Skip to main content
SpringerLink
Log in

Cross-Word Pronunciation Variations

  • Chapter
  • First Online:
Cross-Word Modeling for Arabic Speech Recognition

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSSPEECHTECH))

  • 580 Accesses

Abstract

This chapter presents the cross-word problem of the Arabic language. It also includes the main sources of this problem: Idgham (merging), Iqlaab (changing), Hamzat Al-Wasl deleting, and merging of two consecutive unvoweled letters. Illustrative examples of the cross-word problem are also provided.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • AbuZeina D, Al-Khatib W et al (2011) Cross-word Arabic pronunciation variation modeling for speech recognition. Int J Speech Tech: 1–10

    Google Scholar 

  • Alghamdi M, Elshafei M, Almuhtasib H (2009) Arabic broadcast news transcription system. Int J Speech Tech 10:183–195

    Article  Google Scholar 

  • Al-Haj H, Hsiao R, Lane I, Black A, Waibel A (2009) Pronunciation modeling for dialectal Arabic speech recognition, ASRU 2009: IEEE workshop, Italy

    Google Scholar 

  • Ali M, Elshafei M, Alghamdi M, Almuhtaseb H, Alnajjar A (2009) Arabic phonetic dictionaries for speech recognition. J Inform Tech Res 2(4):67–80

    Article  Google Scholar 

  • Amdal I, Fosler-Lussier E (2003) Pronunciation variation modeling in automatic speech recognition. Telektronikk, 2.2003, pp 70–82

    Google Scholar 

  • Amdal I, Korkmazskiy F, Surendran AC (2000) Joint pronunciation modeling of non-native speakers using data-driven methods, ICSLP, Beijing, China, pp 622–625

    Google Scholar 

  • Benzeghiba M, De Mori R et al (2007) Automatic speech recognition and speech variability: a review. Speech Comm 49(10–11):763–786

    Article  Google Scholar 

  • Berton A, Fetter P, Regel-Brietzmann P (1996) Compound words in large-vocabulary German speech recognition systems. In: Proceedings of the fourth international conference on spoken language, 1996. ICSLP 96, vol 2. 3–6 Oct 1996, pp 1165–1168

    Google Scholar 

  • Beulen K, Ortmanns S, Eiden A, Martin S, Welling L, Overmann J, Ney H (1998) Pronunciation modeling in the RWTH large vocabulary speech recognizer. In: Proceedings of the ESCA workshop modeling pronunciation variation for automatic speech recognition, pp 13–16

    Google Scholar 

  • Biadsy F, Habash N, Hirschberg J (2009) Improving the Arabic pronunciation dictionary for phone and word recognition with linguistically-based pronunciation rules. The 2009 annual conference of the North American chapter of the ACL, Colorado, pp 397–405

    Google Scholar 

  • Billa J, Noamany M et al (2002) Audio indexing of Arabic broadcast news. 2002 IEEE international conference on acoustics, speech, and signal processing (ICASSP)

    Google Scholar 

  • Boulianne G, Brousseau J, Ouellet P, Dumouchel P (2000) French large vocabulary recognition with cross-word phonology transducers. ICASSP 3:1675–1678

    Google Scholar 

  • Braga D, Freitas D, Barros MJ (2001) On the identification of word-boundaries using phonological rules for speech recognition and labeling phonological rules and trends in word endings. Forum American Bar Association, Chicago, IL

    Google Scholar 

  • Finke M, Waibel A (1997) Speaking mode dependent pronunciation modeling in large vocabulary conversational speech recognition. In: Proceedings of EuroSpeech-97, Rhodes, pp 2379–2382

    Google Scholar 

  • Fosler-Lussier E, Williams G (1999) Not just what, but also when: guided automatic pronunciation modeling for broadcast news. DARPA broadcast news workshop, Herndon, VA

    Google Scholar 

  • Fosler-Lussier E, Greenberg S, Morgan N (1999) Incorporating contextual phonetics into automatic speech recognition. In: Proceedings of the international congress on phonetic sciences, pp 611–614

    Google Scholar 

  • Giachin EP, Rosenberg AE et al (1991) Word juncture modeling using phonological rules for HMM-based continuous speech recognition. Comput Speech Lang 5(2):155–168

    Article  Google Scholar 

  • Hazen TJ, Hetherington IL, Shu H, Livescu K (2005) Pronunciation modeling using a finite-state transducer representation. Speech Comm 46(2):189–203

    Article  Google Scholar 

  • Helmer S (2001) Pronunciation adaptation at the lexical level. In: Proceedings ISCA ITRW workshop adaptation methods for speech recognition, Sophia Antipolis, France, 2001

    Google Scholar 

  • Hirsimaki T (2003) A review: decision trees in speech recognition. Helsinki University of Technology, Finland

    Google Scholar 

  • Hofmann H, Sakti S et al (2010) Improving spontaneous English ASR using a joint-sequence pronunciation model. 2010 4th international universal communication symposium (IUCS)

    Google Scholar 

  • Jurafsky D, Ward W, Zhang J, Herold K, Yu X, Zhang S (2001) What kind of pronunciation variation is hard for triphones to model? In: Proceedings of ICASSP, 2001

    Google Scholar 

  • Kessens JM, Wester M et al (1999) Improving the performance of a Dutch CSR by modeling within-word and cross-word pronunciation variation. Speech Comm 29(2–4):193–207

    Article  Google Scholar 

  • Kim M, Oh YR, Kim HK (2007) Non-native pronunciation variation modeling using an indirect data-driven method. In: Proceedings of ASRU, Japan, 2007

    Google Scholar 

  • Lamel L, Messaoudi A et al (2009) Automatic speech-to-text transcription in Arabic. ACM Trans Asian Lang Inform Process 8(4):1–18

    Article  Google Scholar 

  • Lee K-N, Chung M (2007) Morpheme-based modeling of pronunciation variation for large vocabulary continuous speech recognition in Korean. IEICE Trans Inf Syst E90-D(7):1063–1072

    Article  Google Scholar 

  • Lestari D, Furui S (2010) Adaptation to pronunciation variations in Indonesian spoken query-based information retrieval. IEICE Trans Inf Syst E93.D(9):2388–2396

    Article  Google Scholar 

  • McAllester D, Gillick L, Scattone F, Newman M (1998) Fabricating conversational speech data with acoustic models: a program to examine model-data mismatch. In: Proceedings of ICSLP, Sydney, Australia, December 1998

    Google Scholar 

  • Nock HJ, Young SJ (1998) Detecting and correcting poor pronunciations for multiword units. ESCA workshop, 1998

    Google Scholar 

  • Pousse L, Perennou G (1997) Dealing with pronunciation variants at the language model level for automatic continuous speech recognition of French. In: Proceedings of Eurospeech-97, Rhodes, pp 2727–2730

    Google Scholar 

  • Ravishankar M, Eskenazi M (1997) Automatic generation of context-dependent pronunciations. In: Proceedings of EuroSpeech-97, Rhodes, pp 2467–2470

    Google Scholar 

  • Riley M, Ljolje A (1995) Automatic generation of detailed pronunciation lexicons. In: Lee CH, Soong FK, Paliwal KK (eds) Automatic speech and speaker recognition: advanced topics. Kluwer Academic, Boston

    Google Scholar 

  • Riley M, Byrne W, Finke M, Khudanpur S, Ljolje A, McDonough J, Nock H, Saraclar M, Wooters C, Zavaliagkos G (1998) Stochastic pronunciation modelling from handlabelled phonetic corpora. In: Proceedings of ETRW on modeling pronunciation variation for automatic speech recognition, 1998, pp 109–116

    Google Scholar 

  • Saon G, Padmanabhan M (2001) Data-driven approach to designing compound words for continuous speech recognition. IEEE Trans Speech Audio Process 9(4):327–332

    Article  Google Scholar 

  • Saraçlar M, Nock H, Khudanpur S (2000) Pronunciation modeling by sharing Gaussian densities across phonetic models. Comput Speech Lang 14:137–160

    Article  Google Scholar 

  • Seman N, Kamaruzaman J (2008) Acoustic pronunciation variations modeling for standard Malay speech recognition. Comput Inform Sci 1(4):112–120, ISSN 1913-8989

    Google Scholar 

  • Siegler MA, Stern RM (1995) On the effects of speech rate in large vocabulary speech recognition systems. 1995 international conference on acoustics, speech, and signal processing, 1995. ICASSP-95

    Google Scholar 

  • Sloboda T, Waibel A (1996) Dictionary learning for spontaneous speech recognition. In: Proceedings of ICSLP-96, Philadelphia, PA, USA, pp 2328–2331

    Google Scholar 

  • Tajchman G, Foster E, Jurafsky D (1995) Building multiple pronunciation models for novel words using exploratory computational phonology. In EUROSPEECH-1995, pp 2247–2250

    Google Scholar 

  • Tsai MY, Chou FC et al (2007) Pronunciation modeling with reduced confusion for Mandarin Chinese using a three-stage framework. IEEE Trans Audio Speech Lang Process 15(2):661–675

    Article  MathSciNet  Google Scholar 

  • Uraga E, Pineda LA (2002) Automatic generation of pronunciation lexicons for Spanish. In: Proceedings of the third international conference on computational linguistics and intelligent text processing, Springer, Netherlands, pp 330–338

    Google Scholar 

  • Weintraub M, Murveit H et al (1989) Linguistic constraints in hidden Markov model based speech recognition. 1989 international conference on acoustics, speech, and signal processing, 1989. ICASSP-89

    Google Scholar 

  • Wester M (2003) Pronunciation modeling for ASR—knowledge-based and data-derived methods. Comput Speech Lang 17:69–85

    Article  Google Scholar 

  • Wester M, Fosler-Lussier E (2000) A comparison of data-derived and knowledge-based modeling of pronunciation variation, ICSLP, Bejing, China, 2000

    Google Scholar 

  • Xin L, Wen W et al (2009) Data-driven lexicon expansion for Mandarin broadcast news and conversation speech recognition. IEEE international conference on acoustics, speech and signal processing, 2009. ICASSP 2009

    Google Scholar 

  • Yang Q, Martens J-P (2000) On the importance of exception and cross-word rules for the data-driven creation of Lexica for ASR. In: Proceedings of the 11th ProRisc workshop, 29 Nov–1 Dec, Veldhoven, The Netherlands, pp 589–593

    Google Scholar 

  • Zhang J, Gao J et al (2000) Extraction of Chinese compound words: an experimental study on a very large corpus. In: Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th annual meeting of the association for computational linguistics, vol 12. Association for Computational Linguistics, Hong Kong, pp 132–139

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    Dia AbuZeina & Moustafa Elshafei

Authors
  1. Dia AbuZeina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moustafa Elshafei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dia AbuZeina .

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Dia AbuZeina

About this chapter

Cite this chapter

AbuZeina, D., Elshafei, M. (2012). Cross-Word Pronunciation Variations. In: Cross-Word Modeling for Arabic Speech Recognition. SpringerBriefs in Electrical and Computer Engineering(). Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1213-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-1213-7_4

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4614-1212-0

  • Online ISBN: 978-1-4614-1213-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation