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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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Senior Research Scientist, Industrial Robotics Group
US, WA, Seattle
Amazon is seeking exceptional science talent to develop AI and machine learning systems that will enable the next generation of advanced manufacturing capabilities at unprecedented scale. We're building revolutionary software infrastructure that combines cutting-edge AI, large-scale optimization, and advanced manufacturing processes to create adaptive production control systems. As a Senior Research Scientist, you will develop and improve machine learning systems that enable real-time manufacturing flow decisions. You will leverage state-of-the-art optimization and ML techniques, evaluate them against representative manufacturing scenarios, and adapt them to meet the robustness, reliability, and performance needs of production environments. You will invent new algorithms where gaps exist. You'll collaborate closely with software engineering, manufacturing engineering, robotics simulation, and operations teams, and your outputs will directly power the systems that determine what to build next, where to allocate resources, and how to maximize throughput. The ideal candidate brings deep expertise in optimization and machine learning, with a proven track record of delivering scientifically complex solutions into production. You are hands-on, writing significant portions of critical-path scientific code while driving your team's scientific agenda. If you're passionate about inventing the intelligent manufacturing systems of tomorrow rather than optimizing those of today, this role offers the chance to make a lasting impact on the future of automation. Key job responsibilities - Identify and devise new scientific approaches for constraint identification, dispatch optimization, WIP release control, and predictive flow intelligence when the problem is ill-defined and new methodologies need to be invented - Lead the design, implementation, and successful delivery of scientifically complex solutions for real-time manufacturing flow optimization in production - Design and build ML models and optimization algorithms including constraint prediction, starvation risk forecasting, and dispatch optimization - Write a significant portion of critical-path scientific code with solutions that are inventive, maintainable, scalable, and extensible - Execute rapid, rigorous experimentation with reproducible results, closing the gap between simulation and real manufacturing environments - Build evaluation benchmarks that measure model performance against manufacturing outcomes including constraint utilization and throughput rather than traditional ML metrics alone - Influence your team's science and business strategy through insightful contributions to roadmaps, goals, and priorities - Partner with manufacturing engineering, robotics simulation, and applied intelligence teams to ensure scientific approaches are grounded in operational reality - Drive your team's scientific agenda and role model publishing of research results at peer-reviewed venues when appropriate and not precluded by business considerations - Actively participate in hiring and mentor other scientists, improving their skills and ability to deliver - Write clear narratives and documentation describing scientific solutions and design choices
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