Reliable Neural-Codec Text-to-Speech by ASR Self-Verification and Distillation: Near-Zero Catastrophic Failures Across Models and Codecs
arXiv:2606.18323v1 Announce Type: cross Abstract: Open autoregressive neural-codec text-to-speech (TTS) models sound excellent on typical inputs yet suffer stochastic catastrophic failures: on a meaningful fraction of utterances they emit silence, terminate early, or collapse into repetitive or hallucinated content. We show this failure mode is cheap to remove. Under a single format-robust metric (a catastrophic-failure rate via an ASR round-trip), best-of-N ASR self-verification drives failures to near-zero: no observed failures remain by N=2 on a standard corpus (LibriSpeech) and by N=4 on a
This paper addresses a critical reliability bottleneck for neural text-to-speech models, leveraging advancements in ASR for verification.
Improved reliability in TTS removes a significant barrier to widespread adoption in sensitive applications, paving the way for more robust AI-powered interactions.
The ability to virtually eliminate catastrophic failures makes advanced TTS models viable for mainstream and mission-critical uses, expanding their potential applications.
- · AI voice synthesis companies
- · Customer service and support
- · Content creation platforms
- · Accessibility technology
- · Platforms reliant on less reliable TTS
- · Manual voice recording services
Wider deployment of high-quality, reliable AI voices in user interfaces and automated systems.
Increased demand for personalized and context-aware AI voice agents, impacting human-computer interaction paradigms.
Potential for deepfake voice exploitation to become more sophisticated and harder to detect due to higher fidelity and reliability.
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Read at arXiv cs.LG