Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·25d ago·5 min read
Best-of-$N$ TTS Evaluation is Confounded by ASR Family Alignment
TL;DR
Best-of-$N$ evaluations for Text-to-Speech (TTS) systems are impacted by the alignment of Automatic Speech Recognition (ASR) families. Researchers discovered that ASR family alignment can confound TTS evaluation results, leading to misleading conclusions.
✦ Why It Matters
Evaluate TTS systems with awareness of ASR family alignment to avoid misleading performance assessments.
Key Takeaways
- Consider ASR family alignment when evaluating TTS systems.
- Use statistical methods to assess the impact of ASR on TTS evaluations.
- Avoid relying solely on Best-of-$N$ evaluations for TTS quality.
- Ensure valid comparisons by understanding ASR influences on TTS outputs.
Full Summary
Text-to-Speech (TTS) systems are often evaluated using a Best-of-$N$ approach, where multiple outputs are compared to determine the best one. However, this study reveals that the alignment of Automatic Speech Recognition (ASR) families can significantly affect these evaluations.
By analyzing various TTS outputs and their corresponding ASR results, the researchers found that ASR family alignment can introduce biases, leading to inaccurate assessments of TTS quality. They employed statistical methods to quantify the impact of ASR alignment on TTS evaluation outcomes.
The results indicate that TTS systems may appear to perform differently based on the ASR family used, which can mislead developers and researchers. This highlights the importance of considering ASR influences when conducting TTS evaluations to ensure valid comparisons and conclusions.