In this paper, we propose a novel soft and monotonic alignment mechanism used
for sequence transduction. It is inspired by the integrate-and-fire model in
spiking neural networks and employed in the encoder-decoder framework consists
of continuous functions, thus being named as: Continuous Integrate-and-Fire
(CIF). Applied to the ASR task, CIF not only shows a concise calculation, but
also supports online recognition and acoustic boundary positioning, thus
suitable for various ASR scenarios. Several support strategies are also
proposed to alleviate the unique problems of CIF-based model. With the joint
action of these methods, the CIF-based model shows competitive performance.
Notably, it achieves a word error rate (WER) of 2.86% on the test-clean of
Librispeech and creates new state-of-the-art result on Mandarin telephone ASR
benchmark.

本文提出了一种新颖的软单调对齐机制连续积分与火模型，用于序列传导且支持不同 ASR 方案、在线识别和声学边界定位，在一些支持策略的帮助下它表现良好，尤其在中文电话 ASR 基准测试上，取得了 2.86％的字错率，创造了新的最佳记录。

CIF: 端到端语音识别的连续积分与火模型

CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition

We present a novel semi-supervised approach for sequence transduction and
apply it to semantic parsing. The unsupervised component is based on a
generative model in which latent sentences generate the unpaired logical forms.
We apply this method to a number of semantic parsing tasks focusing on domains
with limited access to labelled training data and extend those datasets with
synthetically generated logical forms.

我们提出了一种新颖的半监督方法，应用于序列转换和语义分析。该方法包含基于生成模型的无监督成分，在该模型中，潜在的句子生成不成对的逻辑形式，我们将此方法应用于许多语义解析任务，重点关注训练数据标记不足的领域，并使用合成逻辑形式扩充这些数据集。

半监督序列自编码器语义解析

Semantic Parsing with Semi-Supervised Sequential Autoencoders

Many machine learning tasks can be expressed as the transformation---or
\emph{transduction}---of input sequences into output sequences: speech
recognition, machine translation, protein secondary structure prediction and
text-to-speech to name but a few. One of the key challenges in sequence
transduction is learning to represent both the input and output sequences in a
way that is invariant to sequential distortions such as shrinking, stretching
and translating. Recurrent neural networks (RNNs) are a powerful sequence
learning architecture that has proven capable of learning such representations.
However RNNs traditionally require a pre-defined alignment between the input
and output sequences to perform transduction. This is a severe limitation since
\emph{finding} the alignment is the most difficult aspect of many sequence
transduction problems. Indeed, even determining the length of the output
sequence is often challenging. This paper introduces an end-to-end,
probabilistic sequence transduction system, based entirely on RNNs, that is in
principle able to transform any input sequence into any finite, discrete output
sequence. Experimental results for phoneme recognition are provided on the
TIMIT speech corpus.

本文介绍了一种基于递归神经网络的端到端，概率序列转换系统，该系统可以将任意输入序列转换为任意的有限离散输出序列，并在 TIMIT 语音语料库上提供了音素识别的实验结果。