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• Speech Completion: Introducing New Modality into Speech Input Interface

  Goto, M., Itou, K., Akiba, T. & Hayamizu, S., 2002 Jan, In: Computer Software. 19, 4, p. 254-265 12 p.

  Research output: Contribution to journal › Article › peer-review

• SPEECH CONVERSATION SYSTEM OF THE MUSICIAN ROBOT.

  Kobayashi, T., Komori, Y., Hashimoto, N., Iwata, K., Fukazawa, Y. & Shirai, K., 1985 Dec 1, p. 483-488. 6 p.

  Research output: Contribution to conference › Paper › peer-review

  • Robots 100%
  • Deterioration 35%
  • Interpolation 33%
  4 Citations (Scopus)

• Speech database and acoustic-phonetic knowledge base for speech recognition

  Tanaka, K. & Hayamizu, S., 1993, In: Denshi Gijutsu Sogo Kenkyusho Iho/Bulletin of the Electrotechnical Laboratory. 57, 2, p. 48-62 15 p.

  Research output: Contribution to journal › Article › peer-review

  • speech recognition 100%
  • phonetics 97%
  • Speech analysis 77%
  • Speech recognition 61%
  • Acoustics 53%

• Speech dialogue management system for human interface employing visual anthropomorphous agent

  Hiramoto, Y., Dohi, H. & Ishizuka, M., 1994, Robot and Human Communication - Proceedings of the IEEE International Workshop. Piscataway, NJ, United States: IEEE, p. 277-282 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Cathode ray tubes 100%
  • Brain 67%
  • Communication 40%
  5 Citations (Scopus)

• Speech emotional features measured by power-law distribution based on electroglottography

  Chen, L., Mao, X., Xue, Y. & Ishizuka, M., 2012, BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing. p. 131-136 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Support vector machines 100%
  • Experiments 44%
  1 Citation (Scopus)

• Speech enhancement and recognition using multi-task learning of long short-term memory recurrent neural networks

  Chen, Z., Watanabe, S., Erdogan, H. & Hershey, J. R., 2015, In: Unknown Journal. 2015-January, p. 3274-3278 5 p.

  Research output: Contribution to journal › Article › peer-review

  • Speech Enhancement 100%
  • Automatic Speech Recognition 98%
  • Multi-task Learning 90%
  • Speech Recognition 80%
  • Recurrent Neural Networks 76%
  76 Citations (Scopus)

• Speech Enhancement Based on Bayesian Low-Rank and Sparse Decomposition of Multichannel Magnitude Spectrograms

  Bando, Y., Itoyama, K., Konyo, M., Tadokoro, S., Nakadai, K., Yoshii, K., Kawahara, T. & Okuno, H. G., 2018 Feb 1, In: IEEE/ACM Transactions on Audio Speech and Language Processing. 26, 2, p. 215-230 16 p.

  Research output: Contribution to journal › Article › peer-review

  • Speech Enhancement 100%
  • Spectrogram 82%
  • spectrograms 68%
  • Speech enhancement 65%
  • Microphones 55%
  13 Citations (Scopus)

• Speech enhancement based on multiple directivity patterns using a microphone array

  Sekiya, T. & Kobayashi, T., 2004 Sep 28, In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 1, p. I877-I880

  Research output: Contribution to journal › Conference article › peer-review

  • Speech enhancement 100%
  • Microphones 84%
  • Speech recognition 25%
  • Acoustic waves 21%
  1 Citation (Scopus)

• Speech enhancement based on the subspace method

  Asano, F., Hayamizu, S., Yamada, T. & Nakamura, S., 2000 Sep, In: IEEE Transactions on Speech and Audio Processing. 8, 5, p. 497-507 11 p.

  Research output: Contribution to journal › Article › peer-review

  • Speech enhancement 100%
  • Microphones 84%
  • Signal processing 65%
  • eigenvalues 51%
  • augmentation 49%
  92 Citations (Scopus)

• Speech enhancement using array signal processing based on the coherent-subspace method

  Asano, F. & Hayamizu, S., 1997, In: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. E80-A, 11, p. 2276-2285 10 p.

  Research output: Contribution to journal › Article › peer-review

  • Speech Enhancement 100%
  • Array Signal Processing 98%
  • Subspace Methods 70%
  • Speech enhancement 65%
  • Microphones 55%
  5 Citations (Scopus)

• Speech enhancement using a square microphone array in the presence of directional and diffuse noise

  Ogawa, T., Takada, S., Akagiri, K. & Kobayashi, T., 2010 May, In: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. E93-A, 5, p. 926-935 10 p.

  Research output: Contribution to journal › Article › peer-review

  • Speech Enhancement 96%
  • Speech enhancement 63%
  • Mobile Devices 56%
  • Microphones 53%
  • Mobile computing 43%
  3 Citations (Scopus)

• Speech enhancement using CSS-based array processing

  Asano, F. & Hayamizu, S., 1997, In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2, p. 1191-1194 4 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Array processing 100%
  • Speech enhancement 94%
  • Direction of arrival 84%
  • Microphones 79%
  • Noise abatement 78%
  1 Citation (Scopus)

• Speech enhancement using end-to-end speech recognition objectives

  Subramanian, A. S., Wang, X., Baskar, M. K., Watanabe, S., Taniguchi, T., Tran, D. & Fujita, Y., 2019 Oct, 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2019. Institute of Electrical and Electronics Engineers Inc., p. 234-238 5 p. 8937250. (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics; vol. 2019-October).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Speech enhancement 100%
  • Speech recognition 75%
  • Beamforming 26%
  • Signal reconstruction 17%
  • Reinforcement learning 13%
  7 Citations (Scopus)

• Speech enhancement using non-negative spectrogram models with mel-generalized cepstral regularization

  Li, L., Kameoka, H., Toda, T. & Makino, S., 2017, In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. 2017-August, p. 1998-2002 5 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Speech Enhancement 100%
  • Spectrogram 82%
  • Speech enhancement 65%
  • Enhancement 54%
  • Factorization 52%
  1 Citation (Scopus)

• Speech Enhancement Using Self-Adaptation and Multi-Head Self-Attention

  Koizumi, Y., Yatabe, K., Delcroix, M., Masuyama, Y. & Takeuchi, D., 2020 May, 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 181-185 5 p. 9053214. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; vol. 2020-May).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Speech enhancement 100%
  • Deep neural networks 39%
  • Multi-task learning 27%
  • Speech synthesis 24%
  • Speech recognition 18%
  15 Citations (Scopus)

• Speech enhancement using square microphone array for mobile devices

  Takada, S., Ogawa, T., Akagiri, K. & Kobayashi, T., 2008 Sep 16, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. p. 313-316 4 p. 4517609. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Speech enhancement 100%
  • Microphones 84%
  • Mobile computing 68%
  • Speech recognition 50%
  • Costs 20%
  4 Citations (Scopus)

• Speech enhancement with ad-hoc microphone array using single source activity

  Sakanashi, R., Ono, N., Miyabe, S., Yamada, T. & Makino, S., 2013, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013. 6694323. (2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Speech enhancement 100%
  • Microphones 84%
  • Synchronization 21%
  • Acoustic waves 21%
  • Sampling 18%
  15 Citations (Scopus)

• Speech enhancement with LSTM recurrent neural networks and its application to noise-robust ASR

  Weninger, F., Erdogan, H., Watanabe, S., Vincent, E., Le Roux, J., Hershey, J. R. & Schuller, B., 2015, Latent Variable Analysis and Signal Separation - 12th International Conference, LVA/ICA 2015, Proceedings. Koldovský, Z., Vincent, E., Yeredor, A. & Tichavský, P. (eds.). Springer Verlag, p. 91-99 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9237).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Speech Enhancement 100%
  • Automatic Speech Recognition 98%
  • Memory Term 69%
  • Recurrent Neural Networks 68%
  • Speech enhancement 65%
  286 Citations (Scopus)

• SPEECH I/O SYSTEM REALIZING FLEXIBLE CONVERSATION FOR ROBOT - THE CONVERSATIONAL SYSTEM OF WABOT-2.

  Shirai, K., Kobayashi, T., Komori, Y., Hashimoto, N., Iwata, K., Fukazawa, Y. & Yazawa, J., 1985 Dec 1, In: Waseda Daigaku Rikogaku Kenkyusho Hokoku/Bulletin of Science and Engineering Research Laboratory, Waseda University. 112, p. 53-79 27 p.

  Research output: Contribution to journal › Article › peer-review

  • Robots 100%
  • Speech synthesis 47%

• Speech planning of an anthropomorphic talking robot for consonant sounds production

  Nishikawa, K., Imai, A., Ogawara, T., Takanobu, H., Mochida, T. & Takanishi, A., 2002 Jan 1, In: Proceedings - IEEE International Conference on Robotics and Automation. 2, p. 1830-1835 6 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Anthropomorphic robots 100%
  • Acoustic waves 70%
  • Planning 56%
  • Robots 16%
  11 Citations (Scopus)

• Speech Processing for Digital Home Assistants: Combining signal processing with deep-learning techniques

  Haeb-Umbach, R., Watanabe, S., Nakatani, T., Bacchiani, M., Hoffmeister, B., Seltzer, M. L., Zen, H. & Souden, M., 2019 Nov, In: IEEE Signal Processing Magazine. 36, 6, p. 111-124 14 p., 8887564.

  Research output: Contribution to journal › Review article › peer-review
  30 Citations (Scopus)

• Speech production by a mechanical model: Construction of a vocal tract and its control by neural network

  Higashimoto, T. & Sawada, H., 2002 Jan 1, In: Proceedings - IEEE International Conference on Robotics and Automation. 4, p. 3858-3863 6 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Acoustic waves 100%
  • Fluid mechanics 77%
  • Neural networks 76%
  • Mechatronics 70%
  • Fluid dynamics 67%
  19 Citations (Scopus)

• Speech Production Model and its Application to Speech Synthesis System - Speech Synthesis by Mimicing Human Speech Production -

  Honda, M., 1998 Dec 1, In: NTT R and D. 47, 4, p. 399-404 6 p.

  Research output: Contribution to journal › Article › peer-review

  • Speech synthesis 100%
  • Acoustics 50%
  • Research laboratories 23%

• Speech production model based on articulatory movements

  Honda, M. & Kaburagi, T., 1995 Jan 1, In: NTT R and D. 44, 1, p. 87-92 6 p.

  Research output: Contribution to journal › Article › peer-review

  • Acoustics 100%

• Speech production of an advanced talking robot based on human acoustic theory

  Nishikawa, K., Takanobu, H., Mochida, T., Honda, M. & Takanishi, A., 2004 Jul 5, In: Proceedings - IEEE International Conference on Robotics and Automation. 2004, 4, p. 3213-3219 7 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Acoustics 100%
  • Robots 78%
  • Magnetic resonance imaging 42%
  • Acoustic waves 32%
  7 Citations (Scopus)

• Speech-Quality Assessment Methods for Speech-Coding Systems

  Kitawaki, N., Honda, M. & Itoh, K., 1984 Oct, In: IEEE Communications Magazine. 22, 10, p. 26-33 8 p.

  Research output: Contribution to journal › Article › peer-review
  38 Citations (Scopus)

• Speech quality assessment of CS-ACELP

  Hayashi, S., Kataoka, A., Moriya, T. & Kaneko, T., 1996 Jul 1, In: NTT Review. 8, 4, p. 36-41 6 p.

  Research output: Contribution to journal › Article › peer-review

  • Differential pulse code modulation 100%
  • Speech coding 43%
  • Telephone 34%
  • Telecommunication 31%

• Speech recognition based on acoustically derived segment units

  Fukada, T., Bacchiani, M., Paliwal, K. K. & Sagisaka, Y., 1996 Dec 1, p. 1077-1080. 4 p.

  Research output: Contribution to conference › Paper › peer-review

  • Speech recognition 100%
  • Acoustics 57%
  • Maximum likelihood 33%
  • Statistics 26%
  • Computer simulation 19%
  8 Citations (Scopus)

• Speech recognition based on student's t-distribution derived from total Bayesian framework

  Watanabe, S. & Nakamura, A., 2006 Jan 1, In: IEICE Transactions on Information and Systems. E89-D, 3, p. 970-980 11 p.

  Research output: Contribution to journal › Article › peer-review

  • Speech recognition 100%
  • Students 78%
  4 Citations (Scopus)

• Speech recognition chip for monosyllables

  Nakamura, K., Zhu, Q., Maruoka, S., Horiyama, T., Kimura, S. & Watanabe, K., 2001, Proceedings of the ASP-DAC 2001: Asia and South Pacific Design Automation Conference 2001. Institute of Electrical and Electronics Engineers Inc., p. 396-399 4 p. 913339. (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC; vol. 2001-January).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Speech recognition 100%
  • Networks (circuits) 58%
  • Hidden Markov models 53%
  6 Citations (Scopus)

• Speech recognition for a humanoid with motor noise utilizing missing feature theory

  Nishimura, Y., Ishizuka, M., Nakadai, K., Nakano, M. & Tsujino, H., 2006, Proceedings of the 2006 6th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS. p. 26-33 8 p. 4115576

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Speech recognition 100%
  • Microphones 44%
  • Signal to noise ratio 35%
  • Speech intelligibility 27%
  • Reverberation 26%
  13 Citations (Scopus)

• Speech recognition in living rooms: Integrated speech enhancement and recognition system based on spatial, spectral and temporal modeling of sounds

  Delcroix, M., Kinoshita, K., Nakatani, T., Araki, S., Ogawa, A., Hori, T., Watanabe, S., Fujimoto, M., Yoshioka, T., Oba, T., Kubo, Y., Souden, M., Hahm, S. J. & Nakamura, A., 2013 May 3, In: Computer Speech and Language. 27, 3, p. 851-873 23 p., 547.

  Research output: Contribution to journal › Article › peer-review

  • Speech Enhancement 100%
  • Spatial Modeling 81%
  • Speech Recognition 80%
  • Speech 69%
  • Speech enhancement 65%
  19 Citations (Scopus)

• Speech recognition in nonstationary noise based on parallel HMMs and spectral subtraction

  Mine, R., Kobayashi, T. & Shirai, K., 1996 Dec, In: Systems and Computers in Japan. 27, 14, p. 37-44 8 p.

  Research output: Contribution to journal › Article › peer-review

  • Speech Recognition 100%
  • Subtraction 79%
  • Speech recognition 61%
  • Spectrality 49%
  • Speech 34%
  2 Citations (Scopus)

• Speech recognition in the blind condition based on multiple directivity patterns using a microphone array

  Sekiya, T. & Kobayashi, T., 2005 Jan 1, 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing. Institute of Electrical and Electronics Engineers Inc., p. I373-I376 1415128. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; vol. I).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Microphones 100%
  • Speech recognition 89%
  • Acoustic waves 75%
  • Sound stages 31%
  • Blind source separation 18%
  2 Citations (Scopus)

• Speech recognition of a named entity

  Tomita, T., Okimoto, Y., Yamamoto, H. & Sagisaka, Y., 2005 Jan 1, 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing. Institute of Electrical and Electronics Engineers Inc., p. I1057-I1060 1415299. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; vol. I).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Speech recognition 100%
  • Continuous speech recognition 95%
  • Computer simulation 38%
  • Statistics 26%
  • Experiments 12%
  2 Citations (Scopus)

• Speech recognition of double talk using SAFIA-based audio segregation

  Sekiya, T., Ogawa, T. & Kobayashi, T., 2003, EUROSPEECH 2003 - 8th European Conference on Speech Communication and Technology. International Speech Communication Association, p. 1285-1288 4 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Speech Recognition 100%
  • Segregation 64%
  • Microphones 63%
  • Speech recognition 57%
  • segregation 56%

• Speech recognition of foreign out-of-vocabulary words using a hierarchical language model

  Yamamoto, H., Kikui, G., Nakamura, S. & Sagisaka, Y., 2006 Jan 1, INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP. International Speech Communication Association, p. 1870-1873 4 p. (INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP; vol. 4).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Speech recognition 100%
  • Statistics 22%
  • Experiments 5%

• Speech recognition technology combined with three dimensional lip movement

  Komiya, K., Ishikawa, R. & Momose, K., 2001 Jan 1, In: Proceedings of SPIE - The International Society for Optical Engineering. 4298, p. 95-102 8 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Speech Recognition 100%
  • speech recognition 99%
  • Movement 66%
  • Speech recognition 61%
  • Three-dimensional 46%

• Speech recognition using deep canonical correlation analysis in noisy environments

  Isobe, S., Tamura, S. & Hayamizu, S., 2021, ICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods. De Marsico, M., di Baja, G. S. & Fred, A. (eds.). SciTePress, p. 63-70 8 p. (ICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Speech recognition 100%
  • Audiovisual 40%
  • Fusion reactions 14%

• Speech robot mimicking human articulatory motion

  Fukui, K., Kusano, T., Mukaeda, Y., Suzuki, Y., Takanishi, A. & Honda, M., 2010, Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010. International Speech Communication Association, p. 1021-1024 4 p. (Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Robot 100%
  • robot 92%
  • Continuous Speech 44%
  • Electromagnetic Phenomena 41%
  • Human Speech 39%
  5 Citations (Scopus)

• Speech segment network approach for optimization of synthesis unit set

  Iwahashi, N. & Sagisaka, Y., 1995 Oct, In: Computer Speech and Language. 9, 4, p. 335-352 18 p.

  Research output: Contribution to journal › Article › peer-review

  • Speech 100%
  • Synthesis 68%
  • Optimization 57%
  • Unit 52%
  • Set theory 48%
  2 Citations (Scopus)

• Speech Segment Selection for Concatenative Synthesis Based on Spectral Distortion Minimization

  Iwahashi, N., Kaiki, N. & Sagisaka, Y., 1993 Nov 1, In: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. E76-A, 11, p. 1942-1948 7 p.

  Research output: Contribution to journal › Article › peer-review

  • Speech 100%
  • Synthesis 68%
  • Acoustics 61%
  • Spectrality 56%
  • Acoustic distortion 48%
  25 Citations (Scopus)

• Speech shift: Direct speech-input-mode switching through intentional control of voice pitch

  Goto, M., Omoto, Y., Itou, K. & Kobayashi, T., 2003 Jan 1, p. 1201-1204. 4 p.

  Research output: Contribution to conference › Paper › peer-review

  • Direct Speech 100%
  • Word Processor 13%
  • Dictation 12%
  • File 8%
  • Regular 7%
  2 Citations (Scopus)

• Speech spectrum conversion based on speaker interpolation and multi-functional representation with weighting by radial basis function networks

  Iwahashi, N. & Sagisaka, Y., 1995 Feb, In: Speech Communication. 16, 2, p. 139-151 13 p.

  Research output: Contribution to journal › Article › peer-review

  • Radial Basis Function Network 100%
  • Speech 83%
  • Interpolation 77%
  • Radial basis function networks 75%
  • weighting 71%
  43 Citations (Scopus)

• Speech spectrum transformation by speaker interpolation

  Ituahashi, N. & Sagisaka, Y., 1994, In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 1, p. I461-I464 389256.

  Research output: Contribution to journal › Conference article › peer-review

  • Interpolation 100%
  • Speech synthesis 47%
  • Experiments 13%
  14 Citations (Scopus)

• Speech spotter: On-demand speech recognition in human-human conversation on the telephone or in face-to-face situations

  Goto, M., Kitayama, K., Itou, K. & Kobayashi, T., 2004 Jan 1, p. 1533-1536. 4 p.

  Research output: Contribution to conference › Paper › peer-review

  • Speech Recognition 100%
  • Telephone 62%
  • telephone 57%
  • conversation 46%
  • demand 42%
  8 Citations (Scopus)

• Speech starter: Noise-robust endpoint detection by using filled pauses

  Kitayama, K., Goto, M., Itou, K. & Kobayashi, T., 2003, EUROSPEECH 2003 - 8th European Conference on Speech Communication and Technology. International Speech Communication Association, p. 1237-1240 4 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Filled Pause 100%
  • Starters 74%
  • Speech Recognition 32%
  • Speech recognition 15%
  • Fillers 12%
  10 Citations (Scopus)

• Speech synthesis by mimicking articulatory movements

  Honda, M., Kaburagi, T. & Okadome, T., 1999 Dec 1, In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. 2, p. II-463 - II-468

  Research output: Contribution to journal › Conference article › peer-review

  • Speech synthesis 100%
  • Acoustics 54%
  • Trajectories 34%
  • Recovery 11%
  4 Citations (Scopus)

• SPEECH SYNTHESIS BY RULE USING AN OPTIMAL SELECTION OF NON-UNIFORM SYNTHESIS UNITS.

  Sagisaka, Y., 1988 Jan 1, In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. p. 679-682 4 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Speech synthesis 100%
  • Glossaries 79%
  • Statistical methods 34%
  • Set theory 26%
  74 Citations (Scopus)

• Speech synthesis for conversational news contents delivery

  Takatsu, H., Fukuoka, I., Fujie, S., Iwata, K. & Kobayashi, T., 2019, In: Transactions of the Japanese Society for Artificial Intelligence. 34, 2, B-I65_1-15.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Speech synthesis 100%
  • Deep neural networks 20%
  • Neural networks 12%