5 papers
VARAN: Variational Inference for Self-Supervised Speech Models Fine-Tuning on Downstream Tasks
Daria Diatlova, Nikita Balagansky, Alexander Varlamov +1
Conventional methods for aggregating layers in fine-tuned self-supervised speech models, such as using the final layer or weighted sum, suffer from information bottlenecks and stat…
NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech
Maksim Borisov, Egor Spirin, Daria Diatlova
Current expressive speech synthesis models are constrained by the limited availability of open-source datasets containing diverse nonverbal vocalizations (NVs). In this work, we in…
METR: Image Watermarking with Large Number of Unique Messages
Alexander Varlamov, Daria Diatlova, Egor Spirin
Improvements in diffusion models have boosted the quality of image generation, which has led researchers, companies, and creators to focus on improving watermarking algorithms. Thi…
Adapting WavLM for Speech Emotion Recognition
Daria Diatlova, Anton Udalov, Vitalii Shutov +1
Recently, the usage of speech self-supervised models (SSL) for downstream tasks has been drawing a lot of attention. While large pre-trained models commonly outperform smaller mode…
EmoSpeech: Guiding FastSpeech2 Towards Emotional Text to Speech
Daria Diatlova, Vitaly Shutov
State-of-the-art speech synthesis models try to get as close as possible to the human voice. Hence, modelling emotions is an essential part of Text-To-Speech (TTS) research. In our…