This study presents a valuable advance in reconstructing naturalistic speech from intracranial ECoG data using a dual-pathway model. The evidence supporting the claims of the authors is solid. This ...
Abstract: Currently, the prevailing approach in voice conversion (VC) involves separating clearer linguistic information from the source audio and then reconstructing it with the identity of the ...
Abstract: Depression is a significant mental health problem and presents a challenge for the machine learning field in the detection of this illness. This study explores automated depression ...
Speech Synthesis explores advancements in speech technology and linguistics and machine learning. Expressive speech-to-speech translation (S2ST) is a key research topic in seamless communication, ...
Traditional methods of diagnosing mental-health conditions require patients to speak directly to a psychiatrist. Sensible in theory, such assessments can, in practice, take months to schedule and ...
Deep learning has significantly advanced text-to-speech (TTS) systems. These neural network-based systems have enhanced speech synthesis quality and are increasingly vital in applications like ...
When West Virginia University President Gordon Gee virtually addressed the school’s faculty during a Faculty Senate meeting earlier this week, he defended the university’s plan to implement program ...
The Temporal Modulation Spectrum Toolbox is a Matlab toolbox designed for the computation of amplitude and f0 modulation spectra and spectrograms. This versatile toolbox has been developed as part of ...
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