The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Voice conversion through vector quantization
Open accessAbstract
A new voice conversion technique through vector quantization and spectrum mapping is proposed. This technique is based on mapping codebooks which represent the cor respondencebetween different speakers' codebooks. The mapping codebooks for spectrum parameters, power values, and pitch frequencies are separately generated using training utterances. This technique makes it possible to precisely control voice individuality. The performance of this technique is confirmed by spectrum distortion and pitch frequency difference. To evaluate the overall performance of this technique, listening tests are carried out on two kinds of voice conversions: one between male and female speakers, the other between male speakers. In the male-to-female conversion experiment, all converted utterances are judged as female, and in the male-to-male conversion, 57% of them are identified as the target speaker.
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Voice conversion can help those with speech disorders and is useful for rehabilitation medicine.
“A benefit of using voice conversion is it can help those with speech disorders, which is useful for rehabilitation medicine (Abe et al., 1990; Toda et al., 2016).”
From Audio deepfakes: A survey (2023) · cites this paper as Abe et al., 1990
The Introduction discusses voice conversion for controlling voice individuality and synthesizing different voice qualities. The evaluation passages assess conversion between speakers, not benefits for people with speech disorders or rehabilitation medicine. Searches for those applications and related terms found no passage addressing them. (LLM, conf=0.76)
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Cites
Speaker adaptation through vector quantization
Shikano, K., Lee, K.-F., & Reddy, R. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1986.1168676 · Cited by 96
Self-citationVoice conversion: Factors responsible for quality
Childers, D. G., Yegnanarayana, B., & Wu, K. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1985.1168479 · Cited by 68
Hayashi, C. (1985) · Behaviormetrika · doi:10.2333/bhmk.12.18_67 · Cited by 14
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Research works printed but not linked: 1
- K.Shikano, K.Lee, and R.Reddy,"Speaker ad aptationthrough vector quantization," ICASSP 86, 2643-2646 (1986).
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Masood, M., Nawaz, M., Malik, K. M., Javed, A., Irtaza, A., & Malik, H. (2023) · Applied Intelligence · doi:10.1007/s10489-022-03766-z · Cited by 543
Data Augmentation for Deep Neural Network Acoustic Modeling
Cui, X., Goel, V., & Kingsbury, B. (2015) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2015.2438544 · Cited by 376
The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods
Lorenzo-Trueba, J., Yamagishi, J., Toda, T., Saito, D., Villavicencio, F., Kinnunen, T., & Ling, Z. (2018) · The Speaker and Language Recognition Workshop (Odyssey 2018) · doi:10.21437/odyssey.2018-28 · Cited by 305
The Voice Conversion Challenge 2016
Toda, T., Chen, L.-H., Saito, D., Villavicencio, F., Wester, M., Wu, Z., & Yamagishi, J. (2016) · Interspeech 2016 · doi:10.21437/interspeech.2016-1066 · Cited by 156
“High-quality nonparallel voice conversion based on cycle-consistent adversarial network,”
Fang, F., Yamagishi, J., Echizen, I., & Lorenzo-Trueba, J. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8462342 · Cited by 128
Khanjani, Z., Watson, G., & Janeja, V. P. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2022.1001063 · Cited by 121
Metadata
- Authors
- Abe, M., Nakamura, S., Shikano, K., & Kuwabara, H.
- Affiliations
- A TR Interpreting Telephony Research Laboratories
- Venue
- Journal of the Acoustical Society of Japan (E)
- Publisher
- Acoustical Society of Japan
- Published
- 1990
- Volume
- 11
- Issue
- 2
- Pages
- 71-76
- ISSN
- 0388-2861 (ISSN-L)
- 2185-3509
- OpenAlex
- W2013996527
- Cited by
- 150
- References
- 3
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 8 · 2022: 4 · 2021: 14 · 2020: 16 · 2019: 14 · 2018: 13 · 2017: 6 · 2016: 3 · 2015: 3 · 2014: 3 · 2013: 3 · 2012: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Acoustical Society of Japan (E) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Keywords
- voice conversion, vector quantization, speaker individuality, pitch frequency, spectral distortion, speaker identification
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