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A quick guide to Amazon’s 50-plus ICASSP papers 2022

Topics range from the predictable, such as speech recognition and signal processing, to time series forecasting and personalization.

By Staff writer
May 10, 2022
7 min read
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Amazon researchers have more than 50 papers at this year’s International Conference on Acoustics, Speech, and Signal Processing (ICASSP). A plurality of them are on automatic speech recognition and related topics, such as keyword spotting and speaker identification. But others range farther afield, to topics such as computer vision and federated learning.

ICASSP-2022-Header.png
This year's ICASSP includes a virtual component, from May 7 to 13, and an in-person component in Singapore, May 22 to 27.

Acoustic-event detection

Federated self-supervised learning for acoustic event classification
Meng Feng, Chieh-Chi Kao, Qingming Tang, Ming Sun, Viktor Rozgic, Spyros Matsoukas, Chao Wang

Improved representation learning for acoustic event classification using tree-structured ontology
Arman Zharmagambetov, Qingming Tang, Chieh-Chi Kao, Qin Zhang, Ming Sun, Viktor Rozgic, Jasha Droppo, Chao Wang

WikiTAG: Wikipedia-based knowledge embeddings towards improved acoustic event classification
Qin Zhang, Qingming Tang, Chieh-Chi Kao, Ming Sun, Yang Liu, Chao Wang

Automatic speech recognition

A likelihood ratio-based domain adaptation method for end-to-end models
Chhavi Choudhury, Ankur Gandhe, Xiaohan Ding, Ivan Bulyko

Being greedy does not hurt: Sampling strategies for end-to-end speech recognition
Jahn Heymann, Egor Lakomkin, Leif RādellJahn Heymann, Egor Lakomkin, Leif RādelJahn Heymann, Egor Lakomkin, Leif RādelJahn Heymann, Egor Lakomkin, Leif Rādel

Caching networks: Capitalizing on common speech for ASR
Anastasios Alexandridis, Grant P. Strimel, Ariya Rastrow, Pavel Kveton, Jon Webb, Maurizio Omologo, Siegfried Kunzmann, Athanasios Mouchtaris

Lattice attention.png
In "LATTENTION: Lattice attention in ASR rescoring", Amazon researchers show that applying an attention mechanism (colored grid) to a lattice encoding multiple automatic-speech-recognition (ASR) hypotheses improves ASR performance.

Contextual adapters for personalized speech recognition in neural transducers
Kanthashree Mysore Sathyendra, Thejaswi Muniyappa, Feng-Ju Chang, Jing Liu, Jinru Su, Grant P. Strimel, Athanasios Mouchtaris, Siegfried Kunzmann

LATTENTION: Lattice attention in ASR rescoring
Prabhat Pandey, Sergio Duarte Torres, Ali Orkan Bayer, Ankur Gandhe, Volker Leutnant

Listen, know and spell: Knowledge-infused subword modeling for improving ASR performance of out-of-vocabulary (OOV) named entities
Nilaksh Das, Monica Sunkara, Dhanush Bekal, Duen Horng Chau, Sravan Bodapati, Katrin Kirchhoff

KG ASR rescoring.png
In "Listen, know and spell: Knowledge-infused subword modeling for improving ASR performance of OOV named entities", Amazon researchers show how to improve automatic speech recognition by incorporating information from knowledge graphs into the processing pipeline.

Mitigating closed-model adversarial examples with Bayesian neural modeling for enhanced end-to-end speech recognition
Chao-Han Huck Yang, Zeeshan Ahmed, Yile Gu, Joseph Szurley, Roger Ren, Linda Liu, Andreas Stolcke, Ivan Bulyko

Multi-modal pre-training for automated speech recognition
David M. Chan, Shalini Ghosh, Debmalya Chakrabarty, Björn Hoffmeister

Multiturn encoder.png
The model used in "Multi-turn RNN-T for streaming recognition of multi-party speech" to disentangle overlapping speech in multi-party automatic speech recognition.

Multi-turn RNN-T for streaming recognition of multi-party speech
Ilya Sklyar, Anna Piunova, Xianrui Zheng, Yulan Liu

RescoreBERT: Discriminative speech recognition rescoring with BERT
Liyan Xu, Yile Gu, Jari Kolehmainen, Haidar Khan, Ankur Gandhe, Ariya Rastrow, Andreas Stolcke, Ivan Bulyko

USTED: Improving ASR with a unified speech and text encoder-decoder
Bolaji Yusuf, Ankur Gandhe, Alex Sokolov

VADOI: Voice-activity-detection overlapping inference for end-to-end long-form speech recognition
Jinhan Wang, Xiaosu Tong, Jinxi Guo, Di He, Roland Maas

Computer vision

ASD-transformer: Efficient active speaker detection using self and multimodal transformers
Gourav Datta, Tyler Etchart, Vivek Yadav, Varsha Hedau, Pradeep Natarajan, Shih-Fu Chang

Dynamically pruning SegFormer for efficient semantic segmentation
Haoli Bai, Hongda Mao, Dinesh Nair

Enhancing contrastive learning with temporal cognizance for audio-visual representation generation
Chandrashekhar Lavania, Shiva Sundaram, Sundararajan Srinivasan, Katrin Kirchhoff

Few-shot gaze estimation with model offset predictors
Jiawei Ma, Xu Zhang, Yue Wu, Varsha Hedau, Shih-Fu Chang

Visual representation learning with self-supervised attention for low-label high-data regime
Prarthana Bhattacharyya, Chenge Li, Xiaonan Zhao, István Fehérvári, Jason Sun

Federated learning

Federated learning challenges and opportunities: An outlook
Jie Ding, Eric Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang

FL framework.png
The federated-learning scenario considered in "Federated learning challenges and opportunities: An outlook".

Learnings from federated learning in the real world
Christophe Dupuy, Tanya G. Roosta, Leo Long, Clement Chung, Rahul Gupta, Salman Avestimehr

Information retrieval

Contrastive knowledge graph attention network for request-based recipe recommendation
Xiyao Ma, Zheng Gao, Qian Hu, Mohamed Abdelhady

Keyword spotting

Unified speculation, detection, and verification keyword spotting
Geng-shen Fu, Thibaud Senechal, Aaron Challenner, Tao Zhang

Machine translation

Isometric MT: Neural machine translation for automatic dubbing
Surafel Melaku Lakew, Yogesh Virkar, Prashant Mathur, Marcello Federico

Natural-language understanding

ADVIN: Automatically discovering novel domains and intents from user text utterances
Nikhita Vedula, Rahul Gupta, Aman Alok, Mukund Sridhar, Shankar Ananthakrishnan

An efficient DP-SGD mechanism for large scale NLU models
Christophe Dupuy, Radhika Arava, Rahul Gupta, Anna Rumshisky

Paralinguistics

Confidence estimation for speech emotion recognition based on the relationship between emotion categories and primitives
Yang Li, Constantinos Papayiannis, Viktor Rozgic, Elizabeth Shriberg, Chao Wang

Multi-lingual multi-task speech emotion recognition using wav2vec 2.0
Mayank Sharma

Representation learning through cross-modal conditional teacher-student training for speech emotion recognition
Sundararajan Srinivasan, Zhaocheng Huang, Katrin Kirchhoff

Sentiment-aware automatic speech recognition pre-training for enhanced speech emotion recognition
Ayoub Ghriss, Bo Yang, Viktor Rozgic, Elizabeth Shriberg, Chao Wang

Personalization

Incremental user embedding modeling for personalized text classification
Ruixue Lian, Che-Wei Huang, Yuqing Tang, Qilong Gu, Chengyuan Ma, Chenlei (Edward) Guo

Signal processing

Deep adaptive AEC: Hybrid of deep learning and adaptive acoustic echo cancellation
Hao Zhang, Srivatsan Kandadai, Harsha Rao, Minje Kim, Tarun Pruthi, Trausti Kristjansson

Improved singing voice separation with chromagram-based pitch-aware remixing
Siyuan Yuan, Zhepei Wang, Umut Isik, Ritwik Giri, Jean-Marc Valin, Michael M. Goodwin, Arvindh Krishnaswamy

Sparse recovery of acoustic waves
Mohamed Mansour

Upmixing via style transfer: A variational autoencoder for disentangling spatial images and musical content
Haici Yang, Sanna Wager, Spencer Russell, Mike Luo, Minje Kim, Wontak Kim

Sound source localization

End-to-end Alexa device arbitration
Jarred Barber, Yifeng Fan, Tao Zhang

Speaker diarization/identification/verification

ASR-aware end-to-end neural diarization
Aparna Khare, Eunjung Han, Yuguang Yang, Andreas Stolcke

Improving fairness in speaker verification via group-adapted fusion network
Hua Shen, Yuguang Yang, Guoli Sun, Ryan Langman, Eunjung Han, Jasha Droppo, Andreas Stolcke

OpenFEAT: Improving speaker identification by open-set few-shot embedding adaptation with Transformer
Kishan K C, Zhenning Tan, Long Chen, Minho Jin, Eunjung Han, Andreas Stolcke, Chul Lee

Self-supervised speaker recognition training using human-machine dialogues
Metehan Cekic, Ruirui Li, Zeya Chen, Yuguang Yang, Andreas Stolcke, Upamanyu Madhow

Self-supervised speaker verification with simple Siamese network and self-supervised regularization
Mufan Sang, Haoqi Li, Fang Liu, Andrew O. Arnold, Li Wan

Spoken-language understanding

A neural prosody encoder for end-to-end dialogue act classification
Kai Wei, Dillon Knox, Martin Radfar, Thanh Tran, Markus Mueller, Grant P. Strimel, Nathan Susanj, Athanasios Mouchtaris, Maurizio Omologo

Multi-task RNN-T with semantic decoder for streamable spoken language understanding
Xuandi Fu, Feng-Ju Chang, Martin Radfar, Kai Wei, Jing Liu, Grant P. Strimel, Kanthashree Mysore Sathyendra

Tie your embeddings down: Cross-modal latent spaces for end-to-end spoken language understanding
Bhuvan Agrawal, Markus Mueller, Samridhi Choudhary, Martin Radfar, Athanasios Mouchtaris, Ross McGowan, Nathan Susanj, Siegfried Kunzmann

TINYS2I: A small-footprint utterance classification model with contextual support for on-device SLU
Anastasios Alexandridis, Kanthashree Mysore Sathyendra, Grant P. Strimel, Pavel Kveton, Jon Webb, Athanasios Mouchtaris

Text-to-speech

Cross-speaker style transfer for text-to-speech using data augmentation
Manuel Sam Ribeiro, Julian Roth, Giulia Comini, Goeric Huybrechts, Adam Gabrys, Jaime Lorenzo-Trueba

Distribution augmentation for low-resource expressive text-to-speech
Mateusz Lajszczak, Animesh Prasad, Arent van Korlaar, Bajibabu Bollepalli, Antonio Bonafonte, Arnaud Joly, Marco Nicolis, Alexis Moinet, Thomas Drugman, Trevor Wood, Elena Sokolova

Duration modeling of neural TTS for automatic dubbing
Johanes Effendi, Yogesh Virkar, Roberto Barra-Chicote, Marcello Federico

Neural speech synthesis on a shoestring: Improving the efficiency of LPCNET
Jean-Marc Valin, Umut Isik, Paris Smaragdis, Arvindh Krishnaswamy

Text-free non-parallel many-to-many voice conversion using normalising flows
Thomas Merritt, Abdelhamid Ezzerg, Piotr Biliński, Magdalena Proszewska, Kamil Pokora, Roberto Barra-Chicote, Daniel Korzekwa

VoiceFilter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module
Adam Gabrys, Goeric Huybrechts, Manuel Sam Ribeiro, Chung-Ming Chien, Julian Roth, Giulia Comini, Roberto Barra-Chicote, Bartek Perz, Jaime Lorenzo-Trueba

Time series forecasting

Robust nonparametric distribution forecast with backtest-based bootstrap and adaptive residual selection
Longshaokan Marshall Wang, Lingda Wang, Mina Georgieva, Paulo Machado, Abinaya Ulagappa, Safwan Ahmed, Yan Lu, Arjun Bakshi, Farhad Ghassemi

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Instrument Control Engineer, Software, Center for Quantum Computing
US, CA, Pasadena
The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire an Instrument Control Engineer to join our growing software team. You will work closely with our experimental physics and control hardware development teams to enable their work characterizing, calibrating, and operating novel quantum devices. The ideal candidate should be able to translate high-level science requirements into software implementations (e.g. Python APIs/frameworks, compiler passes, embedded SW, instrument drivers) that are performant, scalable, and intuitive. This requires someone who (1) has a strong desire to work within a team of scientists and engineers, and (2) demonstrates ownership in initiating and driving projects to completion. This role has a particular emphasis on working directly with our control hardware designers and vendors to develop instrument software for test and measurement. Inclusive Team Culture Here at Amazon, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Export Control Requirement Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility. Key job responsibilities - Work with control hardware developers, as a “subject matter expert” on the software interfaces around our control hardware - Collaborate with external control hardware vendors to understand and refine integration strategies - Implement instrument drivers and control logic in Python and/or a low-level languages, including C++ or Rust - Contribute to our compiler backend to enable the efficient execution of OpenQASM-based experiments on our next-generation control hardware - Benchmark system performance and help define key performance metrics - Ensure new features are successfully integrated into our Python-based experimental software stack - Partner with scientists to actively contribute to the codebase through mentorship and documentation We are looking for candidates with strong engineering principles, a bias for action, superior problem-solving, and excellent communication skills. Working effectively within a team environment is essential. As an Instrument Control Engineer embedded in a broader science organization, you will have the opportunity to work on new ideas and stay abreast of the field of experimental quantum computation. A day in the life Your time will be spent on projects that extend functional capabilities or performance of our internal research software stack. This requires working backwards from the needs of science staff in the context of our larger experimental roadmap. You will translate science and software requirements into design proposals balancing implementation complexity against time-to-delivery. Once a design proposal has been reviewed and accepted, you’ll drive implementation and coordinate with internal stakeholders to ensure a smooth roll out. Because many high-level experimental goals have cross-cutting requirements, you’ll often work closely with other engineers or scientists or on the team. About the team You will be joining the Software group within the Amazon Center of Quantum Computing. Our team is comprised of scientists and software engineers who are building scalable software that enables quantum computing technologies.
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