Controlling Speaking Rate in Autoregressive TTS via Activation Steering
Autoregressive text-to-speech (TTS) systems synthesize natural speech but, once trained, offer little control over speaking rate. We show that speaking rate can be steered at inference time, without retraining, by clamping a single decoder block's activation along a discovered speed axis. A decoder-block analysis recovers the rate axis, a neutral operating point, and a per-step intensity scale; at inference, the activation's projection onto this axis is set to a fixed scalar. Learning this direction from synthetically time-stretched and time-compressed speech yields rate control that largely preserves speaker identity, generalizes across model architectures, and maintains high naturalness...
| Authors | Francesco Verdini, Antonis Asonitis, Aref Farhadipour et al. |
|---|---|
| Primary category | cs.SD |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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