STAN: A stuttering therapy analysis helper
- URL: http://arxiv.org/abs/2106.09545v1
- Date: Tue, 15 Jun 2021 13:48:12 GMT
- Title: STAN: A stuttering therapy analysis helper
- Authors: Sebastian P. Bayerl, Marc Wenninger, Jochen Schmidt, Alexander Wolff
von Gudenberg, Korbinian Riedhammer
- Abstract summary: Stuttering is a complex speech disorder identified by repeti-tions, prolongations of sounds, syllables or words and blockswhile speaking.
We introduceSTAN, a system to aid speech therapists in stuttering therapysessions.
- Score: 59.37911277681339
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Stuttering is a complex speech disorder identified by repeti-tions,
prolongations of sounds, syllables or words and blockswhile speaking. Specific
stuttering behaviour differs strongly,thus needing personalized therapy.
Therapy sessions requirea high level of concentration by the therapist. We
introduceSTAN, a system to aid speech therapists in stuttering therapysessions.
Such an automated feedback system can lower thecognitive load on the therapist
and thereby enable a more con-sistent therapy as well as allowing analysis of
stuttering overthe span of multiple therapy sessions.
Related papers
- Self-supervised Speech Models for Word-Level Stuttered Speech Detection [66.46810024006712]
We introduce a word-level stuttering speech detection model leveraging self-supervised speech models.
Our evaluation demonstrates that our model surpasses previous approaches in word-level stuttering speech detection.
arXiv Detail & Related papers (2024-09-16T20:18:20Z) - Empowering Psychotherapy with Large Language Models: Cognitive
Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting.
DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas.
Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
arXiv Detail & Related papers (2023-10-11T02:47:21Z) - GDPR Compliant Collection of Therapist-Patient-Dialogues [48.091760741427656]
We elaborate on the challenges we faced in starting our collection of therapist-patient dialogues in a psychiatry clinic under the General Data Privacy Regulation of the European Union.
We give an overview of each step in our procedure and point out the potential pitfalls to motivate further research in this field.
arXiv Detail & Related papers (2022-11-22T15:51:10Z) - Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0 [0.22940141855172028]
Fine-tuning wav2vec 2.0 for the classification of stuttering on a sizeable English corpus boosts the effectiveness of the general-purpose features.
We evaluate our method on Fluencybank and the German therapy-centric Kassel State of Fluency dataset.
arXiv Detail & Related papers (2022-04-07T13:02:12Z) - KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset
of Stuttering [58.91587609873915]
This work introduces the Kassel State of Fluency (KSoF), a therapy-based dataset containing over 5500 clips of stuttering PWSs.
The audio was recorded during therapy sessions at the Institut der Kasseler Stottertherapie.
arXiv Detail & Related papers (2022-03-10T14:17:07Z) - Machine Learning for Stuttering Identification: Review, Challenges &
Future Directions [9.726119468893721]
Stuttering is a speech disorder during which the flow of speech is interrupted by involuntary pauses and repetition of sounds.
Recent developments in machine and deep learning have dramatically revolutionized speech domain.
This work fills the gap by trying to bring researchers together from interdisciplinary fields.
arXiv Detail & Related papers (2021-07-08T18:15:20Z) - NUVA: A Naming Utterance Verifier for Aphasia Treatment [49.114436579008476]
Assessment of speech performance using picture naming tasks is a key method for both diagnosis and monitoring of responses to treatment interventions by people with aphasia (PWA)
Here we present NUVA, an utterance verification system incorporating a deep learning element that classifies 'correct' versus'incorrect' naming attempts from aphasic stroke patients.
When tested on eight native British-English speaking PWA the system's performance accuracy ranged between 83.6% to 93.6%, with a 10-fold cross-validation mean of 89.5%.
arXiv Detail & Related papers (2021-02-10T13:00:29Z) - Stutter Diagnosis and Therapy System Based on Deep Learning [2.3581263491506097]
Stuttering, also called stammering, is a communication disorder that breaks the continuity of the speech.
This paper focuses on the implementation of a stutter diagnosis agent using Gated Recurrent CNN on MFCC audio features and therapy recommendation agent using SVM.
arXiv Detail & Related papers (2020-07-13T10:24:02Z) - Towards Automated Assessment of Stuttering and Stuttering Therapy [0.22940141855172028]
Stuttering is a complex speech disorder that can be identified by repetitions, prolongations of sounds, syllables or words, and blocks while speaking.
Common methods for the assessment of stuttering severity include percent stuttered syllables (% SS), the average of the three longest stuttering symptoms during a speech task, or the recently introduced Speech Efficiency Score (SES)
This paper introduces the Speech Control Index (SCI), a new method to evaluate the severity of stuttering.
arXiv Detail & Related papers (2020-06-16T14:50:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.