Transformer-based speech recognition models have achieved great success due
to the self-attention (SA) mechanism that utilizes every frame in the feature
extraction process. Especially, SA heads in lower layers capture various
phonetic characteristics by the query-key dot product, which is designed to
compute the pairwise relationship between frames. In this paper, we propose a
variant of SA to extract more representative phonetic features. The proposed
phonetic self-attention (phSA) is composed of two different types of phonetic
attention; one is similarity-based and the other is content-based. In short,
similarity-based attention captures the correlation between frames while
content-based attention only considers each frame without being affected by
other frames. We identify which parts of the original dot product equation are
related to two different attention patterns and improve each part with simple
modifications. Our experiments on phoneme classification and speech recognition
show that replacing SA with phSA for lower layers improves the recognition
performance without increasing the latency and the parameter size.

通过提出改进的语音注意力模型，即基于相似性和基于内容两种不同类型语音自注意力模型，本研究通过简单修改增强了模型对语音特征的提取能力，从而在低层替换自我注意力结构后，提高了语音识别性能而不增加延迟和参数大小。

语音识别中的相似性和基于内容的音素自注意力机制

Similarity and Content-based Phonetic Self Attention for Speech Recognition

We introduce polyglot language models, recurrent neural network models
trained to predict symbol sequences in many different languages using shared
representations of symbols and conditioning on typological information about
the language to be predicted. We apply these to the problem of modeling phone
sequences---a domain in which universal symbol inventories and
cross-linguistically shared feature representations are a natural fit.
Intrinsic evaluation on held-out perplexity, qualitative analysis of the
learned representations, and extrinsic evaluation in two downstream
applications that make use of phonetic features show (i) that polyglot models
better generalize to held-out data than comparable monolingual models and (ii)
that polyglot phonetic feature representations are of higher quality than those
learned monolingually.

通过应用多语言情境下的共用符号表示和语言的语言信息来训练递归神经网络模型，我们介绍了多语言模型的思想，以解决建模音序列的问题，该问题中具有普遍符号发明和跨语言共享特征表示。实验结果表明，多语言模型比单语言模型具有更好的泛化性能和更高质量的语音特征表示。

多语言神经语言模型：跨语言音标表示学习案例研究

Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic  Representation Learning

We investigate different approaches for dialect identification in Arabic
broadcast speech, using phonetic, lexical features obtained from a speech
recognition system, and acoustic features using the i-vector framework. We
studied both generative and discriminate classifiers, and we combined these
features using a multi-class Support Vector Machine (SVM). We validated our
results on an Arabic/English language identification task, with an accuracy of
100%. We used these features in a binary classifier to discriminate between
Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%. We
further report results using the proposed method to discriminate between the
five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine,
North African, and MSA, with an accuracy of 52%. We discuss dialect
identification errors in the context of dialect code-switching between
Dialectal Arabic and MSA, and compare the error pattern between manually
labeled data, and the output from our classifier. We also release the train and
test data as standard corpus for dialect identification.

本文研究使用语音识别系统获得的音位、词汇特征以及基于 i - 向量框架的声学特征等不同方法来进行阿拉伯语方言识别的研究，使用多类支持向量机（SVM）综合这些特征，并在阿拉伯语 / 英语语言识别任务中获得 100％的准确率。在本研究中，我们使用这些特征作为二元分类器来对现代标准阿拉伯语（MSA）和方言阿拉伯语进行区分，获得了 100％的准确率，然后进一步报道了使用该方法来区分最广泛使用的阿拉伯语方言中的五种：埃及语，海湾语，黎凡特语，北非语和 MSA，准确率为 52％。本文讨论了方言识别的错误以及在方言阿拉伯语和 MSA 之间进行语言代码切换的上下文中的错误模式。最后我们释放了研究中使用的数据作为方言识别的标准语料库。