Abstract
This paper mainly focuses on detection of repetitions in stuttered speech. The stuttered speech signal is divided into isolated units based on energy. Mel-frequency cepstrum coefficients (MFCCs), formants and shimmer are used as features for repetition recognition. These features are extracted from each isolated unit. Using Dynamic Time Warping (DTW) the features of each isolated unit are compared with those subsequent units within one second interval of speech. Based on the analysis of scores obtained from DTW a threshold is set, if the score is below the set threshold then the units are identified as repeated events. Twenty seven seconds of speech data used in this work, consists of 50 repetition events. The result shows that the combination of MFCCs, formants and shimmer can be used for the recognition of repetitions in stuttered speech. Out of 50 repetitions, 47 are correctly identified.
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
Riper, V.: The Nature of Stuttering. Prentice Hall, New Jersey (1971)
Kully, D., Boerg, E.: An investigation of inter-clinic agreement in the identification of fluent and stuttered syllables. J. Fluency Disord. 13, 309–318 (1988)
Conture, E.G.: Stuttering Englewood cliffs, New Jersey: Prentice-Hall, 2nd edn. (1990)
Zhang, J., Dong, B., Yan, Y.: A computer-assist algorithm to detect repetitive stuttering automatically. In: International Conference on Asian Language Processing, pp. 249–252 (2013)
Ravikumar, K.M., Balakrishna, R., Rajagopal, R., Nagaraj, H.C.: Automatic detection of syllable repetition in read speech for objective assessment of stuttered disfluencies. Proce. World Acad. Sci. 2, 220–223 (2008)
Palfy, J., Pospichal, J.: Recognition of repetitions using support vector machines. In: Signal Processing Algorithms, Architectures, Arrangements, and Applications Conference Proceedings (SPA), 2011, pp. 1–6 (2011)
Chee, L.S., Ai, O.C., Hariharan, M., Yaacob, S.: Automatic detection of prolongations and repetitions using LPCC. In: 2009 International Conference for Technical Postgraduates (TECHPOS). pp. 1–4 (2009)
Ai, O.C., Hariharan, M., Yaacob, S., Chee, L.S.: Classification of speech dysfluencies with MFCC and LPCC features. J. Med. Syst. 39, 2157–2165 (2012)
Ying, G.S., Mitchell, C.D., Jamieson, L.H.: Endpoint detection of isolated utterances based on a modified teager energy measurement. International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 732–735 (1993)
James, L.: MFCC tutorial. http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/
Welling, L., Ney, H.: Formant estimation for speech recognition. IEEE Transactions on Speech Audio Processing, vol. 6, pp. 36–48 (1998)
Li, X., Tao, J., Johnson, M.T., Soltis, J., Savage, A., Kirsten, M.L., Newman, J.D.: Stress and emotion classification using Jitter and Shimmer features. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2007, vol. 4., pp. IV–1081. IEEE (2007)
Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7, 358–386 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Ramteke, P.B., Koolagudi, S.G., Afroz, F. (2016). Repetition Detection in Stuttered Speech. In: Nagar, A., Mohapatra, D., Chaki, N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 43. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2538-6_63
Download citation
DOI: https://doi.org/10.1007/978-81-322-2538-6_63
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2537-9
Online ISBN: 978-81-322-2538-6
eBook Packages: EngineeringEngineering (R0)