This paper empirically investigates the influence of different data splits
and splitting strategies on the performance of dysfluency detection systems.
For this, we perform experiments using wav2vec 2.0 models with a classification
head as well as support vector machines (SVM) in conjunction with the features
extracted from the wav2vec 2.0 model to detect dysfluencies. We train and
evaluate the systems with different non-speaker-exclusive and speaker-exclusive
splits of the Stuttering Events in Podcasts (SEP-28k) dataset to shed some
light on the variability of results w.r.t. to the partition method used.
Furthermore, we show that the SEP-28k dataset is dominated by only a few
speakers, making it difficult to evaluate. To remedy this problem, we created
SEP-28k-Extended (SEP-28k-E), containing semi-automatically generated speaker
and gender information for the SEP-28k corpus, and suggest different data
splits, each useful for evaluating other aspects of methods for dysfluency
detection.

本文研究了不同的数据分割和分割策略对口吃检测系统性能的影响，并使用 wav2vec 2.0 模型和支持矢量机 (SVM) 来检测口吃现象。我们使用 Stuttering Events in Podcasts (SEP-28k) 数据集中的不同非专属和专属分割进行训练和评估，以揭示结果与使用的分区方法的变异性，并证明 SEP-28k 数据集只由很少的演讲者占据，难以评估。为了解决这个问题，我们创建了 SEP-28k-Extended (SEP-28k-E)，其中包含 SEP-28k 语料库的半自动生成的演讲者和性别信息，并建议不同的数据分割，每个分割都有助于评估口吃检测方法的其他方面。

数据集划分对口吃检测系统的影响

The Influence of Dataset Partitioning on Dysfluency Detection Systems

Deep acoustic models represent linguistic information based on massive
amounts of data. Unfortunately, for regional languages and dialects such
resources are mostly not available. However, deep acoustic models might have
learned linguistic information that transfers to low-resource languages. In
this study, we evaluate whether this is the case through the task of
distinguishing low-resource (Dutch) regional varieties. By extracting
embeddings from the hidden layers of various wav2vec 2.0 models (including new
models which are pre-trained and/or fine-tuned on Dutch) and using dynamic time
warping, we compute pairwise pronunciation differences averaged over 10 words
for over 100 individual dialects from four (regional) languages. We then
cluster the resulting difference matrix in four groups and compare these to a
gold standard, and a partitioning on the basis of comparing phonetic
transcriptions. Our results show that acoustic models outperform the
(traditional) transcription-based approach without requiring phonetic
transcriptions, with the best performance achieved by the multilingual XLSR-53
model fine-tuned on Dutch. On the basis of only six seconds of speech, the
resulting clustering closely matches the gold standard.

本研究通过 wav2vec 2.0 模型中提取的嵌入来区分多达 100 种荷兰四种方言的发音差异，结果表明，基于声学模型的方法优于基于电话转录的方法，且 XLSR-53 模型在荷兰方言的微调后表现最佳，仅仅利用六秒的语音，即可得到与实际情况吻合的聚类。