Towards Automated Assessment of Stuttering and Stuttering Therapy
- URL: http://arxiv.org/abs/2006.09222v1
- Date: Tue, 16 Jun 2020 14:50:56 GMT
- Title: Towards Automated Assessment of Stuttering and Stuttering Therapy
- Authors: Sebastian P. Bayerl, Florian H\"onig, Joelle Reister and Korbinian
Riedhammer
- Abstract summary: 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.
- Score: 0.22940141855172028
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Stuttering is a complex speech disorder that can be identified by
repetitions, prolongations of sounds, syllables or words, and blocks while
speaking. Severity assessment is usually done by a speech therapist. While
attempts at automated assessment were made, it is rarely used in therapy.
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. Unlike SES, it can also be used to
assess therapy success for fluency shaping. We evaluate both SES and SCI on a
new comprehensively labeled dataset containing stuttered German speech of
clients prior to, during, and after undergoing stuttering therapy. Phone
alignments of an automatic speech recognition system are statistically
evaluated in relation to their relative position to labeled stuttering events.
The results indicate that phone length distributions differ with respect to
their position in and around labeled stuttering events
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) - Automatically measuring speech fluency in people with aphasia: first
achievements using read-speech data [55.84746218227712]
This study aims at assessing the relevance of a signalprocessingalgorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency.
arXiv Detail & Related papers (2023-08-09T07:51:40Z) - 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) - Investigation of Data Augmentation Techniques for Disordered Speech
Recognition [69.50670302435174]
This paper investigates a set of data augmentation techniques for disordered speech recognition.
Both normal and disordered speech were exploited in the augmentation process.
The final speaker adapted system constructed using the UASpeech corpus and the best augmentation approach based on speed perturbation produced up to 2.92% absolute word error rate (WER)
arXiv Detail & Related papers (2022-01-14T17:09:22Z) - STAN: A stuttering therapy analysis helper [59.37911277681339]
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.
arXiv Detail & Related papers (2021-06-15T13:48:12Z) - 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)
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.