Low-resource accented speech recognition is one of the important challenges
faced by current ASR technology in practical applications. In this study, we
propose a Conformer-based architecture, called Aformer, to leverage both the
acoustic information from large non-accented and limited accented training
data. Specifically, a general encoder and an accent encoder are designed in the
Aformer to extract complementary acoustic information. Moreover, we propose to
train the Aformer in a multi-pass manner, and investigate three
cross-information fusion methods to effectively combine the information from
both general and accent encoders. All experiments are conducted on both the
accented English and Mandarin ASR tasks. Results show that our proposed methods
outperform the strong Conformer baseline by relative 10.2% to 24.5%
word/character error rate reduction on six in-domain and out-of-domain accented
test sets.

本研究提出了一种基于 Conformer 的架构，即 Aformer，利用大型非方言和有限口音训练数据从声学信息中提取互补的音频信息，并提出了多通道训练和三种跨信息融合方法，用于从 Aformer 的通用编码器和口音编码器中有效地融合信息，实验结果表明，该方法在口音英语和普通话 ASR 任务上相对于强基线 Conformer 的 6 个领域内外测试数据的单词 / 字符错误率降低了 10.2％到 24.5％。

低资源端到端口音语音识别的多遍训练和交叉信息融合

Multi-pass Training and Cross-information Fusion for Low-resource  End-to-end Accented Speech Recognition

Accented speech recognition and accent classification are relatively
under-explored research areas in speech technology. Recently, deep
learning-based methods and Transformer-based pretrained models have achieved
superb performances in both areas. However, most accent classification tasks
focused on classifying different kinds of English accents and little attention
was paid to geographically-proximate accent classification, especially under a
low-resource setting where forensic speech science tasks usually encounter. In
this paper, we explored three main accent modelling methods combined with two
different classifiers based on 105 speaker recordings retrieved from five urban
varieties in Northern England. Although speech representations generated from
pretrained models generally have better performances in downstream
classification, traditional methods like Mel Frequency Cepstral Coefficients
(MFCCs) and formant measurements are equipped with specific strengths. These
results suggest that in forensic phonetics scenario where data are relatively
scarce, a simple modelling method and classifier could be competitive with
state-of-the-art pretrained speech models as feature extractors, which could
enhance a sooner estimation for the accent information in practices. Besides,
our findings also cross-validated a new methodology in quantifying
sociophonetic changes.

本文探讨了不同语音机器学习模型及分类器在地方口音识别方面的应用，发现传统方法的性能也值得关注，并证实了一种新方法测量社会语音变化。

地理区域近似场景下的低资源口音分类：法庭和社会语音学视角

Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective

In this paper, we propose a domain adversarial training (DAT) algorithm to
alleviate the accented speech recognition problem. In order to reduce the
mismatch between labeled source domain data ("standard" accent) and unlabeled
target domain data (with heavy accents), we augment the learning objective for
a Kaldi TDNN network with a domain adversarial training (DAT) objective to
encourage the model to learn accent-invariant features. In experiments with
three Mandarin accents, we show that DAT yields up to 7.45% relative character
error rate reduction when we do not have transcriptions of the accented speech,
compared with the baseline trained on standard accent data only. We also find a
benefit from DAT when used in combination with training from automatic
transcriptions on the accented data. Furthermore, we find that DAT is superior
to multi-task learning for accented speech recognition.

本文提出了一个领域对抗训练算法（DAT）来缓解口音识别问题。我们将领域对抗训练（DAT）目标与 Kaldi TDNN 网络的学习目标相结合，鼓励模型学习不受口音影响的特征，以减少标准口音和带有口音的未标记目标域数据之间的不匹配。通过三种不同的普通话口音的实验，我们发现，与仅训练标准口音数据的基线相比，当我们没有口音转录时，DAT 可以使得字符错误率相对降低达 7.45％。我们还发现，DAT 与口音数据自动转录训练结合使用时具有优势，并且 DAT 比多任务学习在口音识别方面更为优异。