从粗到细:通过多尺度语音编码和生成改进神经编解码器语言模型

Haohan Guo, Fenglong Xie, Dongchao Yang, Xixin Wu, Helen Meng
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引用次数: 0

摘要

神经编解码语言模型(CLM)在文本到语音(TTS)合成中表现出了卓越的性能。然而,受 "时间偏差 "的困扰,CLM 对更高时间范围内的粗粒度信息缺乏足够的关注,往往会产生不自然甚至无法理解的语音。本研究提出了一种从粗到细的 CLM-TTS 方法 CoFi-Speech,它采用多尺度语音编码和生成来解决这一问题。我们训练了一种多尺度神经编解码器 CoFi-Codec,将语音编码为多尺度离散表示,包括具有不同时间分辨率的多个标记序列。然后,我们提出了 CoFi-LM,它能以两种模式生成这种表示:基于单 LM 的尺度链生成和基于多 LM 的尺度堆叠生成。在实验中,CoFi-Speech 在零镜头 TTS 的自然度和说话人相似度方面明显优于单尺度基准系统。对多尺度编码的分析表明,CoFi-Codec 能有效学习多尺度离散语音表示,同时保持高质量语音重建。从粗到细的多尺度生成,尤其是尺度堆栈方法,也被证实是为 TTS 建立高质量神经编解码语言模型的关键方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speaking from Coarse to Fine: Improving Neural Codec Language Model via Multi-Scale Speech Coding and Generation
The neural codec language model (CLM) has demonstrated remarkable performance in text-to-speech (TTS) synthesis. However, troubled by ``recency bias", CLM lacks sufficient attention to coarse-grained information at a higher temporal scale, often producing unnatural or even unintelligible speech. This work proposes CoFi-Speech, a coarse-to-fine CLM-TTS approach, employing multi-scale speech coding and generation to address this issue. We train a multi-scale neural codec, CoFi-Codec, to encode speech into a multi-scale discrete representation, comprising multiple token sequences with different time resolutions. Then, we propose CoFi-LM that can generate this representation in two modes: the single-LM-based chain-of-scale generation and the multiple-LM-based stack-of-scale generation. In experiments, CoFi-Speech significantly outperforms single-scale baseline systems on naturalness and speaker similarity in zero-shot TTS. The analysis of multi-scale coding demonstrates the effectiveness of CoFi-Codec in learning multi-scale discrete speech representations while keeping high-quality speech reconstruction. The coarse-to-fine multi-scale generation, especially for the stack-of-scale approach, is also validated as a crucial approach in pursuing a high-quality neural codec language model for TTS.
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