Papers by Florian Lux
Low-Resource Multilingual and Zero-Shot Multispeaker TTS (2022.aacl-main)
Copied to clipboard
| Challenge: | Currently, the amount of data needed for TTS is limited to the vast majority of the spoken languages. |
| Approach: | They propose to use language agnostic meta learning procedure to learn speaking a new language with just 5 minutes of training data while retaining the ability to infer the voice of even unseen speakers. |
| Outcome: | The proposed approach is able to learn speaking a new language using just 5 minutes of training data while retaining the ability to infer the voice of even unseen speakers in the newly learned language. |
Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features (2022.acl-long)
Copied to clipboard
| Challenge: | Recent advances in text-to-speech systems allow for speech synthesis with unprecedented quality and controllability. |
| Approach: | They use embeddings derived from articulatory vectors rather than phoneme identities to learn phoneme representations that hold across languages. |
| Outcome: | The proposed models fine-tuned on 30 minutes of data in a previously unseen language with language agnostic meta learning. |
ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents (2020.acl-demos)
Copied to clipboard
Chia-Yu Li, Daniel Ortega, Dirk Väth, Florian Lux, Lindsey Vanderlyn, Maximilian Schmidt, Michael Neumann, Moritz Völkel, Pavel Denisov, Sabrina Jenne, Zorica Kacarevic, Ngoc Thang Vu
| Challenge: | Existing toolkits for developing dialog systems are limited to core components and do not support multi-modal processing and social signals. |
| Approach: | They propose to use ADVISER to develop multi-modal dialog agents using multi-text and social signals. |
| Outcome: | The proposed toolkit is flexible, easy to use, and easy to extend for linguists and cognitive scientists, thereby providing a flexible platform for collaborative research. |