Temperature Fallback
A decoding strategy that retries generation at different temperature values when an initial output fails selected quality or confidence criteria.
Lower decoding temperature tends toward more deterministic token selection, while higher values widen the candidate distribution. In some speech-recognition pipelines, an initial decode that fails chosen quality or confidence checks can be retried with another temperature setting.
Fallback does not guarantee a better transcript and it adds inference cost. The trigger criteria must therefore be explicit or the latency budget can grow unpredictably. Whisper architecture in speech recognition systems and VAD, segment duration, and endpointing trade-offs expose the adjacent quality/latency boundary.