Stuttering is a varied speech disorder that harms an individual's
communication ability. Persons who stutter (PWS) often use speech therapy to
cope with their condition. Improving speech recognition systems for people with
such non-typical speech or tracking the effectiveness of speech therapy would
require systems that can detect dysfluencies while at the same time being able
to detect speech techniques acquired in therapy. This paper shows that
fine-tuning wav2vec 2.0 [1] for the classification of stuttering on a sizeable
English corpus containing stuttered speech, in conjunction with multi-task
learning, boosts the effectiveness of the general-purpose wav2vec 2.0 features
for detecting stuttering in speech; both within and across languages. We
evaluate our method on FluencyBank , [2] and the German therapy-centric Kassel
State of Fluency (KSoF) [3] dataset by training Support Vector Machine
classifiers using features extracted from the finetuned models for six
different stuttering-related event types: blocks, prolongations, sound
repetitions, word repetitions, interjections, and - specific to therapy -
speech modifications. Using embeddings from the fine-tuned models leads to
relative classification performance gains up to 27% w.r.t. F1-score.

通过使用最新的波形神经网络技术（wav2vec 2.0）并结合多任务学习，对一个英文语料库中的口吃病识别进行了研究，并在语音识别技术和口吃治疗领域有重要应用。

利用 wav2vec 2.0 检测口吃治疗中的语音障碍

Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0

Ultrasound tongue imaging (UTI) provides a convenient way to visualize the
vocal tract during speech production. UTI is increasingly being used for speech
therapy, making it important to develop automatic methods to assist various
time-consuming manual tasks currently performed by speech therapists. A key
challenge is to generalize the automatic processing of ultrasound tongue images
to previously unseen speakers. In this work, we investigate the classification
of phonetic segments (tongue shapes) from raw ultrasound recordings under
several training scenarios: speaker-dependent, multi-speaker,
speaker-independent, and speaker-adapted. We observe that models underperform
when applied to data from speakers not seen at training time. However, when
provided with minimal additional speaker information, such as the mean
ultrasound frame, the models generalize better to unseen speakers.

本文研究使用超声舌头成像技术对语音产生的过程进行可视化，并通过多种训练方案探讨了从原始超声记录中对语音的分类方法，研究表明，提供最少的额外讲话者信息可以有助于模型更好地推广到未知的讲话者中。

儿童语音中原始超声波音素段的说话人无关分类

Speaker-independent classification of phonetic segments from raw  ultrasound in child speech

We introduce UltraSuite, a curated repository of ultrasound and acoustic
data, collected from recordings of child speech therapy sessions. This release
includes three data collections, one from typically developing children and two
from children with speech sound disorders. In addition, it includes a set of
annotations, some manual and some automatically produced, and software tools to
process, transform and visualise the data.

介绍了 UltraSuite，它是一个儿童言语疗法会话录音的超声波和声学数据的精选存储库，包括来自典型发育儿童和患有言语音韵障碍的两组数据集，以及一些手动和自动生产的注释和软件工具，以处理、转换和可视化这些数据。

UltraSuite：儿童语音治疗会话中的超声和声学数据库

UltraSuite: A Repository of Ultrasound and Acoustic Data from Child  Speech Therapy Sessions

For machines to lipread, or understand speech from lip movement, they decode
lip-motions (known as visemes) into the spoken sounds. We investigate the
visual speech channel to further our understanding of visemes. This has
applications beyond machine lipreading; speech therapists, animators, and
psychologists can benefit from this work. We explain the influence of speaker
individuality, and demonstrate how one can use visemes to boost lipreading.

本文探讨视觉语音信道对于机器口形识别、提高口形识别及其他领域，如语音治疗、动画制作、心理学的应用，特别研究讲话人个体差异的影响并演示了如何利用口形来提高口形识别的能力。