迈向音频语言建模——综述
Towards audio language modeling -- an overview
AI总结：
本文系统综述了神经音频编解码器及其作为音频分词器用于构建音频语言模型的研究进展，梳理了高性能编解码器与基于编解码器的语言模型。
摘要：
神经音频编解码器最初被引入是为了将音频数据压缩成紧凑编码，以降低传输延迟。研究者最近发现编解码器具有作为合适分词器的潜力，可将连续音频转换为离散编码，从而用于开发音频语言模型（LMs）。目前已经开发出大量高性能神经音频编解码器和基于编解码器的语言模型。本文旨在对神经音频编解码器模型以及基于编解码器的语言模型进行全面且系统的综述。
英文摘要：
Neural audio codecs are initially introduced to compress audio data into compact codes to reduce transmission latency. Researchers recently discovered the potential of codecs as suitable tokenizers for converting continuous audio into discrete codes, which can be employed to develop audio language models (LMs). Numerous high-performance neural audio codecs and codec-based LMs have been developed. The paper aims to provide a thorough and systematic overview of the neural audio codec models and codec-based LMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
