Journal article
Structured Sparse Spectral Transforms and Structural Measures for Voice Conversion
IEEE/ACM transactions on audio, speech, and language processing, Vol.26(12), pp.2267-2276
12/01/2018
DOI: 10.1109/TASLP.2018.2860682
PMCID: PMC6980218
PMID: 31984214
Abstract
We investigate a structured sparse spectral transform method for voice conversion (VC) to perform frequency warping and spectral shaping simultaneously on high-dimensional (D) STRAIGHT spectra. Learning a large transform matrix for high-D data often results in an overfit matrix with low sparsity, which leads to muffled speech in VC. We address this problem by using the frequency-warping characteristic of a source-target speaker pair to define a region of support (ROS) in a transform matrix, and further optimize it by nonnegative matrix factorization (NMF) to obtain structured sparse transform. We also investigate structural measures of spectral and temporal covariance and variance at different scales for assessing VC speech quality. Our experiments on ARCTIC dataset of 12 speaker pairs show that embedding the ROS in spectral transforms offers flexibility in tradeoffs between spectral distortion and structure preservation, and the structural measures provide quantitatively reasonable results on converted speech. Our subjective listening tests show that the proposed VC method achieves a mean opinion score of "very good" relative to natural speech, and in comparison with three other VC methods, it is the most preferred one in naturalness and in voice similarity to target speakers.
Details
- Title: Subtitle
- Structured Sparse Spectral Transforms and Structural Measures for Voice Conversion
- Creators
- Yunxin Zhao - University of MissouriMili Kuruvilla-Dugdale - University of MissouriMinguang Song - University of Missouri
- Resource Type
- Journal article
- Publication Details
- IEEE/ACM transactions on audio, speech, and language processing, Vol.26(12), pp.2267-2276
- DOI
- 10.1109/TASLP.2018.2860682
- PMID
- 31984214
- PMCID
- PMC6980218
- NLM abbreviation
- IEEE/ACM Trans Audio Speech Lang Process
- ISSN
- 2329-9290
- eISSN
- 2329-9304
- Publisher
- IEEE
- Number of pages
- 10
- Grant note
- R15DC016383 / NATIONAL INSTITUTE ON DEAFNESS AND OTHER COMMUNICATION DISORDERS; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Deafness & Other Communication Disorders (NIDCD) R15 DC016383 / National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA
- Language
- English
- Date published
- 12/01/2018
- Academic Unit
- Communication Sciences and Disorders
- Record Identifier
- 9984446542202771
Metrics
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