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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 Applied Scientist
US, WA, Seattle
At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities Key job responsibilities include: * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research
Member Technical Staff - Software Development, Frontier AI Robotics
US, CA, San Francisco
In this role, you will act as the primary specialist for physics engine internals and dynamics, developing high-fidelity, vectorized simulation environments for robotics locomotion, navigation, and interaction/manipulation. You will collaborate with hardware engineers to validate robot models and partner with research scientists to ensure numerical stability and physical accuracy for Sim2Real transfer. Your work focuses on tuning solvers, optimizing collision dynamics, and performing system identification to enable the training of robust robot control policies for complex, physical interactions. Key job responsibilities * Develop and maintain the shared simulation software framework, specifically owning the physics integration, robot state management, and control layers * Develop and optimize parallelized (vectorized) physics environments for high-throughput reinforcement learning (e.g., Isaac Lab, MuJoCo) * Tune physics engine parameters (solvers, friction, restitution) to support complex contact-rich scenarios required for dexterous manipulation and agile locomotion. * Implement and validate complex robot models (URDF/MJCF) involving precise actuator and sensor modeling * Collaborate with robot engineers and scientists to perform System Identification (SysID) to minimize the Sim2Real gap About the team At Frontier AI & Robotics (FAR), we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
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