论文标题
语音识别的标签同步和框架同步端到端模型的比较
A Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition
论文作者
论文摘要
端到端模型在自动语音识别（ASR）领域受到广泛关注。他们的优势之一是建立直接识别语音框架序列通过神经网络中的文本标签序列的简单性。根据识别过程中的驾驶端，端到端的ASR模型可以分为两种类型：标签同步和框架同步，每种都具有唯一的模型行为和特征。在这项工作中，我们对代表性标签 - 同步模型（变压器）和软帧同步模型（基于连续的集成和射击（CIF）模型）进行了详细的比较。在三个公共数据集和一个带有12000小时培训数据的大规模数据集上的结果表明，两种类型的模型具有与同步模式一致的相应优势。
End-to-end models are gaining wider attention in the field of automatic speech recognition (ASR). One of their advantages is the simplicity of building that directly recognizes the speech frame sequence into the text label sequence by neural networks. According to the driving end in the recognition process, end-to-end ASR models could be categorized into two types: label-synchronous and frame-synchronous, each of which has unique model behaviour and characteristic. In this work, we make a detailed comparison on a representative label-synchronous model (transformer) and a soft frame-synchronous model (continuous integrate-and-fire (CIF) based model). The results on three public dataset and a large-scale dataset with 12000 hours of training data show that the two types of models have respective advantages that are consistent with their synchronous mode.
