A preliminary demonstration of exemplar-based voice conversion for articulation disorders using an individuality-preserving dictionary

Eurasip Journal on Audio, Speech, and Music Processing

Volumn 2014, Issue , 2014, Pages

  Aihara, Ryo ^a   Takashima, Ryoichi ^a   Takiguchi, Tetsuya ^a   Ariki, Yasuo ^a  

^a KOBE UNIVERSITY   (Japan)

Author keywords

Articulation disorders; Assistive technologies; NMF; Voice conversion; Voice reconstruction

Indexed keywords

LINGUISTICS;

ASSISTIVE TECHNOLOGY; CEREBRAL PALSY; EXEMPLAR-BASED; NMF; NONNEGATIVE MATRIX FACTORIZATION; SPECTRAL CONVERSION; TARGET SPEAKER; VOICE CONVERSION;

SPEECH PROCESSING;

EID: 84901801701     PISSN: 16874714     EISSN: 16874722     Source Type: Journal    
DOI: 10.1186/1687-4722-2014-5     Document Type: Article

References (30)

1. J Lin, W Ying, TS Huang, Capturing human hand motion in image sequences, in IEEE Workshop on Motion and Video Computing, Orlando, 5-6 Dec 2002 (IEEE, Piscataway, 2002), pp. 99-104
2. T Starner, J Weaver, A Pentland, Real-time American sign language recognition using desk and wearable computer based video. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1371-1375 (1998) (Pubitemid 128741382)
3. G Fang, W Gao, D Zhao, Large vocabulary sign language recognition based on hierarchical decision trees. 5th International Conference on Multimodal Interfaces. 34(3), 125-131 (2004)
4. N Ezaki, M Bulacu, L Schomaker, Text detection from natural scene images: towards a system for visually impaired persons. Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004). 2, 683-686 (2004)
5. MK Bashar, T Matsumoto, Y Takeuchi, H Kudo, N Ohnishi, Unsupervised texture segmentation via wavelet-based locally orderless images (WLOIs) and SOM, in Computer Graphics and Imaging (IASTED/ACTA Press, Calgary, 2003), pp. 279-284
6. V Wu, R Manmatha, EM Riseman, Textfinder: an automatic system to detect and recognize text in images. IEEE Trans. Pattern Anal. Mach. Intell. 21(11), 1224-1229 (1999) (Pubitemid 32211200)
7. K Yabu, T Ifukube, S Aomura, A basic design of wearable speech synthesizer for voice disorders [Japanese]. EIC Technical report (Institute Electron. Inf. Commun. Eng). 105(686), 59-64 (2006)
8. ST Canale, WC Campbell, Campbell's Operative Orthopaedics, vol. 4 (Mosby-Year Book, St. Louis, 2002)
9. H Matsumasa, T Takiguchi, Y Ariki, I Li, T Nakabayachi, Integration of metamodel and acoustic model for dysarthric speech recognition. J. Multimedia. 4(4), 254-261 (2009)
10. C Miyamoto, Y Komai, T Takiguchi, Y Ariki, I Li, Multimodal speech recognition of a person with articulation disorders using AAM and MAF, in IEEE International Workshop on Multimedia Signal Processing (MMSP'10), St. Malo, 4-6 Oct 2010 (IEEE, Piscataway, 2010), pp. 517-520
11. A Maier, T Haderlein, F Stelzle, E Noth, E Nkenke, F Rosanowski, A Schutzenberger, M Schuster, Automatic speech recognition systems for the evaluation of voice and speech disorders in head and neck cancer. EURASIP J. Audio Speech Music Process. 2010, 926951 (2010)
12. D Lee, HS Seung, Algorithms for non-negative matrix factorization, in Advances in Neural Information Processing 13 (NIPS 2000) (MIT Press, Massachusetts, 2001), pp. 556-562
13. T Virtanen, Monaural sound source separation by non-negative matrix factorization with temporal continuity and sparseness criteria. IEEE Trans. Audio Speech Lang. Process. 15(3), 1066-1074 (2007)
14. JF Gemmeke, T Virtanen, Noise robust exemplar-based connected digit recognition, in 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Dallas, 14-19 March 2010 (IEEE, Piscataway, 2010), pp. 4546-4549
15. MN Schmidt, RK Olsson, Single-channel speech separation using sparse non-negative matrix factorization, in Interspeech 2006-ICSLP, Ninth International Conference on Spoken Language Processing, Pittsburgh, 17-21 Sept 2006 (Curran Associates, Inc., New York, 2006), pp. 2614-2617
16. T Toda, A Black, K Tokuda, Voice conversion based on maximum likelihood estimation of spectral parameter trajectory. IEEE Trans. Audio Speech Lang. Process. 15(8), 2222-2235 (2007)
17. Y Iwami, T Toda, H Saruwatari, K Shikano, GMM-based voice conversion applied to emotional speech synthesis. IEEE Trans. Speech Audio Process. 7, 2401-2404 (1999)
18. R Aihara, R Takashima, T Takiguchi, Y Ariki, GMM-Based emotional voice conversion using spectrum and prosody features. Am. J. Signal Process. 2(5), 135-138 (2012)
19. Y Stylianou, O Cappe, E Moulines, Continuous probabilistic transform for voice conversion. IEEE Trans. Speech Audio Process. 6(2), 131-142 (1998) (Pubitemid 128720639)
20. E Helander, T Virtanen, J Nurminen, M Gabbouj, Voice conversion using partial least squares regression. IEEE Trans. Audio Speech Lang. Process. 18(5), 912-921 (2010)
21. CH Lee, CH Wu, Map-based adaptation for speech conversion using adaptation data selection and non-parallel training, in Interspeech 2006-ICSLP, Ninth International Conference on Spoken Language Processing, Pittsburgh, 17-21 Sept 2006 (Curran Associates, Inc., New York, 2006), pp. 2254-2257
22. T Toda, Y Ohtani, K Shikano, Eigenvoice conversion based on Gaussian mixture model, in Interspeech 2006-ICSLP, Ninth International Conference on Spoken Language Processing, Pittsburgh, 17-21 Sept 2006 (Curran Associates, Inc., New York, 2006), pp. 2446-2449
23. K Nakamura, T Toda, H Saruwatari, K Shikano, Speaking-aid systems using GMM-based voice conversion for electrolaryngeal speech. Speech Commun. 54(1), 134-146 (2012)
24. K Nakamura, T Toda, H Saruwatari, K Shikano, Speaking aid system for total laryngectomees using voice conversion of body transmitted artificial speech, in Interspeech 2006-ICSLP, Ninth International Conference on Spoken Language Processing, Pittsburgh, 17-21 Sept 2006 (Curran Associates, Inc., New York, 2006), pp. 1395-1398
25. C Veaux, J Yamagishi, S King, Using HMM-based speech synthesis to reconstruct the voice of individuals with degenerative speech disorders, in 13th Annual Conference of the International Speech Communication Association 2012 (INTERSPEECH 2012), Portland, 9-13 September 2012 (Curran Associates, Inc. New York, 2012), pp. 966-969
26. H Kawahara, I Masuda-Katsuse, A Cheveigne, Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency based F0 extraction: possible role of a repetitive structure in sounds. Speech Commun. 27(3-4), 187-207 (1999)
27. JF Gemmeke, T Viratnen, A Hurmalainen, Exemplar-based sparse representations for noise robust automatic speech recognition. IEEE Trans. Audio Speech Lang. Process. 19(7), 2067-2080 (2011)
28. A Kurematsu, K Takeda, Y Sagisaka, S Katagiri, H Kuwabara, K Shikano, ATR Japanese speech database as a tool of speech recognition and synthesis. Speech Commun. 9, 357-363 (1990)
29. J Kominek, T Schultz, AW Black, Synthesizer voice quality of nwe languages calibrated with mean mel cepstral distortion, in The International Workshop on Spoken Language Technology for Under-Resourced Languages (SLTU) (Hanoi University of Technology, Hanoi, 5-7 May 2008)
30. International Telecommunication Union, ITU-T Recommendation P.800-P.899: Methods for Objective and Subjective Assessment of Quality (ITU, Geneva, 2003)