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Found 23 papers, 4 papers with code
Deep Speech Synthesis from Multimodal Articulatory Representations
The amount of articulatory data available for training deep learning models is much less compared to acoustic speech data.
Investigating Effective Speaker Property Privacy Protection in Federated Learning for Speech Emotion Recognition
To this end, this paper focuses on investigating the security of FL for SER concerning property inference attacks.
Speech as a Biomarker for Disease Detection
The proposed framework for reference speech characterization and disease detection is designed to support the medical community by providing clinically meaningful explanations that can serve as a valuable second opinion.
NeuroSpex: Neuro-Guided Speaker Extraction with Cross-Modal Attention
In the study of auditory attention, it has been revealed that there exists a robust correlation between attended speech and elicited neural responses, measurable through electroencephalography (EEG).
On the Role of Visual Grounding in VQA
Finally, we propose an approach to create OOD tests that properly emphasize a requirement for VG, and show how to improve performance on them.
Speech Emotion Recognition under Resource Constraints with Data Distillation
Speech emotion recognition (SER) plays a crucial role in human-computer interaction.
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition
Speech contains rich information on the emotions of humans, and Speech Emotion Recognition (SER) has been an important topic in the area of human-computer interaction.
Uncovering the Full Potential of Visual Grounding Methods in VQA
In this study, we demonstrate that current evaluation schemes for VG-methods are problematic due to the flawed assumption of availability of relevant visual information.
NeuroHeed: Neuro-Steered Speaker Extraction using EEG Signals
We propose both an offline and an online NeuroHeed, with the latter designed for real-time inference.
Measuring Faithful and Plausible Visual Grounding in VQA
Metrics for Visual Grounding (VG) in Visual Question Answering (VQA) systems primarily aim to measure a system's reliance on relevant parts of the image when inferring an answer to the given question.
Visually Grounded VQA by Lattice-based Retrieval
Visual Grounding (VG) in Visual Question Answering (VQA) systems describes how well a system manages to tie a question and its answer to relevant image regions.
Adventurer's Treasure Hunt: A Transparent System for Visually Grounded Compositional Visual Question Answering based on Scene Graphs
With the expressed goal of improving system transparency and visual grounding in the reasoning process in VQA, we present a modular system for the task of compositional VQA based on scene graphs.
Analysis of GlobalPhone and Ethiopian Languages Speech Corpora for Multilingual ASR
From GP, Turkish, Uyghur and Croatian are found to have much overlap with the Ethiopian languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Automatic Speech Recognition for Uyghur through Multilingual Acoustic Modeling
For the developing of multilingual speech recognition system for Uyghur, we used Turkish as donor language, which we selected from GlobalPhone corpus as the most similar language to Uyghur.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
DNN-Based Multilingual Automatic Speech Recognition for Wolaytta using Oromo Speech
Our results show the possibility of developing ASR system for a language, if we have pronunciation dictionary and language model, using an existing speech corpus of another language irrespective of their language family.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Building Language Models for Morphological Rich Low-Resource Languages using Data from Related Donor Languages: the Case of Uyghur
In this work, we show our effort to build word-based as well as morpheme-based language models for Uyghur, a language that combines both challenges, i. e. it is a low-resource and agglutinative language.
TSFEL: Time Series Feature Extraction Library
Time series feature extraction is one of the preliminary steps of conventional machine learning pipelines.
Syntactic and Semantic Features For Code-Switching Factored Language Models
The experimental results reveal that Brown word clusters, part-of-speech tags and open-class words are the most effective at reducing the perplexity of factored language models on the Mandarin-English Code-Switching corpus SEAME.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Towards Automatic Transcription of ILSE ― an Interdisciplinary Longitudinal Study of Adult Development and Aging
Using a recursive long audio alignment procedure 96 hours of the transcribed data have been made accessible for ASR training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
GlobalPhone: Pronunciation Dictionaries in 20 Languages
This paper describes the advances in the multilingual text and speech database GlobalPhone, a multilingual database of high-quality read speech with corresponding transcriptions and pronunciation dictionaries in 20 languages.
Correlated Bigram LSA for Unsupervised Language Model Adaptation
We propose using correlated bigram LSA for unsupervised LM adaptation for automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
