Abstract
In this paper, a novel variant of an automatic phonetic segmentation procedure is presented, especially useful if data is scarce. The procedure uses the Kaldi speech recognition toolkit as its basis, and combines and modifies several existing methods and Kaldi recipes. Both the specifics of model training and test data alignment are explained in detail. Effectiveness of artificial extension of the starting amount of manually labeled material during training is examined as well. Experimental results show the admirable overall correctness of the proposed procedure in the given test environment. Several variants of the procedure are compared, and the usage of speaker-adapted context-dependent triphone models trained without the expanded manually checked data is proven to produce the best results. A few ways to improve the procedure even more, as well as future work, are also discussed.
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 subscriptionsReferences
Brognaux, S., Roekhaut, S., Drugman, T., Beaufort, R.: Train&Align: a new online tool for automatic phonetic alignment. In: Spoken Language Technology Workshop (SLT), pp. 416–421. IEEE Signal Processing Society (2012)
Scharenborg, O., Ernestus, M., Wan, V.: Segmentation of speech: child’s play? In: 8th Annual Conference of the International Speech Communication Association (INTERSPEECH), Antwerp, pp. 1953–1956 (2007)
Esposito, A., Aversano, G.: Text independent methods for speech segmentation. In: Chollet, G., Esposito, A., Faundez-Zanuy, M., Marinaro, M. (eds.) Nonlinear Speech Modeling. LNCS (LNAI), vol. 3445, pp. 261–290. Springer, Heidelberg (2005)
Leow, S.J., Chng, E.S., Lee, C.H.: Language-resource independent speech segmentation using cues from a spectrogram image. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, pp. 5813–5817 (2015)
Priyadarsini, S., Kumar, A.: Automatic speech segmentation in syllable centric speech recognition system. J. Speech Technol. 19(1), 9–18 (2016)
Almpanidis, G., Kotti, M., Kotropoulos, C.: Robust detection of phone boundaries using model selection criteria with few observations. IEEE Trans. Audio Speech Lang. Process. 17(2), 287–298 (2009). IEEE Signal Processing Society
Bigi, B.: SPPAS: a tool for the phonetic segmentations of speech. In: 8th International Conference on Language Resources and Evaluation (LREC), Istanbul, pp. 1748–1755 (2012)
Boeffard, O., Charonnat, L., Le Maguer, S., Lolive, D., Vidal, G.: Towards fully automatic annotation of audio books for TTS. In: 8th International Conference on Language Resources and Evaluation (LREC), Instanbul, pp. 975–980 (2012)
Brognaux, S., Drugman, T.: HMM-based speech segmentation: improvements of fully automatic approaches. IEEE/ACM Trans. Audio Speech Lang. Process. 24(1), 5–15 (2016). IEEE Signal Processing Society
Hoffmann, S., Pfister, B.: Fully automatic segmentation for prosodic speech corpora. In: 10th Annual Conference of the International Speech Communication Association (INTERSPEECH), Makuhari, pp. 1389–1392 (2010)
Hoffmann, S., Pfister, B.: Text-to-speech alignment of long recordings using universal phone models. In: 14th Annual Conference of the International Speech Communication Association (INTERSPEECH), Lyon, pp. 1520–1524 (2013)
Matoušek, J.: Automatic pitch-synchronous phonetic segmentation with context-independent HMMs. In: Matoušek, V., Mautner, P. (eds.) TSD 2009. LNCS, vol. 5729, pp. 178–185. Springer, Heidelberg (2009)
Stan, A., Mamiya, Y., Yamagishi, J., Bell, P., Watts, O., Clark, R.A.J., King, S.: ALISA: an automatic lightly supervised speech segmentation and alignment tool. J. Comput. Speech Lang. 35, 116–133 (2016)
Adell, J., Bonafonte, A., Gomez, J., Castro, M.: Comparative study of automatic phone segmentation methods for TTS. In: 30th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Philadelphia, pp. 309–312 (2005)
Toledano, D., Gomez, L., Grande, L.: Automatic phonetic segmentation. IEEE Trans. Speech Audio Process. 11(6), 617–625 (2003). IEEE Signal Processing Society
Wang, L., Zhao, Y., Chu, M., Zhou, J., Cao, Z.: Refining segmental boundaries for TTS database using fine contextual-dependent boundary models. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, pp. 641–644 (2004)
Brugnara, F., Falavigna, D., Omologo, M.: Automatic segmentation and labeling of speech based on hidden Markov models. J. Speech Commun. 12(4), 357–370 (1993)
Appen, Product Catalog. http://catalog.appenbutlerhill.com/
Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlícek, P., Qian, Y., Schwarz, P., Silovský, J., Stemmer, G., Veselý, K.: The kaldi speech recognition toolkit. In: IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 1–4. IEEE Signal Processing Society (2011)
Acknowledgments
This research was supported in part by the Ministry of Education, Science and Technological Development of the Republic of Serbia, under Grant No. TR32035. The authors are grateful to the company “Speech Morphing, Inc.” from Campbell, CA, USA, for providing the speech corpora for the experiments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Pakoci, E., Popović, B., Jakovljević, N., Pekar, D., Yassa, F. (2016). A Phonetic Segmentation Procedure Based on Hidden Markov Models. In: Ronzhin, A., Potapova, R., Németh, G. (eds) Speech and Computer. SPECOM 2016. Lecture Notes in Computer Science(), vol 9811. Springer, Cham. https://doi.org/10.1007/978-3-319-43958-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-43958-7_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43957-0
Online ISBN: 978-3-319-43958-7
eBook Packages: Computer ScienceComputer Science (R0)