Abstract
This paper presents techniques for objective characterisation of Automatic Speech-to-Phoneme Alignment (ASPA) systems, without the need for human-generated labels to act as a benchmark. As well as being immune to the effects of human variability, these techniques yield diagnostic information which can be helpful in the development of new alignment systems, ensuring that the resulting labels are as consistent as possible. To illustrate this, a total of 48 ASPA systems are used, including three front-end processors. For each processor, the number of states in each phoneme model, and of Gaussian distributions in each state mixture, are adjusted to generate a broad variety of systems. The results are compared using a statistical measure and a model-based Bayesian Monte-Carlo approach. The most consistent alignment system is identified, and is (as expected) in close agreement with typical “baseline” systems used in ASR research.
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
Baghai-Ravary, L.: Multi-dimensional Adaptive Signal Processing, with Application to Speech Recognition, Speech Coding and Image Compression. University of Sheffield PhD. Thesis (1995)
Beet, S.W., Gransden, I.R.: Interfacing an Auditory Model to a Parametric Speech Recogniser. Proc. Insititute of Acoustics 14(6), 321–328 (1992)
Chen, L., Liu, Y., Maia, E., Harper, M.: Evaluating Factors Impacting the Accuracy of Forced Alignments in a Multimodal Corpus. In: 4th International Conference on Language Resources and Evaluation (LREC), ELRA (2004)
Hutchinson, W., Knopoff, L.: The Acoustic Component of Western Consonance. Interface 7, 1–29 (1978)
Kochanski, G., et al.: Loudness Predicts Prominence; Fundamental Frequency Lends Little. J. Acoustical Society of America 11(2), 1038–1054 (2005)
Kochanski, G., Orphanidou, C.: Testing the Ecological Validity of Repetitive Speech. In: Proc. International Congress of Phonetic Sciences (ICPhS 2007), IPA (2007), http://www.icphs2007.de/conference/Papers/1632/1632.pdf
Kochanski, G., Rosner, B.S.: Bootstrap Markov Chain Monte Carlo and Optimal Solutions to The Law of Categorical Judgement (Corrected). Submitted to Behavior Research Methods (2010), http://arxiv.org/abs/1008.1596
Lander, T.: CSLU Labeling Guide, Center for Spoken Language Understanding, Oregon Graduate Institute (1997)
Ljolje, A., Riley, M.D.: Automatic Segmentation of Speech for TTS. In: Proc 3rd European Conference on Speech Communication and Technology (EUROSPEECH 1993), ESCA, pp. 1445–1448 (1993)
Moore, B.C.J., Glasberg, B.R.: Suggested Formulae for Calculating Auditory-Filter Bandwidths and Excitation Patterns. J. Acoustical Society of America 74(3), 750–753 (1983)
Sebestyen, G.S.: Decision-Making Processes in Pattern Recognition. ACM Monograph Series, pp. 40–47. MacMillan, Basingstoke (1962)
SoX Sound eXchange manual (2009), http://sox.sourceforge.net/sox.html
Young, S.J., et al.: The HTK Book (for HTK Version 3.4). Cambridge University Engineering Department (2009), http://htk.eng.cam.ac.uk/docs/docs.shtml
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Baghai-Ravary, L., Kochanski, G., Coleman, J. (2011). Data-Driven Approaches to Objective Evaluation of Phoneme Alignment Systems. In: Vetulani, Z. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2009. Lecture Notes in Computer Science(), vol 6562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20095-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-20095-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20094-6
Online ISBN: 978-3-642-20095-3
eBook Packages: Computer ScienceComputer Science (R0)