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Evaluation of the Speech Resynthesis Capabilities of the VoicePrivacy Challenge Baseline B1
Audio and Speech Processing · Submitted 22 Aug 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our findings indicate that both the speech representation and the vocoder introduces artifacts, causing an unnatural perception.
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The abstract, sentence by sentence
Speaker anonymization systems continue to improve their ability to obfuscate the original speaker characteristics in a speech signal, but often create processing artifacts and unnatural sounding voices as a tradeoff. Many of those systems stem from the VoicePrivacy Challenge (VPC) Baseline B1, using a neural vocoder to synthesize speech from an F0, x-vectors and bottleneck features-based speech representation. Inspired by this, we investigate the reproduction capabilities of the aforementioned baseline, to assess how successful the shared methodology is in synthesizing human-like speech. We use four objective metrics to measure speech quality, waveform similarity, and F0 similarity. Our findings indicate that both the speech representation and the vocoder introduces artifacts, causing an unnatural perception. A MUSHRA-like listening test on 18 subjects corroborate our findings, motivating further research on the analysis and synthesis components of the VPC Baseline B1.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Speaker anonymization systems continue to improve their ability to obfuscate the original speaker characteristics in a speech signal, but often create processing artifacts and unnatural sounding voices as a tradeoff. | background 1.00 | 0.05 | 0.05 | asserted 0.95 |
| s1 | Many of those systems stem from the VoicePrivacy Challenge (VPC) Baseline B1, using a neural vocoder to synthesize speech from an F0, x-vectors and bottleneck features-based speech representation. | background 1.00 | 0.05 | 0.03 | asserted 0.98 |
| s2 | Inspired by this, we investigate the reproduction capabilities of the aforementioned baseline, to assess how successful the shared methodology is in synthesizing human-like speech. | contribution 0.98 | 0.48 | 0.03 | asserted 0.54 |
| s3 | We use four objective metrics to measure speech quality, waveform similarity, and F0 similarity. | method 1.00 | 0.92 | 0.03 | asserted 0.99 |
| s4 | Our findings indicate that both the speech representation and the vocoder introduces artifacts, causing an unnatural perception. | result 0.95 | 0.95 | 0.82 | asserted 0.58 |
| s5 | A MUSHRA-like listening test on 18 subjects corroborate our findings, motivating further research on the analysis and synthesis components of the VPC Baseline B1. | result 0.53 | 0.91 | 0.27 | asserted 0.95 |
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The paper
Original abstract
Speaker anonymization systems continue to improve their ability to obfuscate the original speaker characteristics in a speech signal, but often create processing artifacts and unnatural sounding voices as a tradeoff. Many of those systems stem from the VoicePrivacy Challenge (VPC) Baseline B1, using a neural vocoder to synthesize speech from an F0, x-vectors and bottleneck features-based speech representation. Inspired by this, we investigate the reproduction capabilities of the aforementioned baseline, to assess how successful the shared methodology is in synthesizing human-like speech. We use four objective metrics to measure speech quality, waveform similarity, and F0 similarity. Our findings indicate that both the speech representation and the vocoder introduces artifacts, causing an unnatural perception. A MUSHRA-like listening test on 18 subjects corroborate our findings, motivating further research on the analysis and synthesis components of the VPC Baseline B1.
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How the literature uses it
What this paper does with the arXiv papers it cites
What it cites · 11 sentences without a confident label
Uses the method or tool of 2 papers
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The system denotes the alternative vocoder (HiFiGAN [ref] ) provided by the VPC organizers.
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis · Dataset · 0.95
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The users are presented each synthesis output and asked to rate the naturalness using the following prompt, inspired by the VPC subjective test [ref] .
The VoicePrivacy 2022 Challenge Evaluation Plan · Subjective listening test · 0.95
Cites as background 2 papers
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The VPC framework introduced objective and subjective metrics to evaluate different aspects of the anonymized speech signals [ref] .
The VoicePrivacy 2022 Challenge Evaluation Plan · Evaluation of voice anonymization systems · 0.84
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Numerous developments in the speech signal processing domain have rendered the collection of speech data as well as its adversarial utilization simpler [ref] .
Introducing the VoicePrivacy Initiative · Introduction · 0.96
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