Accent is the pattern of pronunciation which can identify a person’s linguistic, social or cultural background. It is an important source of inter-speaker variability and a particular problem for automated speech recognition. This study aims to investigate the effectiveness of rule extraction from support vector machines for speech accent classification. The presence of a speaker’s accent in the speech signal has significant implications for the accuracy of speech recognition because the effectiveness of an Automatic Speech Recognition System (ASR) is greatly reduced when the particular accent or dialect in the speech samples on which it is trained differs from the accent or dialect of the end-user [4] [14]. The correct identification of a speaker’s accent, and the subsequent use of the appropriately trained system, can be used to improve the efficiency and accuracy of the ASR application. If used in automated telephone helplines, analysing accent and then directing callers to the appropriately-accented response system may improve customer comfort and understanding. The increasing use of speech recognition technology in modern applications by people with a wide variety of linguistic and cultural backgrounds, means that addressing accent-related variability in speech is an important area of ongoing research. Rule extraction in this context can aid in the refinement of the design of a successful classifier, by discovering the contribution of the various input features, as well as by facilitating the comparison of the results with other machine learning methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Angkititrakul P, Hansen JLH (2003) Use of trajectory models for automatic accent classification. In: Proc INTERSPEECH-2003/Eurospeech-2003, Geneva, Switzerland, pp. 1353-1356, September 2003.
Barakat N, Diederich J (2005) Eclectic Rule-Extraction from Support Vector Machines. Int J Computational Intelligence 2(1):59-62.
Bond ZS, Stockmal V, Markus D, (2003) Sentence Durations and Accentedness Judgments. J Acoust Soc Am 113(4):2330-2331.
Caballero M, Moreno A, Nogueiras A (2006) Multidialectal Acoustic Modeling: a Comparative Study. In: Proc ITRW on Multilingual Speech and Language Processing, Stellenbosch, South Africa, paper 001, April 2006.
Chapelle O, Haffner P, Vapnik VN (1999) Support vector machines for histogram-based image classification: Vapnik-Chervonenkis (VC) learning theory and its applications. In: IEEE Transcactions on Neural Networks, vol. 10, no. 5, pp. 1055-1064, September 1999.
Craven MW, Shavlik JW (1994) Using Sampling and Queries to Extract Rules from Trained Neural Networks. In: Cohen WW, Hirsh H (eds) Machine Learning: Proceedings of the Eleventh International Conference. Morgan Kaufmann San Francisco pp. 37-45.
Crystal D (1997) English as a global language. Cambridge University Press, Cambridge New York.
Davis SB, Mermelstein P (1980) Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences. In: IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. ASSP-28, No. 4, August 1980.
Frid J (2002) Automatic classification of accent and dialect type: results from southern Swedish. In: Fonetic 2002 - TMH QPSR, vol. 43, pp. 89-92.
Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10): 906-914.
Golland P, Grimson WEL, Shenton ME, Kikinis R (2000) Small sample size learning for shape analysis of anatomical structures. In: Proc. MICCAI-00, Pittsburgh, PA, pp. 72-82, October 2000.
Gong Y, Treurniet WC (1993) Duration of Phones as Function of Utterance Length and its use in Automatic Speech Recognition. In: Proc Eurospeech-93, Berlin, Germany, pp. 315-318, September 1993.
Guo G, Li SZ (2003) Content-Based Audio Classification and Retrieval by Support Vector Machines. IEEE Transactions on Neural Networks 14(1):209-215.
Huang C, Chen T, Chang E (2004) Accent Issues in Large Vocabulary Continuous Speech Recognition. Int J Speech Technology 7:141-153.
Joachims T (1998) Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: ECML-98, 10th European Conference on Machine Learning, Heidelberg, Germany, pp. 137-142, April 1998.
Joachims T (1999) Making Large-Scale SVM Learning Practical. In: Schölkopf B, Burges C, Smola A (eds) Advances in Kernel Methods - Support Vector Learning, MIT Press.
Kumpf K, King RW (1996) Automatic accent classification of foreign accented. Australian English speech. In: Proc ICSLP 1996, Philadelphia, PA, pp. 1740- 1743, October 1996.
Lin X, Simske S (2004) Phoneme-less heirarchichal accent classification. In: Matthews MB (ed) Signals, Systems and Computers 2004; Conference Record of the Thirty-Eighth Asilomar Conference on. vol. 2:1801-1804.
Milner B (2002) A Comparison of Front-End Configurations for Robust speech Recognition. In: Proc. ICASSP 2002, Orlando Florida May 2002.
Milner B, Shao X, (2007) Prediction of Fundamental Frequency and Voicing from Mel-Frequency Cepstral Coefficients for Unconstrained Speech Reconstruction. IEEE Transactions on Audio, Speech and Language Processing 15(1): 24-33.
Mitsdorffer R, Diederich J, Tan CNW (2002) Rule Extraction from Technology IPOs in the US Stock Market. In: 9th International Conference on Neural Information Processing. 4th Asia-Pacific Conference on Simulated Evolution And Learning. 2002 International Conference on Fuzzy Systems and Knowledge Discover. Orchid Country Club, Singapore, 18 November-22 November 2002.
Munro MJ (1995) Non-segmental factors in foreign accent: Ratings of filtered speech. Studies in Second Language Acquisition 17:17-34.
Munro MJ, Derwing TM, Burgess CS (2003) The Detection of Foreign Accent in Backwards Speech. In: Sole M-J, Recasens De, Romero J (eds) Proceedings of the 15th International Congress of Phonetic Sciences, (Barcelona). Causal Productions Australia. pp. 535-538.
Quinlan JR (2007) Data Mining Tools See5 and C5.0, Rulequest Research (2007) http://rulequest.com/see5-info.html.
Pedersen C, Diederich J (2006) Listener Discrimination of Accent. In: Proc Human and Machine Speech Workshop, HCSNet Summerfest ’06, Sydney, Australia, p107, November-December 2006.
Pedersen C, Diederich J (2007) Accent Classification Using Support Vector Machines. In: Lee R, Chowdhury MU, Ray S, Lee T (eds) Proceedings 6th IEEE/ACIS International Conference on Computer and Information Science. Melbourne Australia, July 2007, pp. 444-449.
Tatham M, Morton K (2005) Developments in Speech Synthesis. Wiley, Chichester.
Teixeira C, Trancoso IM, Serralheiro A (1996) Accent Identification. In: Proc ICSLP 1996, Philadelphia, PA, pp. 1784-1787, October 1996.
van Els T, de Bot K (1987) The Role of Intonation in Foreign Accent. The Modern Language Journal 71(2):147-155.
Wells JC (1982) Accents of English: An Introduction. Cambridge University Press Cambridge New York.
Witten IH, Frank E (2005) “Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Pedersen, C., Diederich, J. (2008). Accent in Speech Samples: Support Vector Machines for Classification and Rule Extraction. In: Diederich, J. (eds) Rule Extraction from Support Vector Machines. Studies in Computational Intelligence, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75390-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-75390-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75389-6
Online ISBN: 978-3-540-75390-2
eBook Packages: EngineeringEngineering (R0)