End-to-end (E2E) systems have achieved competitive results compared to conventional hybrid hidden Markov model (HMM)-deep neural network based automatic speech recognition (ASR) systems. Such E2E systems are attractive due to the lack of dependence on alignments between input acoustic and output grapheme or HMM state sequence during training. This paper explores the design of an ASR-free end-to-end system for text query-based keyword search (KWS) from speech trained with minimal supervision. Our E2E KWS system consists of three sub-systems. The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to reconstruct the audio through a finite-dimensional representation. The second sub-system is a character-level RNN language model using embeddings learned from a convolutional neural network. Since the acoustic and text query embeddings occupy different representation spaces, they are input to a third feed-forward neural network that predicts whether the query occurs in the acoustic utterance or not. This E2E ASR-free KWS system performs respectably despite lacking a conventional ASR system and trains much faster.

本研究探讨一种使用最少监督训练的无自动语音识别（ASR）的端到端（E2E）系统，用于基于文本查询的关键词搜索（KWS）系统，该系统由三个子系统组成：一个基于递归神经网络（RNN）的声学自动编码器，一个使用从卷积神经网络中学到的嵌入的字符级RNN语言模型，以及一个输入文本查询和声纹嵌入并预测查询是否出现在声纹信息中的前馈神经网络。

语音端到端无ASR关键词搜索