2007) Moses: open source toolkit for statistical machine translation. In: Proceedings of association for computational linguistics, pp 177–180

    Google Scholar 

  67. 67.

    Och FJ, Ney H (2000) Improved statistical alignment models. In: Proceedings of association for computational linguistics, pp 440–447

    Google Scholar 

  68. 68.

    Caseli H, Nunes I (2009) Statistical machine translation: little changes big impacts. In: 7th Brazilian symposium in information and human language technology, pp 1–9

    Google Scholar 

  69. 69.

    Zhang Y, Vogel S, Waibel A (2004) Interpreting BLEU/NIST scores: How much improvement do we need to have a better system. In: 4th international conference on language resources and evaluation

    Google Scholar 

  70. 70.

    Koehn P, Hoang H (2007) Factored translation models. In: Empirical methods on natural language processing, pp 868–876

    Google Scholar 

  71. 71.

    Yao X, Bhutada P, Georgila K, Sagae K, Artstein R, Traum D (2010) Practical evaluation of speech recognizers for virtual human dialogue systems. In: 7th international language resources and evaluation

    Google Scholar 

  72. 72. Visited in June 2010

Download references

Author information



Corresponding author

Correspondence to Nelson Neto.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Neto, N., Patrick, C., Klautau, A. et al. Free tools and resources for Brazilian Portuguese speech recognition. J Braz Comput Soc 17, 53–68 (2011).

Download citation


  • Speech recognition
  • Brazilian Portuguese
  • Grapheme-to-phone conversion
  • Application programming interface
  • Speech-based applications