Search Results for author:
Found 16 papers, 5 papers with code
Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data
To generalize our findings across languages and disorders, we validated our approach on a dataset of Spanish-speaking Parkinson's disease patients, leveraging pretrained models from healthy English-speaking datasets, and demonstrated that careful pretraining on large-scale task-specific datasets can maintain favorable accuracy under DP constraints.
Speaker- and Text-Independent Estimation of Articulatory Movements and Phoneme Alignments from Speech
This paper introduces a novel combination of two tasks, previously treated separately: acoustic-to-articulatory speech inversion (AAI) and phoneme-to-articulatory (PTA) motion estimation.
Towards Intelligent Speech Assistants in Operating Rooms: A Multimodal Model for Surgical Workflow Analysis
To develop intelligent speech assistants and integrate them seamlessly with intra-operative decision-support frameworks, accurate and efficient surgical phase recognition is a prerequisite.
The Impact of Speech Anonymization on Pathology and Its Limits
This study investigates anonymization's impact on pathological speech across over 2, 700 speakers from multiple German institutions, focusing on privacy, pathological utility, and demographic fairness.
Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages
Parkinson's disease (PD) is a neurological disorder impacting a person's speech.
PoCaP Corpus: A Multimodal Dataset for Smart Operating Room Speech Assistant using Interventional Radiology Workflow Analysis
This paper presents a new multimodal interventional radiology dataset, called PoCaP (Port Catheter Placement) Corpus.
The effect of speech pathology on automatic speaker verification -- a large-scale study
Navigating the challenges of data-driven speech processing, one of the primary hurdles is accessing reliable pathological speech data.
Disentangled Latent Speech Representation for Automatic Pathological Intelligibility Assessment
Our results are among the first to show that disentangled speech representations can be used for automatic pathological speech intelligibility assessment, resulting in a reference speaker pair invariant method, applicable in scenarios with only few utterances available.
Self-Supervised Speech Representations Preserve Speech Characteristics while Anonymizing Voices
Collecting speech data is an important step in training speech recognition systems and other speech-based machine learning models.
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition
Compared to using Fbank features, XLSR-based features reduced WERs by 6. 8%, 22. 0%, and 7. 0% for the UASpeech, PC-GITA, and EasyCall corpus, respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks
With the increasing number of online learning material in the web, search for specific content in lecture videos can be time consuming.
Does Proprietary Software Still Offer Protection of Intellectual Property in the Age of Machine Learning? -- A Case Study using Dual Energy CT Data
In the domain of medical image processing, medical device manufacturers protect their intellectual property in many cases by shipping only compiled software, i. e. binary code which can be executed but is difficult to be understood by a potential attacker.
Known Operator Learning and Hybrid Machine Learning in Medical Imaging --- A Review of the Past, the Present, and the Future
In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging.
A Scalable Chatbot Platform Leveraging Online Community Posts: A Proof-of-Concept Study
The development of natural language processing algorithms and the explosive growth of conversational data are encouraging researches on the human-computer conversation.
Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training
Dysarthria is a motor speech impairment affecting millions of people.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Self-imitating Feedback Generation Using GAN for Computer-Assisted Pronunciation Training
Trained on 97, 200 spectrogram images of short utterances produced by native and non-native speakers of Korean, the generator is able to successfully transform the non-native spectrogram input to a spectrogram with properties of self-imitating feedback.
