12 accepted papers
Keyword spotting (KWS) refers to the task of identifying a set of predefined words in audio streams. With the advances seen recently with deep neural networks, it has become a popular technology to activate and control small devices, such as voice assistants. Relying on such models for edge devices,…
Cough is an important symptom of numerous respiratory diseases, including COVID-19. While different cough phases (i.e., inhalation, compression, and expulsion) have been shown to be related to different pathological origins, existing cough-based COVID-19 detection systems rely on the entire cough re…
Self-supervised speech pre-training enables deep neural network models to capture meaningful and disentangled factors from raw waveform signals. The learned universal speech representations can then be used across numerous down-stream tasks. These representations, however, are sensitive to distribut…
Existing speech-based coronavirus disease 2019 (COVID-19) detection systems provide poor interpretability and limited robustness to unseen data conditions. In this paper, we propose a system to overcome these limitations. In particular, we propose to fuse two different feature modalities with patien…
Hospital workers are known to work long hours in a highly stressful environment. The COVID-19 pandemic has increased this burden multi-fold. Pre-COVID statistics already showed that one in every three nurses reported burnout, thus affecting patient satisfaction and the quality of their provided serv…
As automatic speaker recognizer systems become mainstream, voice spoofing attacks are on the rise. Common attack strategies include replay, the use of text-to-speech synthesis, and voice conversion systems. While previouslyproposed end-to-end detection frameworks have shown to be effective in spotti…
Non-intrusive estimators for acoustic parameters like the direct-to-reverberation ratio (DRR) are useful tools but still perform weakly as shown in the acoustic characterization of environments (ACE) challenge. In this paper, we develop a novel dual-channel metric based on the modulation energy doma…
Efforts are afoot to design better context-aware human-computer interaction techniques that have knowledge of both their surrounding and the affective state of the user. One of the most important nonverbal behavioural cues for affective human-machine interaction is laughter. Automatic detection of l…
In this study we investigate whether or not event-related (de)synchronisation (ERD/ERS) can be used to differentiate between 27 healthy elderly, 21 subjects diagnosed with amnestic mild cognitive impairment (aMCI) and 16 mild Alzheimer's disease (AD) patients. Using 32-channel EEG recordings, we mea…
Reverberation and noise are known to be the two most important culprits for poor performance in far-field speech applications, such as automatic speech recognition. Recent research has suggested that reverberation-aware speech enhancement (or speech technologies, in general) could be used to improve…
In this paper, automatic speaker verification using normal and whispered speech is explored. Typically, for speaker verification systems with varying vocal effort inputs, standard solutions such as feature mapping or addition of data during parameter estimation (training) and enrollment stages resul…
To enhance the communication experience of workers equipped with hearing protection devices and radio communication in noisy environments, alternative methods of speech capture have been utilized. One such approach uses speech captured by a microphone in an occluded ear canal. Although high in signa…