AudioLens: Multi-Perspective Speech Clustering with Reasoning Audio-Language Models
Audio clustering is a fundamental task for organizing rapidly growing speech collections, supporting applications such as conversational analysis and speech-driven discovery. However, existing methods rely on fixed acoustic similarity metrics or ASR-based text pipelines, limiting their ability to reorganize the same audio collection under different user-specified perspectives, especially when clustering depends on both linguistic and paralinguistic cues. We introduce audio multi-perspective clustering, where a model directly partitions speech recordings according to a natural-language perspective while inferring both the number of clusters and their assignments. To study this setting, we ...
| Authors | Wenjun Huang, Qiaosong Chu, Tiger Shao et al. |
|---|---|
| Primary category | cs.SD |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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