Thanks to the rise of self-supervised learning, automatic speech recognition
(ASR) systems now achieve near-human performance on a wide variety of datasets.
However, they still lack generalization capability and are not robust to domain
shifts like accent variations. In this work, we use speech audio representing
four different French accents to create fine-tuning datasets that improve the
robustness of pre-trained ASR models. By incorporating various accents in the
training set, we obtain both in-domain and out-of-domain improvements. Our
numerical experiments show that we can reduce error rates by up to 25%
(relative) on African and Belgian accents compared to single-domain training
while keeping a good performance on standard French.

本文致力于提高自动语音识别系统的准确性与泛化能力，运用多种口音以构建微调数据集用以提高模型的鲁棒性，同时展示多领域数据集优化效果。

多领域训练提高口音语音识别

Improving Accented Speech Recognition with Multi-Domain Training

In recent years, several high-performance conversational systems have been
proposed based on the Transformer encoder-decoder model. Although previous
studies analyzed the effects of the model parameters and the decoding method on
subjective dialogue evaluations with overall metrics, they did not analyze how
the differences of fine-tuning datasets affect on user's detailed impression.
In addition, the Transformer-based approach has only been verified for English,
not for such languages with large inter-language distances as Japanese. In this
study, we develop large-scale Transformer-based Japanese dialogue models and
Japanese chit-chat datasets to examine the effectiveness of the
Transformer-based approach for building chit-chat dialogue systems. We
evaluated and analyzed the impressions of human dialogues in different
fine-tuning datasets, model parameters, and the use of additional information.

本文研究了基于 Transformer 编码器 - 解码器模型的高性能会话系统对日语的适用性，特别是在不同的微调数据集和模型参数下，探究了用户的具体印象和效果。