Search Results for author:
Found 17 papers, 2 papers with code
Classifying Dementia in the Presence of Depression: A Cross-Corpus Study
Automated dementia screening enables early detection and intervention, reducing costs to healthcare systems and increasing quality of life for those affected.
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem
Most stuttering detection and classification research has viewed stuttering as a multi-class classification problem or a binary detection task for each dysfluency type; however, this does not match the nature of stuttering, in which one dysfluency seldom comes alone but rather co-occurs with others.
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem
This work explores an approach based on a modified wav2vec 2. 0 system for end-to-end stuttering detection and classification as a multi-label problem.
Influence of Utterance and Speaker Characteristics on the Classification of Children with Cleft Lip and Palate
Recent findings show that pre-trained wav2vec 2. 0 models are reliable feature extractors for various speaker characteristics classification tasks.
Multi-class Detection of Pathological Speech with Latent Features: How does it perform on unseen data?
The detection of pathologies from speech features is usually defined as a binary classification task with one class representing a specific pathology and the other class representing healthy speech.
What can Speech and Language Tell us About the Working Alliance in Psychotherapy
To the best of our knowledge, this is the first and a novel study to exploit speech and language for characterising working alliance.
Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments using Acoustic Features
Standardized tests play a crucial role in the detection of cognitive impairment.
The Influence of Dataset Partitioning on Dysfluency Detection Systems
This paper empirically investigates the influence of different data splits and splitting strategies on the performance of dysfluency detection systems.
The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes
The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Vocalisations and Stuttering Sub-Challenges, a classification on human non-verbal vocalisations and speech has to be made; the Activity Sub-Challenge aims at beyond-audio human activity recognition from smartwatch sensor data; and in the Mosquitoes Sub-Challenge, mosquitoes need to be detected.
Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0
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.
Detecting Vocal Fatigue with Neural Embeddings
Vocal fatigue refers to the feeling of tiredness and weakness of voice due to extended utilization.
KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering
To be able to monitor speech behavior over a long time, the ability to detect stuttering events and modifications in speech could help PWSs and speech pathologists to track the level of fluency.
Detecting Emotion Carriers by Combining Acoustic and Lexical Representations
However, in this work, we focus on Emotion Carriers (EC) defined as the segments (speech or text) that best explain the emotional state of the narrator ("loss of father", "made me choose").
STAN: A stuttering therapy analysis helper
Stuttering is a complex speech disorder identified by repeti-tions, prolongations of sounds, syllables or words and blockswhile speaking.
Towards Automated Assessment of Stuttering and Stuttering Therapy
This paper introduces the Speech Control Index (SCI), a new method to evaluate the severity of stuttering.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Comparison of Hybrid and End-to-End Models for Syllable Recognition
The best word error rate (WER) regarding syllables was achieved using kaldi with a 4-gram LM, modeling all syllables observed in the training set.
Timage -- A Robust Time Series Classification Pipeline
Time series are series of values ordered by time.
