- Date & Time: Friday, June 3, 2016; 1:30PM - 3:00PM
Speaker: Nobuaki Minematsu and Daisuke Saito, The University of Tokyo
Research Area: Speech & Audio
Abstract 
Speech signals covey various kinds of information, which are grouped into two kinds, linguistic and extra-linguistic information. Many speech applications, however, focus on only a single aspect of speech. For example, speech recognizers try to extract only word identity from speech and speaker recognizers extract only speaker identity. Here, irrelevant features are often treated as hidden or latent by applying the probability theory to a large number of samples or the irrelevant features are normalized to have quasi-standard values. In speech analysis, however, phases are usually removed, not hidden or normalized, and pitch harmonics are also removed, not hidden or normalized. The resulting speech spectrum still contains both linguistic information and extra-linguistic information. Is there any good method to remove extra-linguistic information from the spectrum? In this talk, our answer to that question is introduced, called speech structure. Extra-linguistic variation can be modeled as feature space transformation and our speech structure is based on the transform-invariance of f-divergence. This proposal was inspired by findings in classical studies of structural phonology and recent studies of developmental psychology. Speech structure has been applied to accent clustering, speech recognition, and language identification. These applications are also explained in the talk.
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- Date: Thursday, May 12, 2016 - Friday, May 13, 2016
Location: Deep Learning Summit, Boston, MA
Research Area: Speech & Audio
Brief - MERL Speech and Audio Senior Team Leader John Hershey is among a set of high-profile researchers invited to speak at the Deep Learning Summit 2016 in Boston on May 12-13, 2016. John will present the team's groundbreaking work on general sound separation using a novel deep learning framework called Deep Clustering. For the first time, an artificial intelligence is able to crack the half-century-old "cocktail party problem", that is, to isolate the speech of a single person from a mixture of multiple unknown speakers, as humans do when having a conversation in a loud crowd.
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- Date & Time: Friday, April 29, 2016; 12:00 PM - 1:00 PM
Speaker: Yu Zhang, MIT
Research Area: Speech & Audio
Abstract 
A recurrent neural network (RNN) is a class of neural network models where connections between its neurons form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Recently the RNN-based acoustic models greatly improved automatic speech recognition (ASR) accuracy on many tasks, such as an advanced version of the RNN, which exploits a structure called long-short-term memory (LSTM). However, ASR performance with distant microphones, low resources, noisy, reverberant conditions, and on multi-talker speech are still far from satisfactory as compared to humans. To address these issues, we develop new strucute of RNNs inspired by two principles: (1) the structure follows the intuition of human speech recognition; (2) the structure is easy to optimize. The talk will go beyond basic RNNs, introduce prediction-adaptation-correction RNNs (PAC-RNNs) and highway LSTMs (HLSTMs). It studies both uni-directional and bi-direcitonal RNNs and discriminative training also applied on top the RNNs. For efficient training of such RNNs, the talk will describe two algorithms for learning their parameters in some detail: (1) Latency-Controlled bi-directional model training; and (2) Two pass forward computation for sequence training. Finally, this talk will analyze the advantages and disadvantages of different variants and propose future directions.
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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 & Time: Tuesday, March 15, 2016; 12:45 PM - 1:30 PM
Speaker: Prof. Hirofumi Aoki, Nagoya University
Research Area: Speech & Audio
Abstract 
Driving requires a complex skill that is involved with the vehicle itself (e.g., speed control and instrument operation), other road users (e.g., other vehicles, pedestrians), surrounding environment, and so on. During driving, visual cues are the main source to supply information to the brain. In order to stabilize the visual information when you are moving, the eyes move to the opposite direction based on the input to the vestibular system. This involuntary eye movement is called as the vestibulo-ocular reflex (VOR) and the physiological models have been studied so far. Obinata et al. found that the VOR can be used to estimate mental workload. Since then, our research group has been developing methods to quantitatively estimate mental workload during driving by means of reflex eye movement. In this talk, I will explain the basic mechanism of the reflex eye movement and how to apply for mental workload estimation. I also introduce the latest work to combine the VOR and OKR (optokinetic reflex) models for naturalistic driving environment.
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- Date & Time: Tuesday, March 15, 2016; 12:00 PM - 12:45 PM
Speaker: Prof. Kazuya Takeda, Nagoya University
Research Area: Speech & Audio
Abstract 
Thanks to advanced "internet of things" (IoT) technologies, situation-specific human behavior has become an area of development for practical applications involving signal processing. One important area of development of such practical applications is driving behavior research. Since 1999, I have been collecting driving behavior data in a wide range of signal modalities, including speech/sound, video, physical/physiological sensors, CAN bus, LIDAR and GNSS. The objective of this data collection is to evaluate how well signal models can represent human behavior while driving. In this talk, I would like to summarize our 10 years of study of driving behavior signal processing, which has been based on these signal corpora. In particular, statistical signal models of interactions between traffic contexts and driving behavior, i.e., stochastic driver modeling, will be discussed, in the context of risky lane change detection. I greatly look forward to discussing the scalability of such corpus-based approaches, which could be applied to almost any traffic situation.
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- Date & Time: Tuesday, February 16, 2016; 12:00 PM - 1:00 PM
Speaker: Dr. Najim Dehak, MIT
Research Area: Speech & Audio
Abstract 
Recently, there has been a great increase of interest in the field of emotion recognition based on different human modalities, such as speech, heart rate etc. Emotion recognition systems can be very useful in several areas, such as medical and telecommunications. In the medical field, identifying the emotions can be an important tool for detecting and monitoring patients with mental health disorder. In addition, the identification of the emotional state from voice provides opportunities for the development of automated dialogue system capable of producing reports to the physician based on frequent phone communication between the system and the patients. In this talk, we will describe a health related application of using emotion recognition system based on human voices in order to detect and monitor the emotion state of people.
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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: Thursday, October 22, 2015
Location: Google, New York City, NY
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - SANE 2015, a one-day event gathering researchers and students in speech and audio from the Northeast of the American continent, will be held on Thursday October 22, 2015 at Google, in New York City, NY.
It is a follow-up to SANE 2012, held at Mitsubishi Electric Research Labs (MERL), SANE 2013, held at Columbia University, and SANE 2014, held at MIT, which each gathered 70 to 90 researchers and students.
SANE 2015 will feature invited talks by leading researchers from the Northeast, as well as from the international community: Rohit Prasad (Amazon), Michael Mandel (Brooklyn College, CUNY), Ron Weiss (Google), John Hershey (MERL), Pablo Sprechmann (NYU), Tuomas Virtanen (Tampere University of Technology), and Paris Smaragdis (UIUC). It will also feature a lively poster session during lunch time, open to both students and researchers.
SANE 2015 is organized by Jonathan Le Roux (MERL), Hank Liao (Google), Andrew Senior (Google), and John R. Hershey (MERL).
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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: Thursday, October 23, 2014
Location: Mitsubishi Electric Research Laboratories (MERL)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - SANE 2014, a one-day event gathering researchers and students in speech and audio from the Northeast of the American continent, will be held on Thursday October 23, 2014 at MIT, in Cambridge, MA. It is a follow-up to SANE 2012, held at Mitsubishi Electric Research Labs (MERL), and SANE 2013, held at Columbia University, which each gathered around 70 researchers and students. SANE 2014 will feature invited talks by leading researchers from the Northeast as well as Europe: Najim Dehak (MIT), Hakan Erdogan (MERL/Sabanci University), Gael Richard (Telecom ParisTech), George Saon (IBM Research), Andrew Senior (Google Research), Stavros Tsakalidis (BBN - Raytheon), and David Wingate (Lyric). It will also feature a lively poster session during lunch time, open to both students and researchers. SANE 2014 is organized by Jonathan Le Roux (MERL), Jim Glass (MIT), and John R. Hershey (MERL).
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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 & Time: Thursday, October 24, 2013; 8:45 AM - 5:00 PM
Location: Columbia University
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - SANE 2013, a one-day event gathering researchers and students in speech and audio from the Northeast of the American continent, will be held on Thursday October 24, 2013 at Columbia University, in New York City.
A follow-up to SANE 2012 held in October 2012 at MERL in Cambridge, MA, this year's SANE will be held in conjunction with the WASPAA workshop, held October 20-23 in upstate New York. WASPAA attendees are welcome and encouraged to attend SANE.
SANE 2013 will feature invited speakers from the Northeast, as well as from the international community. It will also feature a lively poster session during lunch time, open to both students and researchers.
SANE 2013 is organized by Prof. Dan Ellis (Columbia University), Jonathan Le Roux (MERL) and John R. Hershey (MERL).
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- Date & Time: Thursday, October 17, 2013; 12:00 PM
Speaker: Prof. Laurent Daudet, Paris Diderot University, France
MERL Host: Jonathan Le Roux
Research Area: Speech & Audio
Abstract 
In acoustics, one may wish to acquire a wavefield over a whole spatial domain, while we can only make point measurements (ie, with microphones). Even with few sources, this remains a difficult problem because of reverberation, which can be hard to characterize. This can be seen as a sampling / interpolation problem, and it raises a number of interesting questions: how many sample points are needed, where to choose the sampling points, etc. In this presentation, we will review some case studies, in 2D (vibrating plates) and 3D (room acoustics), with numerical and experimental data, where we have developed sparse models, possibly with additional 'structures', based on a physical modeling of the acoustic field. These type of models are well suited to reconstruction techniques known as compressed sensing. These principles can also be used for sub-nyquist optical imaging : we will show preliminary experimental results of a new compressive imager, remarkably simple in its principle, using a multiply scattering medium.
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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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