On Hallucinations

Generative AI systems, including text-to-speech (TTS) models, can experience "hallucinations," where outputs deviate from reality or intended inputs, similar to human sensory misperceptions. TTS hallucinations occur when output speech diverges from input text, often due to the stochastic nature of autoregressive models and the influence of flawed training data. This can result in repetition, mispronunciations, and extraneous sounds, posing challenges for commercial applications that require precise text adherence. A common method to mitigate these hallucinations involves segmenting input text into smaller parts for speech generation, though this can lead to prosody issues and new errors. Rime claims to have developed solutions to prevent such hallucinations entirely, though details of their approach remain proprietary.

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