Abstract

Voice conversion (VC) is a significant aspect of artificial intelligence. It is the study of how to convert one’s voice to sound like that of another without changing the linguistic content. Voice conversion belongs to a general technical field of speech synthesis, which converts text to speech or changes the properties of speech, for example, voice identity, emotion, and accents. Voice conversion involves multiple speech processing techniques, such as speech analysis, spectral conversion, prosody conversion, speaker characterization, and vocoding. With the recent advances in theory and practice, we are now able to produce human-like voice quality with high speaker similarity. In this talk, Dr. Sisman will present the recent advances in voice conversion and discuss their promise and limitations. Dr. Sisman will also provide a summary of the available resources for expressive voice conversion research.

Biography

Dr. Berrak Sisman (Member, IEEE) received the Ph.D. degree in electrical and computer engineering from National University of Singapore in 2020, fully funded by A*STAR Graduate Academy under Singapore International Graduate Award (SINGA). She is currently working as a tenure-track Assistant Professor at the Erik Jonsson School Department of Electrical and Computer Engineering at University of Texas at Dallas, United States. Prior to joining UT Dallas, she was a faculty member at Singapore University of Technology and Design (2020-2022). She was a Postdoctoral Research Fellow at the National University of Singapore (2019-2020). She was an exchange doctoral student at the University of Edinburgh and a visiting scholar at The Centre for Speech Technology Research (CSTR), University of Edinburgh (2019). She was a visiting researcher at RIKEN Advanced Intelligence Project in Japan (2018). Her research is focused on machine learning, signal processing, emotion, speech synthesis and voice conversion.

Dr. Sisman has served as the Area Chair at INTERSPEECH 2021, INTERSPEECH 2022, IEEE SLT 2022 and as the Publication Chair at ICASSP 2022. She has been elected as a member of the IEEE Speech and Language Processing Technical Committee (SLTC) in the area of Speech Synthesis for the term from January 2022 to December 2024. She plays leadership roles in conference organizations and active in technical committees. She has served as the General Coordinator of the Student Advisory Committee (SAC) of International Speech Communication Association (ISCA).

Abstract

Abstract

Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe, high-quality results, all while keeping ethical considerations in mind? In this talk, I introduce No Language Left Behind, an initiative to break language barriers for low-resource languages. In No Language Left Behind, we took on the low-resource language translation challenge by first contextualizing the need for translation support through exploratory interviews with native speakers. Then, we created datasets and models aimed at narrowing the performance gap between low and high-resource languages. We proposed multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Critically, we evaluated the performance of over 40,000 different translation directions using a human-translated benchmark, Flores-200, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system in an open-source manner.

Biography

Angela is a research scientist at Meta AI Research in New York, focusing on supporting efforts in speech and language research. Recent projects include No Language Left Behind (https://ai.facebook.com/research/no-language-left-behind/) and Universal Speech Translation for Unwritten Languages (https://ai.facebook.com/blog/ai-translation-hokkien/). Before translation, Angela previously focused on research in on-device models for NLP and computer vision and text generation.