BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical and Computer Engineering - ECPv6.17.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Electrical and Computer Engineering
X-ORIGINAL-URL:https://engineering.jhu.edu/ece
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20190310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20191103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200402T150000
DTEND;TZID=America/New_York:20200402T160000
DTSTAMP:20210628T204256Z
CREATED:20210628T204256Z
LAST-MODIFIED:20210628T204256Z
UID:554110-1585839600-1585843200@engineering.jhu.edu
SUMMARY:Seminar: Shinji Watanabe
DESCRIPTION:This presentation is happening remotely. Click this link as early as 15 minutes before the scheduled start time of the presentation to watch in a Zoom meeting. Title: Interpretable End-to-End Neural Network for Audio and Speech ProcessingAbstract: This talk introduces extensions of the basic end-to-end automatic speech recognition (ASR) architecture by focusing on its integration function to tackle major problems faced by current ASR technologies in adverse environments including cocktail party and data sparseness problems. The first topic is to integrate microphone-array signal processing\, speech separation\, and speech recognition in a single neural network to realize multichannel multi-speaker ASR for the cocktail party problem. Our architecture is carefully designed to maintain the role of each module as a differentiable subnetwork so that we can jointly optimize the whole network but still keep the interpretability of each subnetwork including the speech separation\, speech enhancement\, and acoustic beamforming abilities in addition to ASR. The second topic is based on semi-supervised training using cycle-consistency\, which enables us to leverage unpaired speech and/or text data by integrating ASR with text-to-speech (TTS) within the end-to end framework. This scheme can be regarded as an interpretable disentanglement of audio signals with explicit decomposition of linguistic characteristics by ASR and speaker and speaking style characteristics by speaker embedding. These explicitly decomposed characteristics are converted back to the original audio signals by neural TTS; thus we form an acoustic feedback loop based on speech recognition and synthesis like human hearing\, and both components can be jointly optimized only with the audio data.
URL:https://engineering.jhu.edu/ece/event/seminar-shinji-watanabe/
END:VEVENT
END:VCALENDAR