Current trends in multilingual speech processing

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Atypical aspects in speech concern speech that deviates from what is commonly considered normal or healthy. In this thesis, we propose novel methods for detection and analysis of these aspects, e.g. to monitor the temporary state of a speaker, diseases tha ...

Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...

Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is no ...

In light of steady progress in machine learning, automatic speech recognition (ASR) is entering more and more areas of our daily life, but people with dysarthria and other speech pathologies are left behind. Their voices are underrepresented in the trainin ...

Neural machine translation (MT) and text generation have recently reached very high levels of quality. However, both areas share a problem: in order to reach these levels, they require massive amounts of data. When this is not present, they lack generaliza ...

Dense image-based prediction methods have advanced tremendously in recent years. Their remarkable development has been possible due to the ample availability of real-world imagery. While these methods work well on photographs, their abilities do not genera ...

The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins. All these successful methods depend on a gradient-based learning algorithm to train a model on massive ...

Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...

In this paper we propose a novel virtual simulation-pilot engine for speeding up air traffic controller (ATCo) training by integrating different state-of-the-art artificial intelligence (AI)-based tools. The virtual simulation-pilot engine receives spoken ...

The respiratory system is an integral part of human speech production. As a consequence, there is a close relation between respiration and speech signal, and the produced speech signal carries breathing pattern related information. Speech can also be gener ...

Many pathologies cause impairments in the speech production mechanism resulting in reduced speech intelligibility and communicative ability. To assist the clinical diagnosis, treatment and management of speech disorders, automatic pathological speech asses ...

Automatic Speech Recognition (ASR), as the assistance of speech communication between pilots and air-traffic controllers, can significantly reduce the complexity of the task and increase the reliability of transmitted information. ASR application can lead ...

Automatic speech recognition (ASR) allows transcribing the communications between air traffic controllers (ATCOs) and aircraft pilots. The transcriptions are used later to extract ATC named entities, e.g., aircraft callsigns. One common challenge is speech ...

Magnetic Resonance Imaging (MRI) is an established technique to study in vivo neurological disorders such as Multiple Sclerosis (MS). To avoid errors on MRI data organization and automated processing, a standard called Brain Imaging Data Structure (BIDS) h ...

In the Digital Age, the analysis and exploration of unstructured document collections is of central importance to members of investigative professions, whether they might be scholars, journalists, paralegals, or analysts. In many of their domains, entities ...

Speech signal conveys several kinds of information such as a message, speaker identity, emotional state of the speaker and social state of the speaker. Automatic speech assessment is a broad area that refers to using automatic methods to predict human judg ...

In the literature, the task of dysarthric speech intelligibility assessment has been approached through development of different low-level feature representations, subspace modeling, phone confidence estimation or measurement of automatic speech recognitio ...

Respiration is an essential and primary mechanism for speech production. We first inhale and then produce speech while exhaling. When we run out of breath, we stop speaking and inhale. Though this process is involuntary, speech production involves a system ...

In communication systems, it is crucial to estimate the perceived quality of audio and speech. The industrial standards for many years have been PESQ, 3QUEST, and POLQA, which are intrusive methods. This restricts the possibilities of using these metrics i ...

Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer. In recent years, unsupervised and self-supervised techniques for learning speech representation were developed to foster automatic speech recognition. Up to ...

Current trends in multilingual speech processing

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Novel Methods For Detection And Analysis Of Atypical Aspects In Speech

Sparse Autoencoders for Speech Modeling and Recognition

Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?

On matching data and model in LF-MMI-based dysarthric speech recognition

Regularization Techniques for Low-Resource Machine Translation

Dense Image-based Predictions for Comics Analysis

Transformer Models for Vision

Text Representation Learning for Low Cost Natural Language Understanding

A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers

On Breathing Pattern Information in Synthetic Speech

Automatic pathological speech assessment

A Two-Step Approach To Leverage Contextual Data: Speech Recognition In Air-Traffic Communications

Bertraffic: Bert-Based Joint Speaker Role And Speaker Change Detection For Air Traffic Control Communications

BMAT: An open-source BIDS managing and analysis tool

ECCE: Entity-centric Corpus Exploration Using Contextual Implicit Networks

Novel Methods for Incorporating Prior Knowledge for Automatic Speech Assessment

Utterance Verification-Based Dysarthric Speech Intelligibility Assessment Using Phonetic Posterior Features

Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings

Non-Intrusive Speech Quality Assessment with Transfer Learning and Subject-specific Scaling

Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing