Abstract | ||
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Any modification applied to speech signals has an impact on their perceptual quality. In particular, voice conversion to modify a source voice so that it is perceived as a specific target voice involves prosodic and spectral transformations that produce significant quality degradation. Choosing among the current voice conversion methods represents a trade-off between the similarity of the converted voice to the target voice and the quality of the resulting converted speech, both rated by listeners. This paper presents a new voice conversion method termed Weighted Frequency Warping that has a good balance between similarity and quality. This method uses a time-varying piecewise-linear frequency warping function and an energy correction filter, and it combines typical probabilistic techniques and frequency warping transformations. Compared to standard probabilistic systems, Weighted Frequency Warping results in a significant increase in quality scores, whereas the conversion scores remain almost unaltered. This paper carefully discusses the theoretical aspects of the method and the details of its implementation, and the results of an international evaluation of the new system are also included. |
Year | DOI | Venue |
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2010 | 10.1109/TASL.2009.2038663 | Audio, Speech, and Language Processing, IEEE Transactions |
Keywords | Field | DocType |
target voice,specific target voice,voice conversion,quality score,new voice conversion method,weighted frequency warping,significant quality degradation,current voice conversion method,source voice,converted voice,perceptual quality,degradation,stochastic model,speech synthesis,piecewise linear,piecewise linear function,probability,gaussian mixture model,loudspeakers,stochastic processes | Speech synthesis,Image warping,Pattern recognition,Voice activity detection,Computer science,Stochastic process,Speech recognition,Artificial intelligence,Probabilistic logic,Loudspeaker,Piecewise linear function,Perception | Journal |
Volume | Issue | ISSN |
18 | 5 | 1558-7916 |
Citations | PageRank | References |
67 | 2.17 | 21 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Daniel Erro | 1 | 169 | 6.24 |
Asunción Moreno | 2 | 399 | 44.97 |
Antonio Bonafonte | 3 | 693 | 64.80 |