PyChain：端到端ASR的LF-MMI的完全并行化的PyTorch实现
PyChain: A Fully Parallelized PyTorch Implementation of LF-MMI for End-to-End ASR
摘要：
我们提出了PyChain，这是一个完全并行化的PyTorch实现，用于Kaldi自动语音识别（ASR）工具包中所谓的\emph{chain models}的端到端无格最大互信息（LF-MMI）训练。与其他基于PyTorch和Kaldi的ASR工具包不同，PyChain被设计成尽可能灵活和轻量级，以便它可以轻松地插入新的ASR项目或其他现有的基于PyTorch的ASR工具中，例如新项目PyChain-example和Espresso，现有的端到端ASR工具包。PyChain的效率和灵活性通过诸如分子/分母图上的完整GPU训练以及对不等长序列的支持等新功能得到了证明。在WSJ数据集上的实验表明，通过简单的神经网络和常用的机器学习技术，PyChain可以实现与Kaldi相当的竞争结果，并且优于其他端到端ASR系统。
英文摘要：
We present PyChain, a fully parallelized PyTorch implementation of end-to-end lattice-free maximum mutual information (LF-MMI) training for the so-called \emph{chain models} in the Kaldi automatic speech recognition (ASR) toolkit. Unlike other PyTorch and Kaldi based ASR toolkits, PyChain is designed to be as flexible and light-weight as possible so that it can be easily plugged into new ASR projects, or other existing PyTorch-based ASR tools, as exemplified respectively by a new project PyChain-example, and Espresso, an existing end-to-end ASR toolkit. PyChain's efficiency and flexibility is demonstrated through such novel features as full GPU training on numerator/denominator graphs, and support for unequal length sequences. Experiments on the WSJ dataset show that with simple neural networks and commonly used machine learning techniques, PyChain can achieve competitive results that are comparable to Kaldi and better than other end-to-end ASR systems.
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。
