The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Pitch-synchronous waveform processing techniques for text-to-speech synthesis using diphones
Abstract
Abstract We review in a common framework several algorithms that have been proposed recently, in order to improve the voice quality of a text-to-speech synthesis based on acoustical units concatenation (Charpentier and Moulines, 1988; Moulines and Charpentier, 1988; Hamon et al., 1989). These algorithms rely on a pitch-synchronous overlap-add (PSOLA) approach for modifying the speech prosody and concatenating speech waveforms. The modifications of the speech signal are performed either in the frequency domain (FD-PSOLA), using the Fast Fourier Transform, or directly in the time domain (TD-PSOLA), depending on the length of the window used in the synthesis process. The frequency domain approach is capable of a great flexibility in modifying the spectral characteristics of the speech signal, while the time domain approach provides very efficient solutions for the real time implementation of synthesis systems. We also discuss the different kinds of distortions involved in these different algorithms.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Standard time-domain TSM methods, such as the synchronized overlap-add (SOLA) [15], the waveform-similarity overlap-add [16], and the pitch-synchronous overlap-add [17] techniques, are considered to provide high-quality TSM for quasi-harmonic signals.”
From Audio Time Stretching Using Fuzzy Classification of Spectral Bins (2017) · cites this paper as [17]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Speech Analysis Synthesis and Perception
Flanagan, J. L. (1972) · doi:10.1007/978-3-662-01562-9 · Cited by 1,469
Cited by
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911
“There are two main approaches to realize the speech synthesis part; non-parametric, example-based approach known as concatenative speech synthesis (Moulines & Charpentier, 1990; Sagisaka et al., 1992; Hunt & Black, 1996), and parametric, model-based approach known as statistical parametric speech…”
MethodMusic training for the development of auditory skills
Kraus, N., & Chandrasekaran, B. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2882 · Cited by 1,115
Spoofing and countermeasures for speaker verification: A survey
Wu, Z., Evans, N., Kinnunen, T., Yamagishi, J., Alegre, F., & Li, H. (2015) · Speech Communication · doi:10.1016/j.specom.2014.10.005 · Cited by 601
Scherer, K. R., Johnstone, & Klasmeyer, G. (2002) · Handbook of Affective Sciences · doi:10.1093/oso/9780195126013.003.0023 · Cited by 426
Peelle, J. E., Troiani, V., Wingfield, A., & Grossman, M. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhp142 · Cited by 245
Metadata
- Authors
- Moulines, E., & Charpentier, F.
- Venue
- Speech Communication
- Publisher
- Elsevier BV
- Published
- 1990-12
- Volume
- 9
- Issue
- 5-6
- Pages
- 453-467
- ISSN
- 0167-6393 (ISSN-L)
- OpenAlex
- W2428180336
- Cited by
- 1,244
- References
- 1
- FWCI
- 8.70
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
