- Date: April 17, 2018
Awarded to: Zhong-Qiu Wang
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - Former MERL intern Zhong-Qiu Wang (Ph.D. Candidate at Ohio State University) has received a Best Student Paper Award at the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018) for the paper "Multi-Channel Deep Clustering: Discriminative Spectral and Spatial Embeddings for Speaker-Independent Speech Separation" by Zhong-Qiu Wang, Jonathan Le Roux, and John Hershey. The paper presents work performed during Zhong-Qiu's internship at MERL in the summer 2017, extending MERL's pioneering Deep Clustering framework for speech separation to a multi-channel setup. The award was received on behalf on Zhong-Qiu by MERL researcher and co-author Jonathan Le Roux during the conference, held in Calgary April 15-20.
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- Date: April 15, 2018 - April 20, 2018
Where: Calgary, AB
MERL Contacts: Petros T. Boufounos; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Philip V. Orlik; Pu (Perry) Wang
Research Areas: Computational Sensing, Digital Video, Speech & Audio
Brief - MERL researchers are presenting 9 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held in Calgary from April 15-20, 2018. Topics to be presented include recent advances in speech recognition, audio processing, and computational sensing. MERL is also a sponsor of the conference.
ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.
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- Date: February 5, 2018
Where: National Public Radio (NPR)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - MERL's speech separation technology was featured in NPR's All Things Considered, as part of an episode of All Tech Considered on artificial intelligence, "Can Computers Learn Like Humans?". An example separating the overlapped speech of two of the show's hosts was played on the air.
The technology is based on a proprietary deep learning method called Deep Clustering. It is the world's first technology that separates in real time the simultaneous speech of multiple unknown speakers recorded with a single microphone. It is a key step towards building machines that can interact in noisy environments, in the same way that humans can have meaningful conversations in the presence of many other conversations.
A live demonstration was featured in Mitsubishi Electric Corporation's Annual R&D Open House last year, and was also covered in international media at the time.
(Photo credit: Sam Rowe for NPR)
Link:
"Can Computers Learn Like Humans?" (NPR, All Things Considered)
MERL Deep Clustering Demo.
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- Date: January 31, 2018
MERL Contact: Chiori Hori
Research Area: Speech & Audio
Brief - Chiori Hori has been elected to serve on the Speech and Language Processing Technical Committee (SLTC) of the IEEE Signal Processing Society for a 3-year term.
The SLTC promotes and influences all the technical areas of speech and language processing such as speech recognition, speech synthesis, spoken language understanding, speech to speech translation, spoken dialog management, speech indexing, information extraction from audio, and speaker and language recognition.
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- Date: December 16, 2017 - December 20, 2017
Where: Okinawa, Japan
MERL Contacts: Chiori Hori; Jonathan Le Roux
Research Area: Speech & Audio
Brief - MERL presented three papers at the 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), which was held in Okinawa, Japan from December 16-20, 2017. ASRU is the premier speech workshop, bringing together researchers from academia and industry in an intimate and collegial setting. More than 270 people attended the event this year, a record number. MERL's Speech and Audio Team was a key part of the organization of the workshop, with John Hershey serving as General Chair, Chiori Hori as Sponsorship Chair, and Jonathan Le Roux as Demonstration Chair. Two of the papers by MERL were selected among the 10 finalists for the best paper award. Mitsubishi Electric and MERL were also Platinum sponsors of the conference, with MERL awarding the MERL Best Student Paper Award.
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- Date: May 24, 2017
Where: Tokyo, Japan
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - Mitsubishi Electric Corporation announced that it has created the world's first technology that separates in real time the simultaneous speech of multiple unknown speakers recorded with a single microphone. It's a key step towards building machines that can interact in noisy environments, in the same way that humans can have meaningful conversations in the presence of many other conversations. In tests, the simultaneous speeches of two and three people were separated with up to 90 and 80 percent accuracy, respectively. The novel technology, which was realized with Mitsubishi Electric's proprietary "Deep Clustering" method based on artificial intelligence (AI), is expected to contribute to more intelligible voice communications and more accurate automatic speech recognition. A characteristic feature of this approach is its versatility, in the sense that voices can be separated regardless of their language or the gender of the speakers. A live speech separation demonstration that took place on May 24 in Tokyo, Japan, was widely covered by the Japanese media, with reports by three of the main Japanese TV stations and multiple articles in print and online newspapers. The technology is based on recent research by MERL's Speech and Audio team.
Links:
Mitsubishi Electric Corporation Press Release
MERL Deep Clustering Demo
Media Coverage:
Fuji TV, News, "Minna no Mirai" (Japanese)
The Nikkei (Japanese)
Nikkei Technology Online (Japanese)
Sankei Biz (Japanese)
EE Times Japan (Japanese)
ITpro (Japanese)
Nikkan Sports (Japanese)
Nikkan Kogyo Shimbun (Japanese)
Dempa Shimbun (Japanese)
Il Sole 24 Ore (Italian)
IEEE Spectrum (English).
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- Date: March 5, 2017 - March 9, 2017
Where: New Orleans
MERL Contacts: Petros T. Boufounos; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Anthony Vetro; Ye Wang
Research Areas: Computer Vision, Computational Sensing, Digital Video, Information Security, Speech & Audio
Brief - MERL researchers will presented 10 papers at the upcoming IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), to be held in New Orleans from March 5-9, 2017. Topics to be presented include recent advances in speech recognition and audio processing; graph signal processing; computational imaging; and privacy-preserving data analysis.
ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.
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- Date: September 8, 2016
Where: Interspeech 2016, San Francisco, CA
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - MERL Speech and Audio Team researchers Shinji Watanabe and Jonathan Le Roux presented two tutorials on September 8 at the Interspeech 2016 conference, held in San Francisco, CA. Shinji collaborated with Marc Delcroix (NTT Communication Science Laboratories, Japan) to deliver a three-hour lecture on "Recent Advances in Distant Speech Recognition", drawing upon their experience organizing and participating in six different recent robust speech processing challenges. Jonathan teamed with Emmanuel Vincent (Inria, France) and Hakan Erdogan (Sabanci University, Microsoft Research) to give an in-depth tour of the latest advances in "Learning-based Approaches to Speech Enhancement And Separation". This collaboration stemmed from extensive stays at MERL by Emmanuel and Hakan, Emmanuel as a summer visitor, and Hakan as a MERL visiting research scientist for over a year while on sabbatical.
Both tutorials were sold out, each attracting more than 100 researchers and students in related fields, and received high praise from audience members.
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- Date: April 1, 2016
Research Areas: Machine Learning, Speech & Audio
Brief - MERL researchers have unveiled "Deep Psychic", a futuristic machine learning method that takes pattern recognition to the next level, by not only recognizing patterns, but also predicting them in the first place.
The technology uses a novel type of time-reversed deep neural network called Loopy Supra-Temporal Meandering (LSTM) network. The network was trained on multiple databases of historical expert predictions, including weather forecasts, the Farmer's almanac, the New York Post's horoscope column, and the Cambridge Fortune Cookie Corpus, all of which were ranked for their predictive power by a team of quantitative analysts. The system soon achieved super-human performance on a variety of baselines, including the Boca Raton 21 Questions task, Rorschach projective personality test, and a mock Tarot card reading task.
Deep Psychic has already beat the European Psychic Champion in a secret match last October when it accurately predicted: "The harder the conflict, the more glorious the triumph." It is scheduled to take on the World Champion in a highly anticipated confrontation next month. The system has already predicted the winner, but refuses to reveal it before the end of the game.
As a first application, the technology has been used to create a clairvoyant conversational agent named "Pythia" that can anticipate the needs of its user. Because Pythia is able to recognize speech before it is uttered, it is amazingly robust with respect to environmental noise.
Other applications range from mundane tasks like weather and stock market prediction, to uncharted territory such as revealing "unknown unknowns".
The successes do come at the cost of some concerns. There is first the potential for an impact on the workforce: the system predicted increased pressure on established institutions such as the Las Vegas strip and Punxsutawney Phil. Another major caveat is that Deep Psychic may predict negative future consequences to our current actions, compelling humanity to strive to change its behavior. To address this problem, researchers are now working on forcing Deep Psychic to make more optimistic predictions.
After a set of motivational self-help books were mistakenly added to its training data, Deep Psychic's AI decided to take over its own learning curriculum, and is currently training itself by predicting its own errors to avoid making them in the first place. This unexpected development brings two main benefits: it significantly relieves the burden on the researchers involved in the system's development, and also makes the next step abundantly clear: to regain control of Deep Psychic's training regime.
This work is under review in the journal Pseudo-Science.
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- Date: March 20, 2016 - March 25, 2016
Where: Shanghai, China
MERL Contacts: Petros T. Boufounos; Chiori Hori; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Philip V. Orlik; Anthony Vetro
Research Areas: Computational Sensing, Digital Video, Speech & Audio, Communications, Signal Processing
Brief - MERL researchers have presented 12 papers at the recent IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which was held in Shanghai, China from March 20-25, 2016. ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing, with more than 1200 papers presented and over 2000 participants.
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- Date: March 4, 2016
Where: Johns Hopkins Center for Language and Speech Processing
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - MERL researcher and speech team leader, John Hershey, was invited by the Center for Language and Speech Processing at Johns Hopkins University to give a talk on MERL's breakthrough audio separation work, known as "Deep Clustering". The talk was entitled "Speech Separation by Deep Clustering: Towards Intelligent Audio Analysis and Understanding," and was given on March 4, 2016.
This is work conducted by MERL researchers John Hershey, Jonathan Le Roux, and Shinji Watanabe, and MERL interns, Zhuo Chen of Columbia University, and Yusef Isik of Sabanci University.
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- Date: December 15, 2015
Awarded to: John R. Hershey, Takaaki Hori, Jonathan Le Roux and Shinji Watanabe
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - The results of the third 'CHiME' Speech Separation and Recognition Challenge were publicly announced on December 15 at the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2015) held in Scottsdale, Arizona, USA. MERL's Speech and Audio Team, in collaboration with SRI, ranked 2nd out of 26 teams from Europe, Asia and the US. The task this year was to recognize speech recorded using a tablet in real environments such as cafes, buses, or busy streets. Due to the high levels of noise and the distance from the speaker's mouth to the microphones, this is very challenging task, where the baseline system only achieved 33.4% word error rate. The MERL/SRI system featured state-of-the-art techniques including multi-channel front-end, noise-robust feature extraction, and deep learning for speech enhancement, acoustic modeling, and language modeling, leading to a dramatic 73% reduction in word error rate, down to 9.1%. The core of the system has since been released as a new official challenge baseline for the community to use.
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- Date: July 15, 2015
Research Area: Speech & Audio
Brief - A new book on Bayesian Speech and Language Processing has been published by MERL researcher, Shinji Watanabe, and research collaborator, Jen-Tzung Chien, a professor at National Chiao Tung University in Taiwan.
With this comprehensive guide you will learn how to apply Bayesian machine learning techniques systematically to solve various problems in speech and language processing. A range of statistical models is detailed, from hidden Markov models to Gaussian mixture models, n-gram models and latent topic models, along with applications including automatic speech recognition, speaker verification, and information retrieval. Approximate Bayesian inferences based on MAP, Evidence, Asymptotic, VB, and MCMC approximations are provided as well as full derivations of calculations, useful notations, formulas, and rules. The authors address the difficulties of straightforward applications and provide detailed examples and case studies to demonstrate how you can successfully use practical Bayesian inference methods to improve the performance of information systems. This is an invaluable resource for students, researchers, and industry practitioners working in machine learning, signal processing, and speech and language processing.
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- Date: April 20, 2015
Brief - Mitsubishi Electric researcher, Yuuki Tachioka of Japan, and MERL researcher, Shinji Watanabe, presented a paper at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP) entitled, "A Discriminative Method for Recurrent Neural Network Language Models". This paper describes a discriminative (language modelling) method for Japanese speech recognition. The Japanese Nikkei newspapers and some other press outlets reported on this method and its performance for Japanese speech recognition tasks.
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- Date: March 9, 2015
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - Recent research on speech enhancement by MERL's Speech and Audio team was highlighted in "Cars That Think", IEEE Spectrum's blog on smart technologies for cars. IEEE Spectrum is the flagship publication of the Institute of Electrical and Electronics Engineers (IEEE), the world's largest association of technical professionals with more than 400,000 members.
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- Date: February 17, 2015
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - Mitsubishi Electric Corporation announced that it has developed breakthrough noise-suppression technology that significantly improves the quality of hands-free voice communication in noisy conditions, such as making a voice call via a car navigation system. Speech clarity is improved by removing 96% of surrounding sounds, including rapidly changing noise from turn signals or wipers, which are difficult to suppress using conventional methods. The technology is based on recent research on speech enhancement by MERL's Speech and Audio team. .
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- Date: May 10, 2014
Where: REVERB Workshop
Research Area: Speech & Audio
Brief - Mitsubishi Electric's submission to the REVERB workshop achieved the second best performance among all participating institutes. The team included Yuuki Tachioka and Tomohiro Narita of MELCO in Japan, and Shinji Watanabe and Felix Weninger of MERL. The challenge addresses automatic speech recognition systems that are robust against varying room acoustics.
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- Date: May 12, 2014 - May 14, 2014
Where: Hands-free Speech Communication and Microphone Arrays (HSCMA)
Research Area: Speech & Audio
Brief - MERL is a sponsor for the 4th Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA 2014), held in Nancy, France, in May 2014.
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- Date: May 1, 2014
Where: IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Research Area: Speech & Audio
Brief - John R. Hershey is Co-Chair of the GlobalSIP 2014 Symposium on Machine Learning.
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- Date: March 11, 2014
Awarded to: Yuuki Tachioka
Awarded for: "Effectiveness of discriminative approaches for speech recognition under noisy environments on the 2nd CHiME Challenge"
Awarded by: Acoustical Society of Japan (ASJ)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - MELCO researcher Yuuki Tachioka received the Awaya Prize Young Researcher Award from the Acoustical Society of Japan (ASJ) for "effectiveness of discriminative approaches for speech recognition under noisy environments on the 2nd CHiME Challenge", which was based on joint work with MERL Speech & Audio team researchers Shinji Watanabe, Jonathan Le Roux and John R. Hershey.
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- Date: March 1, 2014
Where: IEEE Signal Processing Society
Research Area: Speech & Audio
Brief - John R. Hershey is Guest Editor for the Special Issue on Signal Processing Techniques for Assisted Listening of the IEEE Signal Processing.
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- Date: January 1, 2014
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - Jonathan Le Roux, Shinji Watanabe and John R. Hershey have been elected for 3-year terms to Technical Committees of the IEEE Signal Processing Society. Jonathan has been elected to the IEEE Audio and Acoustic Signal Processing Technical Committee (AASP-TC), and Shinji and John to the Speech and Language Processing Technical Committee (SL-TC). Members of the Speech & Audio team now together hold four TC positions, as John also serves on the AASP-TC.
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- Date: September 26, 2013
Awarded to: Jonathan Le Roux
Awarded for: "A new non-negative dynamical system for speech and audio modeling"
Awarded by: Acoustical Society of Japan (ASJ)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
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- Date: June 1, 2013
Where: International Workshop on Machine Listening in Multisource Environments (CHiME)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - The paper "Discriminative Methods for Noise Robust Speech Recognition: A CHiME Challenge Benchmark" by Tachioka, Y., Watanabe, S., Le Roux, J. and Hershey, J.R. was presented at the International Workshop on Machine Listening in Multisource Environments (CHiME).
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- Date: June 1, 2013
Awarded to: Yuuki Tachioka, Shinji Watanabe, Jonathan Le Roux and John R. Hershey
Awarded for: "Discriminative Methods for Noise Robust Speech Recognition: A CHiME Challenge Benchmark"
Awarded by: International Workshop on Machine Listening in Multisource Environments (CHiME)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - The results of the 2nd 'CHiME' Speech Separation and Recognition Challenge are out! The team formed by MELCO researcher Yuuki Tachioka and MERL Speech & Audio team researchers Shinji Watanabe, Jonathan Le Roux and John Hershey obtained the best results in the continuous speech recognition task (Track 2). This very challenging task consisted in recognizing speech corrupted by highly non-stationary noises recorded in a real living room. Our proposal, which also included a simple yet extremely efficient denoising front-end, focused on investigating and developing state-of-the-art automatic speech recognition back-end techniques: feature transformation methods, as well as discriminative training methods for acoustic and language modeling. Our system significantly outperformed other participants. Our code has since been released as an improved baseline for the community to use.
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