In this paper, we describe a spoken Arabic dialect identification (ADI) model
for Arabic that consistently outperforms previously published results on two
benchmark datasets: ADI-5 and ADI-17. We explore two architectural variations:
ResNet and ECAPA-TDNN, coupled with two types of acoustic features: MFCCs and
features exratected from the pre-trained self-supervised model UniSpeech-SAT
Large, as well as a fusion of all four variants. We find that individually,
ECAPA-TDNN network outperforms ResNet, and models with UniSpeech-SAT features
outperform models with MFCCs by a large margin. Furthermore, a fusion of all
four variants consistently outperforms individual models. Our best models
outperform previously reported results on both datasets, with accuracies of
84.7% and 96.9% on ADI-5 and ADI-17, respectively.

本篇论文描述了一个用于阿拉伯语的口语方言识别 (ADI) 模型，该模型在两个基准数据集 ADI-5 和 ADI-17 上始终表现优于先前发表的结果。我们探索了两种不同的架构变体：ResNet 和 ECAPA-TDNN，以及两种类型的声学特征：MFCC 和从预训练的自监督模型 UniSpeech-SAT Large 提取的特征，以及所有四个变体的融合。我们发现，ECAPA-TDNN 网络表现优于 ResNet，而具有 UniSpeech-SAT 特征的模型远远优于具有 MFCC 特征的模型。此外，所有四个变体的融合一致优于单个模型。我们的最佳模型在两个数据集上的准确率分别为 84.7% 和 96.9%，超过了先前报道的结果。

又一个阿拉伯方言识别模型

Yet Another Model for Arabic Dialect Identification

The adoption of advanced deep learning architectures in stuttering detection
(SD) tasks is challenging due to the limited size of the available datasets. To
this end, this work introduces the application of speech embeddings extracted
from pre-trained deep learning models trained on large audio datasets for
different tasks. In particular, we explore audio representations obtained using
emphasized channel attention, propagation, and aggregation time delay neural
network (ECAPA-TDNN) and Wav2Vec2.0 models trained on VoxCeleb and LibriSpeech
datasets respectively. After extracting the embeddings, we benchmark with
several traditional classifiers, such as the K-nearest neighbour (KNN),
Gaussian naive Bayes, and neural network, for the SD tasks. In comparison to
the standard SD systems trained only on the limited SEP-28k dataset, we obtain
a relative improvement of 12.08%, 28.71%, 37.9% in terms of unweighted average
recall (UAR) over the baselines. Finally, we have shown that combining two
embeddings and concatenating multiple layers of Wav2Vec2.0 can further improve
the UAR by up to 2.60% and 6.32% respectively.

本研究通过使用预先训练的深度学习模型提取的语音嵌入，探索了使用 ECAPA-TDNN 和 Wav2Vec2.0 模型进行口吃检测任务的音频表示。相对于仅在有限 SEP-28k 数据集上训练的标准口吃检测系统，本研究在多个传统分类器上获得了 12.08％、28.71％、37.9％的相对改进并进一步证明结合两个嵌入和连接多个层的 Wav2Vec2.0 可进一步提高其性能。

使用说话人表示和自监督上下文嵌入进行口吃检测

Stuttering Detection Using Speaker Representations and Self-supervised  Contextual Embeddings

Despite the recent advancements in Automatic Speech Recognition (ASR), the
recognition of accented speech still remains a dominant problem. In order to
create more inclusive ASR systems, research has shown that the integration of
accent information, as part of a larger ASR framework, can lead to the
mitigation of accented speech errors. We address multilingual accent
classification through the ECAPA-TDNN and Wav2Vec 2.0/XLSR architectures which
have been proven to perform well on a variety of speech-related downstream
tasks. We introduce a simple-to-follow recipe aligned to the SpeechBrain
toolkit for accent classification based on Common Voice 7.0 (English) and
Common Voice 11.0 (Italian, German, and Spanish). Furthermore, we establish new
state-of-the-art for English accent classification with as high as 95%
accuracy. We also study the internal categorization of the Wav2Vev 2.0
embeddings through t-SNE, noting that there is a level of clustering based on
phonological similarity. (Our recipe is open-source in the SpeechBrain toolkit,
see: this https URL)

本文介绍了使用 ECAPA-TDNN 和 Wav2Vec 2.0 / XLSR 体系结构进行多语言口音分类的简单操作方法，并以 Common Voice 数据集为基础，在英语、意大利语、德语和西班牙语上建立了新的最先进的英语口音分类。结果表明，将口音信息与 ASR 框架的其他部分相结合可以减轻口音识别错误。