News & Events

99 News items and Awards found.



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  •  AWARD    MERL Intern and Researchers Win ICASSP 2023 Best Student Paper Award
    Date: June 9, 2023
    Awarded to: Darius Petermann, Gordon Wichern, Aswin Subramanian, Jonathan Le Roux
    MERL Contacts: Jonathan Le Roux; Gordon Wichern
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • Former MERL intern Darius Petermann (Ph.D. Candidate at Indiana University) has received a Best Student Paper Award at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023) for the paper "Hyperbolic Audio Source Separation", co-authored with MERL researchers Gordon Wichern and Jonathan Le Roux, and former MERL researcher Aswin Subramanian. The paper presents work performed during Darius's internship at MERL in the summer 2022. The paper introduces a framework for audio source separation using embeddings on a hyperbolic manifold that compactly represent the hierarchical relationship between sound sources and time-frequency features. Additionally, the code associated with the paper is publicly available at https://github.com/merlresearch/hyper-unmix.

      ICASSP is the flagship conference of the IEEE Signal Processing Society (SPS). ICASSP 2023 was held in the Greek island of Rhodes from June 04 to June 10, 2023, and it was the largest ICASSP in history, with more than 4000 participants, over 6128 submitted papers and 2709 accepted papers. Darius’s paper was first recognized as one of the Top 3% of all papers accepted at the conference, before receiving one of only 5 Best Student Paper Awards during the closing ceremony.
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  •  AWARD    Joint CMU-MERL team wins DCASE2023 Challenge on Automated Audio Captioning
    Date: June 1, 2023
    Awarded to: Shih-Lun Wu, Xuankai Chang, Gordon Wichern, Jee-weon Jung, Francois Germain, Jonathan Le Roux, Shinji Watanabe
    MERL Contacts: Francois Germain; Jonathan Le Roux; Gordon Wichern
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • A joint team consisting of members of CMU Professor and MERL Alumn Shinji Watanabe's WavLab and members of MERL's Speech & Audio team ranked 1st out of 11 teams in the DCASE2023 Challenge's Task 6A "Automated Audio Captioning". The team was led by student Shih-Lun Wu and also featured Ph.D. candidate Xuankai Chang, Postdoctoral research associate Jee-weon Jung, Prof. Shinji Watanabe, and MERL researchers Gordon Wichern, Francois Germain, and Jonathan Le Roux.

      The IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE Challenge), started in 2013, has been organized yearly since 2016, and gathers challenges on multiple tasks related to the detection, analysis, and generation of sound events. This year, the DCASE2023 Challenge received over 428 submissions from 123 teams across seven tasks.

      The CMU-MERL team competed in the Task 6A track, Automated Audio Captioning, which aims at generating informative descriptions for various sounds from nature and/or human activities. The team's system made strong use of large pretrained models, namely a BEATs transformer as part of the audio encoder stack, an Instructor Transformer encoding ground-truth captions to derive an audio-text contrastive loss on the audio encoder, and ChatGPT to produce caption mix-ups (i.e., grammatical and compact combinations of two captions) which, together with the corresponding audio mixtures, increase not only the amount but also the complexity and diversity of the training data. The team's best submission obtained a SPIDEr-FL score of 0.327 on the hidden test set, largely outperforming the 2nd best team's 0.315.
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  •  NEWS    Jonathan Le Roux gives invited talk at CMU's Language Technology Institute Colloquium
    Date: December 9, 2022
    Where: Pittsburg, PA
    MERL Contact: Jonathan Le Roux
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • MERL Senior Principal Research Scientist and Speech and Audio Senior Team Leader, Jonathan Le Roux, was invited by Carnegie Mellon University's Language Technology Institute (LTI) to give an invited talk as part of the LTI Colloquium Series. The LTI Colloquium is a prestigious series of talks given by experts from across the country related to different areas of language technologies. Jonathan's talk, entitled "Towards general and flexible audio source separation", presented an overview of techniques developed at MERL towards the goal of robustly and flexibly decomposing and analyzing an acoustic scene, describing in particular the Speech and Audio Team's efforts to extend MERL's early speech separation and enhancement methods to more challenging environments, and to more general and less supervised scenarios.
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  •  NEWS    MERL researchers presenting five papers at NeurIPS 2022
    Date: November 29, 2022 - December 9, 2022
    Where: NeurIPS 2022
    MERL Contacts: Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Suhas Lohit
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    Brief
    • MERL researchers are presenting 5 papers at the NeurIPS Conference, which will be held in New Orleans from Nov 29-Dec 1st, with virtual presentations in the following week. NeurIPS is one of the most prestigious and competitive international conferences in machine learning.

      MERL papers in NeurIPS 2022:

      1. “AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments” by Sudipta Paul, Amit Roy-Chowdhary, and Anoop Cherian

      This work proposes a unified multimodal task for audio-visual embodied navigation where the navigating agent can also interact and seek help from a human/oracle in natural language when it is uncertain of its navigation actions. We propose a multimodal deep hierarchical reinforcement learning framework for solving this challenging task that allows the agent to learn when to seek help and how to use the language instructions. AVLEN agents can interact anywhere in the 3D navigation space and demonstrate state-of-the-art performances when the audio-goal is sporadic or when distractor sounds are present.

      2. “Learning Partial Equivariances From Data” by David W. Romero and Suhas Lohit

      Group equivariance serves as a good prior improving data efficiency and generalization for deep neural networks, especially in settings with data or memory constraints. However, if the symmetry groups are misspecified, equivariance can be overly restrictive and lead to bad performance. This paper shows how to build partial group convolutional neural networks that learn to adapt the equivariance levels at each layer that are suitable for the task at hand directly from data. This improves performance while retaining equivariance properties approximately.

      3. “Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation” by Moitreya Chatterjee, Narendra Ahuja, and Anoop Cherian

      There often exist strong correlations between the 3D motion dynamics of a sounding source and its sound being heard, especially when the source is moving towards or away from the microphone. In this paper, we propose an audio-visual scene-graph that learns and leverages such correlations for improved visually-guided audio separation from an audio mixture, while also allowing predicting the direction of motion of the sound source.

      4. “What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective” by Huan Wang, Suhas Lohit, Michael Jones, and Yun Fu

      This paper presents theoretical and practical results for understanding what makes a particular data augmentation technique (DA) suitable for knowledge distillation (KD). We design a simple metric that works very well in practice to predict the effectiveness of DA for KD. Based on this metric, we also propose a new data augmentation technique that outperforms other methods for knowledge distillation in image recognition networks.

      5. “FeLMi : Few shot Learning with hard Mixup” by Aniket Roy, Anshul Shah, Ketul Shah, Prithviraj Dhar, Anoop Cherian, and Rama Chellappa

      Learning from only a few examples is a fundamental challenge in machine learning. Recent approaches show benefits by learning a feature extractor on the abundant and labeled base examples and transferring these to the fewer novel examples. However, the latter stage is often prone to overfitting due to the small size of few-shot datasets. In this paper, we propose a novel uncertainty-based criteria to synthetically produce “hard” and useful data by mixing up real data samples. Our approach leads to state-of-the-art results on various computer vision few-shot benchmarks.
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  •  NEWS    Members of the Speech & Audio team elected to IEEE Technical Committee
    Date: November 28, 2022
    MERL Contacts: Francois Germain; Gordon Wichern
    Research Area: Speech & Audio
    Brief
    • Gordon Wichern and François Germain have been elected for 3-year terms to the IEEE Audio and Acoustic Signal Processing Technical Committee (AASP TC) of the IEEE Signal Processing Society.

      The AASP TC's mission is to support, nourish, and lead scientific and technological development in all areas of audio and acoustic signal processing. It numbers 30 or so appointed volunteer members drawn roughly equally from leading academic and industrial organizations around the world, unified by the common aim to offer their expertise in the service of the scientific community.
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  •  NEWS    MERL launches Postdoctoral Research Fellow program
    Date: September 21, 2022
    MERL Contacts: Philip V. Orlik; Anthony Vetro
    Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio
    Brief
    • Mitsubishi Electric Research Laboratories (MERL) invites qualified postdoctoral candidates to apply for the position of Postdoctoral Research Fellow. This position provides early career scientists the opportunity to work at a unique, academically-oriented industrial research laboratory. Successful candidates will be expected to define and pursue their own original research agenda, explore connections to established laboratory initiatives, and publish high impact articles in leading venues. Please refer to our web page for further details.
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  •  NEWS    MERL congratulates Prof. Alex Waibel on receiving 2023 IEEE James L. Flanagan Speech and Audio Processing Award
    Date: August 22, 2022
    MERL Contacts: Chiori Hori; Jonathan Le Roux; Anthony Vetro
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • IEEE has announced that the recipient of the 2023 IEEE James L. Flanagan Speech and Audio Processing Award will be Prof. Alex Waibel (CMU/Karlsruhe Institute of Technology), “For pioneering contributions to spoken language translation and supporting technologies.” Mitsubishi Electric Research Laboratories (MERL), which has become the new sponsor of this prestigious award in 2022, extends our warmest congratulations to Prof. Waibel.

      MERL Senior Principal Research Scientist Dr. Chiori Hori, who worked with Dr. Waibel at Carnegie Mellon University and collaborated with him as part of national projects on speech summarization and translation, comments on his invaluable contributions to the field: “He has contributed not only to the invention of groundbreaking technology in speech and spoken language processing but also to the promotion of an abundance of research projects through international research consortiums by linking American, European, and Asian research communities. Many of his former laboratory members and collaborators are now leading R&D in the AI field.”

      The IEEE Board of Directors established the IEEE James L. Flanagan Speech and Audio Processing Award in 2002 for outstanding contributions to the advancement of speech and/or audio signal processing. This award has recognized the contributions of some of the most renowned pioneers and leaders in their respective fields. MERL is proud to support the recognition of outstanding contributions to the field of speech and audio processing through its sponsorship of this award.
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  •  NEWS    MERL presenting 8 papers at ICASSP 2022
    Date: May 22, 2022 - May 27, 2022
    Where: Singapore
    MERL Contacts: Anoop Cherian; Chiori Hori; Toshiaki Koike-Akino; Jonathan Le Roux; Tim K. Marks; Philip V. Orlik; Kuan-Chuan Peng; Pu (Perry) Wang; Gordon Wichern
    Research Areas: Artificial Intelligence, Computer Vision, Signal Processing, Speech & Audio
    Brief
    • MERL researchers are presenting 8 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held in Singapore from May 22-27, 2022. A week of virtual presentations also took place earlier this month.

      Topics to be presented include recent advances in speech recognition, audio processing, scene understanding, computational sensing, and classification.

      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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  •  NEWS    MERL work on scene-aware interaction featured in IEEE Spectrum
    Date: March 1, 2022
    MERL Contacts: Anoop Cherian; Chiori Hori; Jonathan Le Roux; Tim K. Marks; Anthony Vetro
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    Brief
    • MERL's research on scene-aware interaction was recently featured in an IEEE Spectrum article. The article, titled "At Last, A Self-Driving Car That Can Explain Itself" and authored by MERL Senior Principal Research Scientist Chiori Hori and MERL Director Anthony Vetro, gives an overview of MERL's efforts towards developing a system that can analyze multimodal sensing information for highly natural and intuitive interaction with humans through context-dependent generation of natural language. The technology recognizes contextual objects and events based on multimodal sensing information, such as images and video captured with cameras, audio information recorded with microphones, and localization information measured with LiDAR.

      Scene-Aware Interaction for car navigation, one target application that the article focuses on, will provide drivers with intuitive route guidance. Scene-Aware Interaction technology is expected to have wide applicability, including human-machine interfaces for in-vehicle infotainment, interaction with service robots in building and factory automation systems, systems that monitor the health and well-being of people, surveillance systems that interpret complex scenes for humans and encourage social distancing, support for touchless operation of equipment in public areas, and much more. MERL's Scene-Aware Interaction Technology had previously been featured in a Mitsubishi Electric Corporation Press Release.

      IEEE Spectrum is the flagship magazine and website of the IEEE, the world’s largest professional organization devoted to engineering and the applied sciences. IEEE Spectrum has a circulation of over 400,000 engineers worldwide, making it one of the leading science and engineering magazines.
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  •  NEWS    Jonathan Le Roux discusses MERL's audio source separation work on popular machine learning podcast
    Date: January 24, 2022
    Where: The TWIML AI Podcast
    MERL Contact: Jonathan Le Roux
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • MERL Speech & Audio Senior Team Leader Jonathan Le Roux was featured in an extended interview on the popular TWIML AI Podcast, presenting MERL's work towards solving the "cocktail party problem". Humans have the extraordinary ability to focus on particular sounds of interest within a complex acoustic scene, such as a cocktail party. MERL's Speech & Audio Team has been at the forefront of the field's effort to develop algorithms giving machines similar abilities. Jonathan talked with host Sam Charrington about the group's decade-long journey on this topic, from early pioneering work using deep learning for speech enhancement and speech separation, to recent works on weakly-supervised separation, hierarchical sound separation, as well as the separation of real-world soundtracks into speech, music, and sound effects (aka the "cocktail fork problem").

      The TWIML AI podcast, formerly known as This Week in Machine Learning & AI, was created in 2016 and is followed by more than 10,000 subscribers on Youtube and Twitter. Jonathan's interview marks the 555th episode of the podcast.
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  •  NEWS    MERL Congratulates Recipients of 2022 IEEE Technical Field Awards in Signal Processing
    Date: July 26, 2021
    MERL Contacts: Petros T. Boufounos; Jonathan Le Roux; Philip V. Orlik; Anthony Vetro
    Research Areas: Signal Processing, Speech & Audio
    Brief
    • IEEE has announced that the recipients of the 2022 IEEE James L. Flanagan Speech and Audio Processing Award will be Hervé Bourlard (EPFL/Idiap Research Institute) and Nelson Morgan (ICSI), "For contributions to neural networks for statistical speech recognition," and the recipient of the 2022 IEEE Fourier Award for Signal Processing will be Ali Sayed (EPFL), "For contributions to the theory and practice of adaptive signal processing." More details about the contributions of Prof. Bourlard and Prof. Morgan can be found in the announcements by ICSI and EPFL, and those of Prof. Sayed in EPFL's announcement. Mitsubishi Electric Research Laboratories (MERL) has recently become the new sponsor of these two prestigious awards, and extends our warmest congratulations to all of the 2022 award recipients.

      The IEEE Board of Directors established the IEEE James L. Flanagan Speech and Audio Processing Award in 2002 for outstanding contributions to the advancement of speech and/or audio signal processing, while the IEEE Fourier Award for Signal Processing was established in 2012 for outstanding contribution to the advancement of signal processing, other than in the areas of speech and audio processing. Both awards have recognized the contributions of some of the most renowned pioneers and leaders in their respective fields. MERL is proud to support the recognition of outstanding contributions to the signal processing field through its sponsorship of these awards.
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  •  NEWS    MERL becomes new sponsor of two prestigious IEEE Technical Field Awards in Signal Processing
    Date: July 9, 2021
    MERL Contacts: Petros T. Boufounos; Jonathan Le Roux; Philip V. Orlik; Anthony Vetro
    Research Areas: Signal Processing, Speech & Audio
    Brief
    • Mitsubishi Electric Research Laboratories (MERL) has become the new sponsor of two prestigious IEEE Technical Field Awards in Signal Processing, the IEEE James L. Flanagan Speech and Audio Processing Award and the IEEE Fourier Award for Signal Processing, for the years 2022-2031. "MERL is proud to support the recognition of outstanding contributions to signal processing by sponsoring both the IEEE James L. Flanagan Speech and Audio Processing Award and the IEEE Fourier Award for Signal Processing. These awards celebrate the creativity and innovation in the field that touch many aspects of our lives and drive our society forward" said Dr. Anthony Vetro, VP and Director at MERL.

      The IEEE Board of Directors established the IEEE James L. Flanagan Speech and Audio Processing Award in 2002 for outstanding contributions to the advancement of speech and/or audio signal processing, while the IEEE Fourier Award for Signal Processing was established in 2012 for outstanding contribution to the advancement of signal processing, other than in the areas of speech and audio processing. Both awards have since recognized the contributions of some of the most renowned pioneers and leaders in their respective fields.

      By underwriting these IEEE Technical Field Awards, MERL continues to make a mark by supporting the advancement of technology that makes lasting changes in the world.
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  •  NEWS    Chiori Hori will give keynote on scene understanding via multimodal sensing at AI Electronics Symposium
    Date: February 15, 2021
    Where: The 2nd International Symposium on AI Electronics
    MERL Contact: Chiori Hori
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    Brief
    • Chiori Hori, a Senior Principal Researcher in MERL's Speech and Audio Team, will be a keynote speaker at the 2nd International Symposium on AI Electronics, alongside Alex Acero, Senior Director of Apple Siri, Roberto Cipolla, Professor of Information Engineering at the University of Cambridge, and Hiroshi Amano, Professor at Nagoya University and winner of the Nobel prize in Physics for his work on blue light-emitting diodes. The symposium, organized by Tohoku University, will be held online on February 15, 2021, 10am-4pm (JST).

      Chiori's talk, titled "Human Perspective Scene Understanding via Multimodal Sensing", will present MERL's work towards the development of scene-aware interaction. One important piece of technology that is still missing for human-machine interaction is natural and context-aware interaction, where machines understand their surrounding scene from the human perspective, and they can share their understanding with humans using natural language. To bridge this communications gap, MERL has been working at the intersection of research fields such as spoken dialog, audio-visual understanding, sensor signal understanding, and robotics technologies in order to build a new AI paradigm, called scene-aware interaction, that enables machines to translate their perception and understanding of a scene and respond to it using natural language to interact more effectively with humans. In this talk, the technologies will be surveyed, and an application for future car navigation will be introduced.
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  •  AWARD    Best Poster Award and Best Video Award at the International Society for Music Information Retrieval Conference (ISMIR) 2020
    Date: October 15, 2020
    Awarded to: Ethan Manilow, Gordon Wichern, Jonathan Le Roux
    MERL Contacts: Jonathan Le Roux; Gordon Wichern
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • Former MERL intern Ethan Manilow and MERL researchers Gordon Wichern and Jonathan Le Roux won Best Poster Award and Best Video Award at the 2020 International Society for Music Information Retrieval Conference (ISMIR 2020) for the paper "Hierarchical Musical Source Separation". The conference was held October 11-14 in a virtual format. The Best Poster Awards and Best Video Awards were awarded by popular vote among the conference attendees.

      The paper proposes a new method for isolating individual sounds in an audio mixture that accounts for the hierarchical relationship between sound sources. Many sounds we are interested in analyzing are hierarchical in nature, e.g., during a music performance, a hi-hat note is one of many such hi-hat notes, which is one of several parts of a drumkit, itself one of many instruments in a band, which might be playing in a bar with other sounds occurring. Inspired by this, the paper re-frames the audio source separation problem as hierarchical, combining similar sounds together at certain levels while separating them at other levels, and shows on a musical instrument separation task that a hierarchical approach outperforms non-hierarchical models while also requiring less training data. The paper, poster, and video can be seen on the paper page on the ISMIR website.
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  •  NEWS    Anoop Cherian gave an invited talk at the Multi-modal Video Analysis Workshop, ECCV 2020
    Date: August 23, 2020
    Where: European Conference on Computer Vision (ECCV), online, 2020
    MERL Contact: Anoop Cherian
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    Brief
    • MERL Principal Research Scientist Anoop Cherian gave an invited talk titled "Sound2Sight: Audio-Conditioned Visual Imagination" at the Multi-modal Video Analysis workshop held in conjunction with the European Conference on Computer Vision (ECCV), 2020. The talk was based on a recent ECCV paper that describes a new multimodal reasoning task called Sound2Sight and a generative adversarial machine learning algorithm for producing plausible video sequences conditioned on sound and visual context.
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  •  NEWS    MERL's Scene-Aware Interaction Technology Featured in Mitsubishi Electric Corporation Press Release
    Date: July 22, 2020
    Where: Tokyo, Japan
    MERL Contacts: Anoop Cherian; Chiori Hori; Jonathan Le Roux; Tim K. Marks; Anthony Vetro
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    Brief
    • Mitsubishi Electric Corporation announced that the company has developed what it believes to be the world’s first technology capable of highly natural and intuitive interaction with humans based on a scene-aware capability to translate multimodal sensing information into natural language.

      The novel technology, Scene-Aware Interaction, incorporates Mitsubishi Electric’s proprietary Maisart® compact AI technology to analyze multimodal sensing information for highly natural and intuitive interaction with humans through context-dependent generation of natural language. The technology recognizes contextual objects and events based on multimodal sensing information, such as images and video captured with cameras, audio information recorded with microphones, and localization information measured with LiDAR.

      Scene-Aware Interaction for car navigation, one target application, will provide drivers with intuitive route guidance. The technology is also expected to have applicability to human-machine interfaces for in-vehicle infotainment, interaction with service robots in building and factory automation systems, systems that monitor the health and well-being of people, surveillance systems that interpret complex scenes for humans and encourage social distancing, support for touchless operation of equipment in public areas, and much more. The technology is based on recent research by MERL's Speech & Audio and Computer Vision groups.
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  •  NEWS    Jonathan Le Roux gives Plenary Lecture at the JSALT 2020 Summer Workshop
    Date: July 10, 2020
    Where: Virtual Baltimore, MD
    MERL Contact: Jonathan Le Roux
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • MERL Senior Principal Research Scientist and Speech and Audio Senior Team Leader Jonathan Le Roux was invited by the Center for Language and Speech Processing at Johns Hopkins University to give a plenary lecture at the 2020 Frederick Jelinek Memorial Summer Workshop on Speech and Language Technology (JSALT). The talk, entitled "Deep Learning for Multifarious Speech Processing: Tackling Multiple Speakers, Microphones, and Languages", presented an overview of deep learning techniques developed at MERL towards the goal of cracking the Tower of Babel version of the cocktail party problem, that is, separating and/or recognizing the speech of multiple unknown speakers speaking simultaneously in multiple languages, in both single-channel and multi-channel scenarios: from deep clustering to chimera networks, phasebook and friends, and from seamless ASR to MIMO-Speech and Transformer-based multi-speaker ASR.

      JSALT 2020 is the seventh in a series of six-week-long research workshops on Machine Learning for Speech Language and Computer Vision Technology. A continuation of the well known Johns Hopkins University summer workshops, these workshops bring together diverse "dream teams" of leading professionals, graduate students, and undergraduates, in a truly cooperative, intensive, and substantive effort to advance the state of the science. MERL researchers led such teams in the JSALT 2015 workshop, on "Far-Field Speech Enhancement and Recognition in Mismatched Settings", and the JSALT 2018 workshop, on "Multi-lingual End-to-End Speech Recognition for Incomplete Data".
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  •  NEWS    Zhong-Qiu Wang joins MERL's Speech and Audio Team
    Date: June 22, 2020
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • We are excited to announce that Dr. Zhong-Qiu Wang, who recently obtained his Ph.D. from The Ohio State University, has joined MERL's Speech and Audio Team as a Visiting Research Scientist. Zhong-Qiu brings strong expertise in microphone array processing, speech enhancement, blind source/speaker separation, and robust automatic speech recognition, for which he has developed some of the most advanced machine learning and deep learning methods.

      Prior to joining MERL, Zhong-Qiu received the B.Eng. degree in 2013 from Harbin Institute of Technology, Harbin, China, and the M.Sc. and Ph.D. degree in 2017 and 2020 from The Ohio State University, Columbus, USA, all in Computer Science. He was a summer research intern at Microsoft Research, Mitsubishi Electric Research Laboratories, and Google AI. He received a Best Student Paper Award at ICASSP 2018 for his work as an intern at MERL, and a Graduate Research Award from OSU Department of Computer Science and Engineering in 2020.
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  •  NEWS    MERL presenting 13 papers and an industry talk at ICASSP 2020
    Date: May 4, 2020 - May 8, 2020
    Where: Virtual Barcelona
    MERL Contacts: Karl Berntorp; Petros T. Boufounos; Chiori Hori; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Yanting Ma; Hassan Mansour; Philip V. Orlik; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
    Research Areas: Computational Sensing, Computer Vision, Machine Learning, Signal Processing, Speech & Audio
    Brief
    • MERL researchers are presenting 13 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held virtually from May 4-8, 2020. Petros Boufounos is also presenting a talk on the Computational Sensing Revolution in Array Processing (video) in ICASSP’s Industry Track, and Siheng Chen is co-organizing and chairing a special session on a Signal-Processing View of Graph Neural Networks.

      Topics to be presented include recent advances in speech recognition, audio processing, scene understanding, computational sensing, array processing, and parameter estimation. Videos for all talks are available on MERL's YouTube channel, with corresponding links in the references below.

      This year again, MERL is a sponsor of the conference and will be participating in the Student Job Fair; please join us to learn about our internship program and career opportunities.

      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. Originally planned to be held in Barcelona, Spain, ICASSP has moved to a fully virtual setting due to the COVID-19 crisis, with free registration for participants not covering a paper.
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  •  AWARD    Best Paper Award at the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) 2019
    Date: December 18, 2019
    Awarded to: Xuankai Chang, Wangyou Zhang, Yanmin Qian, Jonathan Le Roux, Shinji Watanabe
    MERL Contact: Jonathan Le Roux
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • MERL researcher Jonathan Le Roux and co-authors Xuankai Chang, Shinji Watanabe (Johns Hopkins University), Wangyou Zhang, and Yanmin Qian (Shanghai Jiao Tong University) won the Best Paper Award at the 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019), for the paper "MIMO-Speech: End-to-End Multi-Channel Multi-Speaker Speech Recognition". MIMO-Speech is a fully neural end-to-end framework that can transcribe the text of multiple speakers speaking simultaneously from multi-channel input. The system is comprised of a monaural masking network, a multi-source neural beamformer, and a multi-output speech recognition model, which are jointly optimized only via an automatic speech recognition (ASR) criterion. The award was received by lead author Xuankai Chang during the conference, which was held in Sentosa, Singapore from December 14-18, 2019.
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  •  NEWS    Takaaki Hori elected to IEEE Technical Committee on Speech and Language Processing
    Date: November 9, 2019
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • Takaaki 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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  •  NEWS    MERL Speech & Audio Researchers Presenting 7 Papers and a Tutorial at Interspeech 2019
    Date: September 15, 2019 - September 19, 2019
    Where: Graz, Austria
    MERL Contacts: Chiori Hori; Jonathan Le Roux; Gordon Wichern
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • MERL Speech & Audio Team researchers will be presenting 7 papers at the 20th Annual Conference of the International Speech Communication Association INTERSPEECH 2019, which is being held in Graz, Austria from September 15-19, 2019. Topics to be presented include recent advances in end-to-end speech recognition, speech separation, and audio-visual scene-aware dialog. Takaaki Hori is also co-presenting a tutorial on end-to-end speech processing.

      Interspeech is the world's largest and most comprehensive conference on the science and technology of spoken language processing. It gathers around 2000 participants from all over the world.
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  •  NEWS    MERL presenting 16 papers at ICASSP 2019
    Date: May 12, 2019 - May 17, 2019
    Where: Brighton, UK
    MERL Contacts: Petros T. Boufounos; Anoop Cherian; Chiori Hori; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Tim K. Marks; Philip V. Orlik; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
    Research Areas: Computational Sensing, Computer Vision, Machine Learning, Signal Processing, Speech & Audio
    Brief
    • MERL researchers will be presenting 16 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held in Brighton, UK from May 12-17, 2019. Topics to be presented include recent advances in speech recognition, audio processing, scene understanding, computational sensing, and parameter estimation. MERL is also a sponsor of the conference and will be participating in the student career luncheon; please join us at the lunch to learn about our internship program and career opportunities.

      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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  •  NEWS    MERL's seamless speech recognition technology featured in Mitsubishi Electric Corporation press release
    Date: February 13, 2019
    Where: Tokyo, Japan
    MERL Contacts: Jonathan Le Roux; Gordon Wichern
    Research Area: Speech & Audio
    Brief
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  •  NEWS    Takaaki Hori leads speech technology workshop
    Date: June 25, 2018 - August 3, 2018
    Where: Johns Hopkins University, Baltimore, MD
    MERL Contact: Jonathan Le Roux
    Research Area: Speech & Audio
    Brief
    • MERL Speech & Audio Team researcher Takaaki Hori led a team of 27 senior researchers and Ph.D. students from different organizations around the world, working on "Multi-lingual End-to-End Speech Recognition for Incomplete Data" as part of the Jelinek Memorial Summer Workshop on Speech and Language Technology (JSALT). The JSALT workshop is a renowned 6-week hands-on workshop held yearly since 1995. This year, the workshop was held at Johns Hopkins University in Baltimore from June 25 to August 3, 2018. Takaaki's team developed new methods for end-to-end Automatic Speech Recognition (ASR) with a focus on low-resource languages with limited labelled data.

      End-to-end ASR can significantly reduce the burden of developing ASR systems for new languages, by eliminating the need for linguistic information such as pronunciation dictionaries. Some end-to-end systems have recently achieved performance comparable to or better than conventional systems in several tasks. However, the current model training algorithms basically require paired data, i.e., speech data and the corresponding transcription. Sufficient amount of such complete data is usually unavailable for minor languages, and creating such data sets is very expensive and time consuming.

      The goal of Takaaki's team project was to expand the applicability of end-to-end models to multilingual ASR, and to develop new technology that would make it possible to build highly accurate systems even for low-resource languages without a large amount of paired data. Some major accomplishments of the team include building multi-lingual end-to-end ASR systems for 17 languages, developing novel architectures and training methods for end-to-end ASR, building end-to-end ASR-TTS (Text-to-speech) chain for unpaired data training, and developing ESPnet, an open-source end-to-end speech processing toolkit. Three papers stemming from the team's work have already been accepted to the 2018 IEEE Spoken Language Technology Workshop (SLT), with several more to be submitted to upcoming conferences.
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