A Novel Automatic Framework for Speaker Drift Detection in Synthesized Speech
Recent diffusion-based text-to-speech (TTS) models achieve high naturalness and expressiveness, yet often suffer from speaker drift, a subtle, gradual shift in perceived speaker identity within a single utterance. This underexplored phenomenon undermines the coherence of synthetic speech, especially in long-form or interactive settings. We introduce the first automatic framework for detecting speaker drift by formulating it as a binary classification task over utterance-level speaker consistency. Our method computes cosine similarity across overlapping segments of synthesized speech and prompts large language models (LLMs) with structured representations to assess drift. We provide theore...
| Authors | Jia-Hong Huang, Seulgi Kim, Yi Chieh Liu et al. |
|---|---|
| Primary category | cs.SD |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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