Advancing Speaker-Based Vocal Effort Classification with WavLM and Data Augmentation in Naturalistic Non-Calibrated Speech Recordings
arXiv:2606.27543v1 Announce Type: cross Abstract: The variations in vocal effort range (e.g. whisper, soft, neutral, loud, shout) alter production and speech acoustics, reducing intelligibility and limiting the robustness of any subsequent speech technology. Classification is challenging since effort lies on a continuum, adjacent categories are easily confused, and labeled data remain scarce. Prior SSL approaches with wav2vec2, HuBERT, and AST improve performance on the AVID corpus but still suffer from boundary errors. In this study, we introduce WavLM for the first time in vocal effort class
The continuous advancements in self-supervised learning for speech processing, particularly with models like WavLM, are enabling more nuanced and robust analysis of human vocalizations.
Improved classification of vocal effort in uncalibrated and naturalistic speech has significant implications for speech technology robustness, human-computer interaction, and potentially health monitoring.
This research introduces WavLM as a new benchmark for vocal effort classification, suggesting a path to more accurate and resilient speech interfaces and analytics, particularly in challenging real-world scenarios.
- · Speech technology developers
- · Voice assistant companies
- · Healthcare monitoring solutions
- · Security and authentication systems
- · Speech recognition systems lacking robust vocal effort compensation
- · Applications relying on narrowly trained speech models
More reliable and natural speech interfaces emerge, reducing frustration from misinterpretations of vocal effort.
Improved vocal biomarkers could be developed for early detection of stress, fatigue, or certain medical conditions based on subtle changes in vocal effort.
The ability to accurately classify vocal effort might enable more sophisticated emotional AI, leading to more empathetic and context-aware digital companions or support systems.
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Read at arXiv cs.LG