Abstract
As technology improves, people around the world are given more effective tools to communicate with each other. This has caused a sensation of secondary language learning. Many countries have now included this as an obligatory component of their education systems. However, the lack of appointing right professionals has led to misleading the practicing the pronunciation of the new language, because students often follow the pronunciation that non-native teachers have. This paper aims to provide a model that has a potential to help learners with increasing the recipient for understanding the speaker. The model records the learner’s English pronunciation of a given context, analyses it and provides feedback on the screen. The system has shown an accuracy of 98.3%. Throughout the research we have discovered that several factors such as the learner’s predefined accent from his mother-tongue language, the noise level of an environment where the learner uses the system as well as different types of English accents interfere with providing accurate feedback to the learner.
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
References
McCrocklin S (2016) Pronunciation learner autonomy: the potential of automatic speech recognition. System 57:25–42
Foote J (2010) Second language Learners’ perceptions of their own recorded speech, Edmonton: PMC working paper series
Kruk M (2012) Using online resources in the development of learner autonomy and english pronunciation: the case of individual Learners. J Second Lang Teach Res 1(2):113–142
Barbosa F, Silva W (2015) Support vector machines, Mel-Frequency Cepstral coefficients and the discrete cosine transform applied on voice based biometric authentication. In: 2015 SAI intelligent systems conference (IntelliSys), pp 1032–1039
Neri A, Cucchiarini C, Strik W (2003) Automatic speech recognition for second language learning: how and why it actually works. In: International congress of phonetic sciences, pp 1157–1160. International congress of phonetic sciences, Barcelona
Hincks R (2003) Speech technologies for pronunciation feedback and evaluation. ReCALL 15
Gu L, Harris J (2003) SLAP: a system for the detection and correction of pronunciation for second language acquisition. In: International symposium on circuits and systems. Bangkok, pp 580–583
Practical Cryptography, http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/#eqn1. Accessed 31 Oct 2017
Du Y (2013) Biometrics. Pan Stanford Publishing Pte Ltd, Singapore
Recurrent neural networks tutorial part 1—introduction to RNNs (2017). http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/. Accessed 31 Oct 2017
Hansen J, Hasan T (2015) Speaker recognition by machines and humans: a tutorial review. IEEE Signal Process Mag 32:74–99
Graves A, Jaitly N (2014) Towards end-to-end speech recognition with recurrent neural networks. In: Proceedings of the 31st international conference on machine learning, PMLR, pp 1764–1772
Chen S, Luo Y (2009) Speaker verification using MFCC and support vector machine. In: Proceedings of the International multiconference of engineers and computer scientists, pp 532–535. Proceedings of the international multiconference of engineers and computer scientists, Hong Kong (2009)
Downey A (2016) Think DSP. O’Reily Media
Probst K, Ke Y, Eskenazi M (2002) Enhancing foreign language tutors—In search of the golden speaker. Speech Commun 37:161–173
Rabiner L, Schafer R (2011) Theory and applications of digital speech processing. Pearson/Prentice Hall, Upper Saddle River [etc.]
Zhang F, Yin P (2009) A study of pronunciation problems of english learners in China. Asian Soc Sci 5
Moustroufas N, Digalakis V (2007) Automatic pronunciation evaluation of foreign speakers using unknown text. Comput Speech Lang 21:219–230
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Byun, J., van der Haar, D. (2019). Pronunciation Detection for Foreign Language Learning Using MFCC and SVM. In: Kim, K., Baek, N. (eds) Information Science and Applications 2018. ICISA 2018. Lecture Notes in Electrical Engineering, vol 514. Springer, Singapore. https://doi.org/10.1007/978-981-13-1056-0_34
Download citation
DOI: https://doi.org/10.1007/978-981-13-1056-0_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1055-3
Online ISBN: 978-981-13-1056-0
eBook Packages: EngineeringEngineering (R0)