Abstract
This works concerns the problem of the application of artificial neural networks in the modelling of the hearing process. The aim of the research was to answer the question whether artificial neural networks are able to evaluate speech rate. Speech samples, first recorded during reading of a story with normal and next with slow articulation rate were used as research material. The experiment proceeded in two phases. In the first stage Kohonen network was used. The purpose of that network was to reduce the dimensions of the vector describing the input signals and to obtain the amplitude-time relationship. As a result of the analysis, an output matrix consisting of the neurons winning in a particular time frame was received. The matrix was taken as input for the following networks in the second phase of the experiment. Various types of artificial neural networks were examined with respect to their ability to classify correctly utterances with different speech rates into two groups. Good examination results were accomplished and classification correctness exceeded 88%.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Koopmans-vanBeinum FJ, vanDonzel ME (1996) Relationship between discourse structure and dynamic speech rate. In: Bunnell HT, Idsardi W (Eds), Proceedings ICSLP96, Vol 3, 1724–1727
Zheng J, Franco H, Stolcke A (2000) Rate-dependent acoustic modeling for large vocabulary conversational speech recognition. In: Proceeding ISCA Tutorial and Research Workshop on Automatic Speech Recognition: Challenges for the new Millennium, Paris, France, 145–149
Ververidis D, Kotropoulos C (2006) Emotional speech recognition: Resources, features and methods. Speech Communication 48, 1162–1181
Verhasselt JP Martens JP (1996) A fast and reliable rate of speech detector, Proceedings ICSLP96, Vol 3, 2258–2261
Guitar B, Kopff-Schaefer H, Donahu-Kilburg G, Bond L (1992) Parent verbal interactions and speech rate: A case study in stuttering. Journal of Speech and Hearing Research 35, 742–754
Howell P, Sackin S (2000) Speech rate modification and its effects on fluency reversal in fluent speakers and people who stutter. Journal of Developmental and Physical Disabilities 12(4)
Chen WY, Chen SH, Lin CHJ (1996) A speech recognition method based on the sequential Multi-layer Perceptrons. Neural Networks 9(4), 655–669
Farrell K, Mamione R, Assaleh K (1994) Speaker recognition using neural networks and conventional classifiers. IEEE Transaction on Speech and Audio Processing 2(1), part 2, 194–205
Kohonen T (2001) Self-Organizing Maps. Springer, Berlin, Heidelberg, New York
Kestler HA, Schwenker F (2000) Classification of high-resolution ECG signals, In Howlett R, Jain L Radial basis function neural networks: theory and applications. Heidelberg: Physica-Verlag
Schwenker F, Kestler HA, Palm G (2001) Three learning phases for radial-basisfunction networks. Neural Networks 14, 439–458.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Szczurowska, I., Kuniszyk-Jóźkowiak, W., Smołka, E. (2007). Articulation Rate Recognition by Using Artificial Neural Networks. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds) Computer Recognition Systems 2. Advances in Soft Computing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75175-5_95
Download citation
DOI: https://doi.org/10.1007/978-3-540-75175-5_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75174-8
Online ISBN: 978-3-540-75175-5
eBook Packages: EngineeringEngineering (R0)