Abstract
The quality of life is negatively impacted when individuals are chronically unable to express themselves due to a lack of speech fluency. Stuttering, also referred to as stammering, is a condition that is exhibited in bad fluency of speech.
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
Similar content being viewed by others
References
Howell, P., Davis, S., Bartrip, J.: The university college london archive of stuttered speech (uclass). J. Speech Lang. Hear. Res. (2009)
Liu, Y., Shriberg, E., Stolcke, A., Harper, M.: Comparing hmm, maximum entropy, and conditional random fields for disfluency detection. In: Ninth European Conference on Speech Communication and Technology (2005)
Fook, C.Y., Muthusamy, H., Chee, L.S., Yaacob, S.B., Adom, A.H.B.: Comparison of speech parameterization techniques for the classification of speech disfluencies. Turk. J. Electr. Eng. Comput. Sci. 21(1), 1983–1994 (2013)
Hariharan, M., Chee, L.S., Ai, O.C., Yaacob, S.: Classification of speech dysfluencies using LPC based parameterization techniques. J. Med. Syst. 36(3), 1821–1830 (2012)
Chee, L.C., Ai, O.C., Yaacob, S.: Overview of automatic stuttering recognition system. In: Proc. International Conference on Man-Machine Systems, no. October, Batu Ferringhi, Penang Malaysia, pp. 1–6 (2009)
Honal, M., Schultz, T.: Correction of disfluencies in spontaneous speech using a noisy-channel approach. In: Eighth European Conference on Speech Communication and Technology (2003)
Honal, M., Schultz, T.: Automatic disfluency removal on recognized spontaneous speech-rapid adaptation to speaker-dependent disfluencies. In: Proceedings (ICASSP’05). IEEE International Conference on Acoustics, Speech, and Signal Processing (2005)
Lease, M., Johnson, M., Charniak, E.: Recognizing disfluencies in conversational speech. IEEE Trans. Audio Speech Lang. Process. 14(5), 1566–1573 (2006)
Ai, O.C., Hariharan, M., Yaacob, S., Chee, L.S.: Classification of speech dysfluencies with MFCC and LPCC features. Expert Syst. Appl. 39(2), 2157–2165 (2012)
Km, R.K., Ganesan, S.: Comparison of multidimensional MFCC feature vectors for objective assessment of stuttered disfluencies. Int. J. Adv. Netw. Appl. 2(05), 854–860 (2011)
Ravikumar, K., Rajagopal, R., Nagaraj, H.: An approach for objective assessment of stuttered speech using MFCC. In: The international congress for global science and technology, p. 19 (2009)
Świetlicka, I., Kuniszyk-Jóźkowiak, W., Smołka, E.: Hierarchical ANN system for stuttering identification. Comput. Speech Lang. 27(1), 228–242 (2013)
Arbajian, P., Hajja, A., Raś, Z.W., Wieczorkowska, A.A.: Segment-removal based stuttered speech remediation. In: International workshop on new frontiers in mining complex patterns, pp. 16–34. Springer (2017)
Arbajian, P., Hajja, A., Raś, Z.W., Wieczorkowska, A.A.: Effect of speech segment samples selection in stutter block detection and remediation. J. Intell. Inform. Syst. 53(2), 241–264 (2019)
Boersma, P.: Praat, a system for doing phonetics by computer. Glot. Int. 5(9), 341–345 (2001)
Boersma, P.: Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In: Proceedings of the institute of phonetic sciences, vol. 17, no. 1193, pp. 97–110. Amsterdam (1993)
Czyzewski, A., Kaczmarek, A., Kostek, B.: Intelligent processing of stuttered speech. J. Intell. Inform. Syst. 21(2), 143–171 (2003)
Hajja, A., Hiers, G.P., Arbajian, P., Raś, Z.M., Wieczorkowska, A.A.: Multipurpose web-platform for labeling audio segments efficiently and effectively. In: International Symposium on Methodologies for Intelligent Systems, pp. 179–188. Springer (2018)
Snover, M., Dorr, B., Schwartz, R.: A lexically-driven algorithm for disfluency detection. In: Proceedings of HLT-NAACL 2004: Short Papers
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)
Ferguson, J., Durrett, G., Klein, D.: Disfluency detection with a semi-markov model and prosodic features. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 257–262 (2015)
Huang, Z., Chen, L., Harper, M.: An open source prosodic feature extraction tool. In: Proceedings of the language resources and evaluation conference (LREC) (2006)
El Ayadi, M., Kamel, M.S., Karray, F.: Survey on speech emotion recognition: features, classification schemes, and databases. Pattern Recognit. 44(3), 572–587 (2011)
Scherer, S., Siegert, I., Bigalke, L., Meudt, S.: Developing an expressive speech labeling tool incorporating the temporal characteristics of emotion. In: LREC (2010)
Tumanova, V., Zebrowski, P.M., Throneburg, R.N., Kayikci, M.E.K.: Articulation rate and its relationship to disfluency type, duration, and temperament in preschool children who stutter. J. Commun. Disord. 44(1), 116–129 (2011)
Kim, S., Georgiou, P.G., Lee, S., Narayanan, S.: Real-time emotion detection system using speech: Multi-modal fusion of different timescale features. In: Multimedia Signal Processing: MMSP 2007, pp. 48–51. IEEE (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Hajja, A., Arbajian, P. (2021). Stutter Detection and Remediation in Speech. In: Ras, Z.W., Wieczorkowska, A., Tsumoto, S. (eds) Recommender Systems for Medicine and Music. Studies in Computational Intelligence, vol 946. Springer, Cham. https://doi.org/10.1007/978-3-030-66450-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-66450-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66448-0
Online ISBN: 978-3-030-66450-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)