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How do Voices from Past Speech Synthesis Challenges Compare Today?

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arxiv 2105.02373 v4 pith:2ZAV5XEK submitted 2021-05-05 cs.SD eess.AS

classification cs.SDeess.AS
keywords challengeschallengesynthesislisteningresultssamplestestcompare
verification ladder T0 review T1 audit T2 compute T3 formal
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Shared challenges provide a venue for comparing systems trained on common data using a standardized evaluation, and they also provide an invaluable resource for researchers when the data and evaluation results are publicly released. The Blizzard Challenge and Voice Conversion Challenge are two such challenges for text-to-speech synthesis and for speaker conversion, respectively, and their publicly-available system samples and listening test results comprise a historical record of state-of-the-art synthesis methods over the years. In this paper, we revisit these past challenges and conduct a large-scale listening test with samples from many challenges combined. Our aims are to analyze and compare opinions of a large number of systems together, to determine whether and how opinions change over time, and to collect a large-scale dataset of a diverse variety of synthetic samples and their ratings for further research. We found strong correlations challenge by challenge at the system level between the original results and our new listening test. We also observed the importance of the choice of speaker on synthesis quality.

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  1. Audio Large Language Models Can Be Descriptive Speech Quality Evaluators

    cs.SD 2025-01 conditional novelty 6.0 of 10

    Audio LLMs fine-tuned with token-level distillation against an LLM teacher can predict speech quality scores and generate natural-language descriptions, including A/B comparisons.

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