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Section: Publications & Resources

The 5th DNS Challenge aims to motivate development of DNS models with great speech quality in presence of reverberation, noise and interfering (neighboring) talkers. DNS has gained momentum given new trends of hybrid and remote work in a variety of daily-life scenarios. Improving speech quality reduce meeting fatigue and improve clarity of communication. 

Section: Publications & Resources

The Multimodal Information Based Speech Processing (MISP) 2022 Challenge aims to extend the application of signal processing technology in specific scenarios, using audio and video data. We target the home TV scenario, where 2-6 people communicate with each other with TV noise in the background. Our new tracks focus on audio-visual speaker diarization (AVSD), and audio-visual diarization and recognition (AVDR).

Section: Publications & Resources

This signal processing challenge is designed to get the latest advancements in speech enhancement applied to hearing aids. 430 million people worldwide require rehabilitation to address hearing loss. Yet even in developed countries, only 40% of people who could benefit from hearing aids have them and use them often enough, because they believe that hearing aids perform poorly.

Section: Publications & Resources

The L3DAS23 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with a particular focus on 3D speech enhancement (SE) and 3D sound event localization and detection (SELD) in augmented reality applications.

Section: Publications & Resources

The challenge will concern the analysis and processing of long-term continuous recordings of biosignals recorded from wearable sensors embedded in smartwatches, in order to extract high-level representations of the wearer’s activity and behavior for two downstream tasks: 1) Identification of the wearer of the smartwatch, and 2) Detection of relapses in patients in the psychotic spectrum. 

Section: Publications & Resources

Epilepsy is one of the most common neurological disorders, affecting almost 1% of the population worldwide. The categorization of seizures is usually made based on the seizure onset zone (area of the brain where the seizure initiates) the progression of the seizure and the awareness status of the patient that experience the seizure. Focal onset seizures are the most common type of seizures in adults with epilepsy.

Section: Publications & Resources

Various neuroimaging techniques can be used to investigate how the brain processes sound. Electroencephalography (EEG) is popular because it is relatively easy to conduct and has a high temporal resolution. An increasingly popular method in these fields is to relate a person’s electroencephalogram (EEG) to a feature of the natural speech signal they were listening to. This is typically done using linear regression or relatively simple neural networks to predict the EEG signal from the stimulus or to decode the stimulus from the EEG.

Section: Publications & Resources

Spoken Language Understanding (SLU) is a critical component of conversational voice assistants, requiring converting user utterances into a structured format for task executions. SLU systems typically consist of an ASR component to convert audio to text and an NLU component to convert text to a tree like structure, however recently, E2E SLU systems have also become of increasing interest in order to increase quality, model efficiency, and data efficiency.

Section: Publications & Resources

The MADReSS SPGC targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of Alzheimer’s Dementia (AD). Dementia is a category of neurodegenerative diseases that entails a long-term and usually gradual decrease of cognitive functioning.

Section: Publications & Resources

The advent of spoken language processing (SLP) technologies on meeting transcripts is crucial for distilling, organizing, and prioritizing information. Meeting transcripts impose two key challenges to SLP tasks.  First, meeting transcripts exhibit a wide variety of spoken language phenomena, leading to dramatic performance degradation.  Second, meeting transcripts are usually long-form documents with several thousand words or more, posing a great challenge to mainstay Transformer-based models with high computational complexity.