Language identification for code-switching (CS), the phenomenon of
alternating between two or more languages in conversations, has traditionally
been approached under the assumption of a single language per token. However,
if at least one language is morphologically rich, a large number of words can
be composed of morphemes from more than one language (intra-word CS). In this
paper, we extend the language identification task to the subword-level, such
that it includes splitting mixed words while tagging each part with a language
ID. We further propose a model for this task, which is based on a segmental
recurrent neural network. In experiments on a new Spanish--Wixarika dataset and
on an adapted German--Turkish dataset, our proposed model performs slightly
better than or roughly on par with our best baseline, respectively. Considering
only mixed words, however, it strongly outperforms all baselines.

本文将语种识别任务推广到子词级别，研究如何识别包含两种以上语言的语素（intra-word CS），提出了一种基于分段循环神经网络的模型，并在西班牙 - 韦萨里卡数据集和德国 - 土耳其数据集的实验中，相较于基准方法有略微提升或相当的表现。

基于子词的语言识别技术用于单词内的语码转换

Subword-Level Language Identification for Intra-Word Code-Switching

We study the segmental recurrent neural network for end-to-end acoustic
modelling. This model connects the segmental conditional random field (CRF)
with a recurrent neural network (RNN) used for feature extraction. Compared to
most previous CRF-based acoustic models, it does not rely on an external system
to provide features or segmentation boundaries. Instead, this model
marginalises out all the possible segmentations, and features are extracted
from the RNN trained together with the segmental CRF. In essence, this model is
self-contained and can be trained end-to-end. In this paper, we discuss
practical training and decoding issues as well as the method to speed up the
training in the context of speech recognition. We performed experiments on the
TIMIT dataset. We achieved 17.3 phone error rate (PER) from the first-pass
decoding --- the best reported result using CRFs, despite the fact that we only
used a zeroth-order CRF and without using any language model.

研究了段落循环神经网络在端到端声学建模中的应用，不依赖于外部系统提供特征或分割边界，通过神经网络进行特征提取，具有自我完备性，可实现端到端训练，实验表明在语音识别领域中表现良好。

端到端语音识别的分段循环神经网络

Segmental Recurrent Neural Networks for End-to-end Speech Recognition

We introduce segmental recurrent neural networks (SRNNs) which define, given
an input sequence, a joint probability distribution over segmentations of the
input and labelings of the segments. Representations of the input segments
(i.e., contiguous subsequences of the input) are computed by encoding their
constituent tokens using bidirectional recurrent neural nets, and these
"segment embeddings" are used to define compatibility scores with output
labels. These local compatibility scores are integrated using a global
semi-Markov conditional random field. Both fully supervised training -- in
which segment boundaries and labels are observed -- as well as partially
supervised training -- in which segment boundaries are latent -- are
straightforward. Experiments on handwriting recognition and joint Chinese word
segmentation/POS tagging show that, compared to models that do not explicitly
represent segments such as BIO tagging schemes and connectionist temporal
classification (CTC), SRNNs obtain substantially higher accuracies.

引入了分段循环神经网络（SRNN），并使用全局半马尔可夫条件随机场来集成本地兼容性分数，从而在手写识别和汉语分词 / 词性标注方面获得了显着更高的准确性。