Language model based text-to-speech (TTS) models, like VALL-E, have gained attention for their outstanding in-context learning capability in zero-shot scenarios. Neural speech codec is a critical component of these models, which can convert speech into discrete token representations. However, excessive token sequences from the codec may negatively affect prediction accuracy and restrict the progression of Language model based TTS models. To address this issue, this paper proposes a novel neural speech codec with time-invariant codes named TiCodec. By encoding and quantizing time-invariant information into a separate code, TiCodec can reduce the amount of frame-level information that needs encoding, effectively decreasing the number of tokens as codes of speech. Furthermore, this paper introduces a time-invariant encoding consistency loss to enhance the consistency of time-invariant code within an utterance and force it to capture more global information, which can benefit the zero-shot TTS task. Experimental results demonstrate that TiCodec can not only enhance the quality of reconstruction speech with fewer tokens but also increase the similarity and naturalness, as well as reduce the word error rate of the synthesized speech by the TTS model.

翻译：基于语言模型的文本到语音（TTS）模型，如VALL-E，因其在零样本场景中出色的上下文学习能力而备受关注。神经语音编解码器是这些模型的关键组成部分，能够将语音转换为离散的令牌表示。然而，编解码器生成的过多令牌序列可能会对预测准确性产生负面影响，并限制基于语言模型的TTS模型的发展。为解决这一问题，本文提出了一种新型的具有时不变码的神经语音编解码器，命名为TiCodec。通过将时不变信息编码并量化到单独的代码中，TiCodec能够减少需要编码的帧级信息量，从而有效降低作为语音编码的令牌数量。此外，本文引入了一种时不变编码一致性损失，以增强话语内时不变码的一致性，并迫使其捕获更多全局信息，这有助于零样本TTS任务。实验结果表明，TiCodec不仅能够以更少的令牌提升重建语音的质量，还能提高相似性和自然度，并降低TTS模型合成语音的词错误率。