SVM-based detection of misannotated words in read speech corpora

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8082 LNAI, Issue , 2013, Pages 457-464

  Matoušek, Jindřich ^a   Tihelka, Daniel ^a  

^a UNIVERSITY OF WEST BOHEMIA   (Czech Republic)

Author keywords

annotation error detection; classification; read speech corpora; support vector machine

Indexed keywords

ANNOTATION ERRORS; AUTOMATIC DETECTION; DETECTION METHODS; FEATURE SETS; PRECISION AND RECALL; SPEECH CORPORA; SVM CLASSIFIERS;

CLASSIFICATION (OF INFORMATION); SPEECH; SUPPORT VECTOR MACHINES;

SPEECH RECOGNITION;

EID: 84884955838     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40585-3_58     Document Type: Conference Paper

References (21)

1. Matoušek, J., Tihelka, D., Šmídl, L.: On the impact of annotation errors on unit-selection speech synthesis. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2012. LNCS, vol. 7499, pp. 456-463. Springer, Heidelberg (2012)
2. Matoušek, J., Romportl, J.: Recording and Annotation of Speech Corpus for Czech Unit Selection Speech Synthesis. In: Matoušek, V., Mautner, P. (eds.) TSD 2007. LNCS (LNAI), vol. 4629, pp. 326-333. Springer, Heidelberg (2007)
3. Adell, J., Agüero, P.D., Bonafonte, A.: Database pruning for unsupervised building of text-to-speech voices. In: Proc. ICASSP, Toulouse, France, pp. 889-892 (2006)
4. Tachibana, R., Nagano, T., Kurata, G., Nishimura, M., Babaguchi, N.: Preliminary experiments toward automatic generation of new TTS voices from recorded speech alone. In: Proc. INTERSPEECH, Antwerp, Belgium, pp. 1917-1920 (2007)
5. Wei, S., Hu, G., Hu, Y., Wang, R.H.: A new method for mispronunciation detection using support vector machine based on pronunciation space models. Speech Commun. 51(10), 896-905 (2009)
6. Kominek, J., Black, A.: Impact of durational outlier removal from unit selection catalogs. In: Proc. SSW, Pittsburgh, USA, pp. 155-160 (2004)
7. Lu, H.,Wei, S., Dai, L.,Wang, R.H.: Automatic error detection for unit selection speech synthesis using log likelihood ratio based SVM classifier. In: Proc. INTERSPEECH, Makuhari, Japan, pp. 162-165 (2010)
8. Wang, W.Y., Georgila, K.: Automatic detection of unnatural word-level segments in unitselection speech synthesis. In: Proc. ASRU, Hawaii, USA, pp. 289-294 (2011)
9. Tihelka, D., Kala, J., Matoušek, J.: Enhancements of Viterbi search for fast unit selection synthesis. In: Proc. INTERSPEECH, Makuhari, Japan, pp. 174-177 (2010)
10. Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.: HTK Book (for HTK Version 3.4). The Cambridge University, Cambridge (2006)
11. Matoušek, J., Tihelka, D., Psutka, J.V.: Experiments with Automatic Segmentation for Czech Speech Synthesis. In: Matoušek, V., Mautner, P. (eds.) TSD 2003. LNCS (LNAI), vol. 2807, pp. 287-294. Springer, Heidelberg (2003) (Pubitemid 37171194)
12. Matoušek, J., Romportl, J.: Automatic pitch-synchronous phonetic segmentation. In: Proc. INTERSPEECH, Brisbane, Australia, pp. 1626-1629 (2008)
13. Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10, 1895-1923 (1998)
14. Cortes, C., Vapnik, V.: Support-vector networks. Machine Leaming 20(3), 273-279 (1995)
15. Matoušek, J., Tihelka, D.: Annotation errors detection in TTS corpora. In: Proc. Interspeech, Lyon, France (2013)
16. Romportl, J., Kala, J.: Prosody modelling in Czech text-to-speech synthesis. In: Proc. SSW, Bonn, Germany, pp. 200-205 (2007)
17. Taylor, P., Caley, R., Black, A., King, S.: Edinburgh speech tools library: System documentation (1999), http://www.cstr.ed.ac.uk/projects/speech- tools/manual-1.2.0/
18. Pedregosa, F., Varoquaux, G., Gramfort, A., Thirion, V.M.B., Grisel, O., Blondel, M., Prettenhofer, P.,Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perror, M.: Édouard Duchesnay: Scikit-learn: Machine learning in Python. J. Machine Learn. Res. 12, 2825-2830 (2011)
19. Přibil, J., Přibilová, A.: Comparison of spectral and prosodic parameters of male and female emotional speech in Czech and Slovak. In: Proc. ICASSP, Prague, Czech Republic, pp. 4720-4723 (2011)
20. Ircing, P., Psutka, J., Psutka, J.V.: Using morphological information for robust language modeling in Czech ASR system. IEEE Trans. Audio Speech Lang. Process. 17, 840-847 (2009)
21. Psutka, J., Švec, J., Psutka, J.V., Vaněk, J., Pražák, A., Šmídl, L., Ircing, P.: System for fast lexical and phonetic spoken term detection in a Czech cultural heritage archive. EURASIP J. Audio Speech Music Process. 10 (2011)