The last post concerning the upcoming HiTZ webinar included incorrect information about Dr. Sakti's bio and summary. This email contains the correct information.
Apologies for the mix up!
------------------------------------------------------------------------ Dear colleague,
We are happy to announce the next webinar in the Language Technology webinar series organized by the HiTZ research center (Basque Center for Language Technology, http://hitz.eus). This will be the final webinar for the 2021-2022 series. You can check the videos of previous webinars and the schedule for upcoming webinars here: http://www.hitz.eus/webinars
Next webinar:
* Speaker: *Sakriani Sakti* (Japan Advanced Institute of Science and
Technology)
* Title: Semi-supervised Learning for Low-resource Multilingual and
Multimodal Speech Processing with Machine Speech Chain
* Date: *May 5, 2022, 15:00 CET*
* Summary: The development of advanced spoken language technologies
based on automatic speech recognition (ASR) and text-to-speech
synthesis (TTS) has enabled computers to either learn how to listen
or speak. Many applications and services are now available but still
support fewer than 100 languages. Nearly 7000 living languages that
are spoken by 350 million people remain uncovered. This is because
the construction is commonly done based on machine learning trained
in a supervised fashion where a large amount of paired speech and
corresponding transcription is required. In this talk, we will
introduce a semi-supervised learning mechanism based on a machine
speech chain framework. First, we describe the primary machine
speech chain architecture that learns not only to listen or speak
but also to listen while speaking. The framework enables ASR and TTS
to teach each other given unpaired data. After that, we describe the
use of machine speech chain for code-switching and cross-lingual ASR
and TTS of several languages, including low-resourced ethnic
languages. Finally, we describe the recent multimodal machine chain
that mimics overall human communication to listen while speaking and
visualizing. With the support of image captioning and production
models, the framework enables ASR and TTS to improve their
performance using an image-only dataset.
* Bio: Sakriani Sakti is currently an associate professor at Japan
Advanced Institute of Science and Technology (JAIST) Japan, adjunct
associate professor at Nara Institute of Science and Technology
(NAIST) Japan, visiting research scientist at RIKEN Center for
Advanced Intelligent Project (RIKEN AIP) Japan, and adjunct
professor at the University of Indonesia. She received DAAD-Siemens
Program Asia 21st Century Award in 2000 to study in Communication
Technology, University of Ulm, Germany, and received her MSc degree
in 2002. During her thesis work, she worked with the Speech
Understanding Department, DaimlerChrysler Research Center, Ulm,
Germany. She then worked as a researcher at ATR Spoken Language
Communication (SLC) Laboratories Japan in 2003-2009, and NICT SLC
Groups Japan in 2006-2011, which established multilingual speech
recognition for speech-to-speech translation. While working with ATR
and NICT, Japan, she continued her study (2005-2008) with Dialog
Systems Group University of Ulm, Germany, and received her Ph.D.
degree in 2008. She was actively involved in international
collaboration activities such as Asian Pacific Telecommunity Project
(2003-2007) and various speech-to-speech translation research
projects, including A-STAR and U-STAR (2006-2011). In 2011-2017, she
was an assistant professor at the Augmented Human Communication
Laboratory, NAIST, Japan. She also served as a visiting scientific
researcher of INRIA Paris-Rocquencourt, France, in 2015-2016, under
JSPS Strategic Young Researcher Overseas Visits Program for
Accelerating Brain Circulation. In 2018–2021, she was a research
associate professor at NAIST and a research scientist at RIKEN,
Center for Advanced Intelligent Project AIP, Japan. Currently, she
is an associate professor at JAIST, adjunct associate professor at
NAIST, visiting research scientist at RIKEN AIP, and adjunct
professor at the University of Indonesia. She is a member of JNS,
SFN, ASJ, ISCA, IEICE, and IEEE. Furthermore, she is currently a
committee member of IEEE SLTC (2021-2023) and an associate editor of
the IEEE/ACM Transactions on Audio, Speech, and Language Processing
(2020-2023). She was a board member of Spoken Language Technologies
for Under-resourced languages (SLTU) and the general chair of
SLTU2016. She was also the general chair of the "Digital Revolution
for Under-resourced Languages (DigRevURL)" Workshop as the
Interspeech Special Session in 2017 and DigRevURL Asia in 2019. She
was also the organizing committee of the Zero Resource Speech
Challenge 2019 and 2020. She was also involved in creating joint
ELRA and ISCA Special Interest Group on Under-resourced Languages
(SIGUL) and served as SIGUL Board since 2018. Last year, in
collaboration with UNESCO and ELRA, she was also the organizing
committee of the International Conference of "Language Technologies
for All (LT4All): Enabling Linguistic Diversity and Multilingualism
Worldwide". Her research interests lie in deep learning & graphical
model framework, statistical pattern recognition, zero-resourced
speech technology, multilingual speech recognition and synthesis,
spoken language translation, social-affective dialog system, and
cognitive-communication.
Check past and upcoming webinars at the following url: http://www.hitz.eus/webinars If you are interested in participating, please complete this registration form: http://www.hitz.eus/webinar_izenematea
If you cannot attend this seminar, but you want to be informed of the following HiTZ webinars, please complete this registration form instead: http://www.hitz.eus/webinar_info
Best wishes,
HiTZ Zentroa
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