4 accepted papers
Traditional voice conversion (VC) methods typically attempt to separate speaker identity and linguistic information into distinct representations, which are then combined to reconstruct the audio. However, effectively disentangling these factors remains challenging, often leading to information loss
Voice conversion (VC) aims to transform speech from a source speaker to a target speaker while preserving the original linguistic content. However, existing VC models typically require large annotated datasets containing transcripts and speaker labels, posing significant challenges for low-resource…
Recent research in speaker recognition aims to address vulnerabilities due to variations between enrolment and test utterances, particularly in the multi-genre phenomenon where the utterances are in different speech genres. Previous resources for Vietnamese speaker recognition are either limited in…
Speech Emotion Recognition (SER) is an essential task in spoken language processing, applicable across various domains. While research on SER systems for English datasets is growing rapidly, the reliability of these models for tonal languages remains a significant concern. Therefore, this paper intr…