Synthesized Speech
Synthesized speech research focuses on creating realistic and natural-sounding artificial speech, primarily for applications like voice assistants, audiobooks, and accessibility tools. Current efforts concentrate on improving the naturalness and expressiveness of synthesized speech, often using deep learning models like GANs, diffusion models, and transformers, and addressing challenges such as detecting synthetic speech (deepfakes) and mitigating biases in these detection systems. This field is crucial for advancing human-computer interaction, improving accessibility technologies, and combating the malicious use of synthetic audio in fraud and disinformation.
Papers
PoeticTTS -- Controllable Poetry Reading for Literary Studies
Julia Koch, Florian Lux, Nadja Schauffler, Toni Bernhart, Felix Dieterle, Jonas Kuhn, Sandra Richter, Gabriel Viehhauser, Ngoc Thang Vu
Speaker consistency loss and step-wise optimization for semi-supervised joint training of TTS and ASR using unpaired text data
Naoki Makishima, Satoshi Suzuki, Atsushi Ando, Ryo Masumura