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 has led to significant success in text-to-speech (TTS) synthesis. Although these models can generate speech with high quality and naturalness%2C their synthesised samples can still suffer from artefacts%2C mispronunciation%2C word repeating%2C etc. In this paper%2C we argue these undesirable properties could partly be caused by the randomness of sampling-based strategies during the autoregressive decoding of LMs. Therefore%2C we look at maximisation-based decoding approaches and propose Temporal Repetition Aware Diverse Beam Search (TRAD-BS) to find the most probable sequences of the generated speech tokens. Experiments with two state-of-the-art LM-based TTS models demonstrate that our proposed maximisation-based decoding strategy generates speech with fewer mispronunciations and improved speaker consistency.?quality=80&w=800)
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