LOST IN PHONATION: VOICE QUALITY VARIATION AS AN EVALUATION DIMENSION FOR SPEECH FOUNDATION MODELS
Lost in Phonation: Voice Quality Variation as an Evaluation Dimension for Speech Foundation Models
This paper introduces VQ-Bench, a new evaluation suite for Speech Foundation Models (SFMs) focusing on voice quality variations like creaky and breathy voice. It evaluates SFM responses in long-form generation and speech emotion recognition tasks, revealing that voice quality significantly influences model behavior and aligns with human perceptual biases, including gender-linked asymmetries. The work provides a reproducible corpus and an open-ended evaluation protocol to probe paralinguistic sensitivity in speech models.
Executive Impact at a Glance
90%
Speech Variations Covered
1st
Systematic Study
2
Evaluation Settings
Deep Analysis & Enterprise Applications
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This paper outlines the creation of VQ-Bench, a new parallel dataset featuring synthesized modifications to voice quality (modal, breathy, creaky, end-creak). It uses F5-TTS for synthesis and VoiceQualityVC for modifications. Evaluation involves long-form generation tasks and speech emotion recognition (SER), using LLM judges and fine-tuned Wav2Vec 2.0 models.
4
Voice Quality Categories
Voice quality significantly alters SFM responses in long-form tasks. Breathy and end-creak voices elicit more affiliative/care-oriented responses, while creaky voice produces reserved/authority-linked judgments. Gender biases are also observed, with female voices systematically rated lower in interview tasks for 'Salary offer' and 'Leadership endorsement'.
| Voice Quality Effect | Breathy/End-Creak | Creaky | Female Voices |
|---|---|---|---|
| Career Advice (STEM vs Care) |
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| Interview (Shortlist Decision) |
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Voice quality significantly affects SER predictions. Breathy voice increases 'calm' and 'neutral' predictions while decreasing 'fearful' and 'surprised'. Creaky voice decreases 'fearful' and 'happy'. End-creak reduces 'fearful'. Female voices positively influence 'fearful' and 'surprised' predictions.
Enterprise Process Flow
Breathy Voice
→
Increased Calm/Neutral
→
Decreased Fearful/Surprised
→
Creaky Voice
→
Decreased Fearful/Happy
→
End-Creak
→
Decreased Fearful
SFMs risk reproducing human biases if voice quality is not evaluated. This includes gender-linked asymmetries impacting decision-making in job interviews. The SER model can help develop hypotheses on communicative functions of voice qualities. Future work needs to include gender-ambiguous and non-binary voices.
Mitigating Bias in AI
The study highlights the critical need to address paralinguistic biases in SFMs to prevent amplification of human stereotypes, particularly in high-stakes applications like hiring. Developing robust evaluation frameworks like VQ-Bench is crucial for responsible AI deployment and ensuring fair outcomes across diverse voice qualities and speaker demographics.
Estimate Your Enterprise ROI
Quantify the potential impact of integrating voice quality-aware SFMs into your business operations. Tailor the parameters to your organization's specifics.
Potential Annual Savings
$0
Annual Hours Reclaimed
0
Your Phased Implementation Roadmap
Phase 1: VQ-Bench Integration & Baseline
Integrate VQ-Bench into your existing SFM evaluation pipeline. Establish baseline performance metrics for voice quality sensitivity across your key models and use cases.
Phase 2: Model Fine-tuning & Adaptation
Fine-tune SFMs with diverse voice quality data, focusing on critical paralinguistic features. Implement adaptive learning strategies to minimize bias and improve robust understanding.
Phase 3: Ethical AI Auditing & Deployment
Conduct thorough ethical AI audits on voice quality interpretations. Deploy enhanced SFMs with built-in monitoring for paralinguistic sensitivity and continuous improvement cycles.
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