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A quick guide to Amazon’s 40-plus papers at ICASSP

Topics such as code generation, commonsense reasoning, and self-learning complement the usual focus on speech recognition and acoustic-event classification.

By Staff writer
June 2, 2023
6 min read
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As usual at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), a plurality of Amazon’s accepted papers concentrate on automatic speech recognition — with, this year, a particular emphasis on personalized speech recognition. The topics of acoustic-event detection, keyword spotting, and signal processing are also well represented.

But as is also usual, some of the Amazon papers wander farther afield, to topics like commonsense reasoning, self-learning, query rewriting, and general machine learning techniques. Below is a quick guide to Amazon’s more than 40 papers at the conference.

Acoustic-event classification

FedRPO: Federated relaxed Pareto optimization for acoustic event classification
Meng Feng, Chieh-Chi Kao, Qingming Tang, Amit Solomon, Viktor Rozgic, Chao Wang

Multiscale audio spectrogram transformer for efficient audio classification
Wentao Zhu, Mohamed Omar

Transformer-based bioacoustic sound event detection on few-shot learning tasks
Liwen You, Erika Pelaez Coyotl, Suren Gunturu, Maarten Van Segbroeck

Weight-sharing supernet for searching specialized acoustic event classification networks across device constraints
Guan-Ting Lin, Qingming Tang, Chieh-Chi Kao, Viktor Rozgic, Chao Wang

Automatic speech recognition

Cross-utterance ASR rescoring with graph-based label propagation
Srinath Tankasala, Long Chen, Andreas Stolcke, Anirudh Raju, Shally Deng, Chander Chandak, Aparna Khare, Roland Maas, Venkatesh Ravichandran

Dynamic chunk convolution for unified streaming and non-streaming Conformer ASR
Xilai Li, Goeric Huybrechts, Srikanth Ronanki, Jeff Farris, Sravan Bodapati

Domain adaptation with external off-policy acoustic catalogs for scalable contextual end-to-end automated speech recognition
David M. Chan, Shalini Ghosh, Ariya Rastrow, Björn Hoffmeister

Gated contextual adapters for selective contextual biasing in neural transducers
Anastasios Alexandridis, Kanthashree Mysore Sathyendra, Grant Strimel, Feng-Ju (Claire) Chang, Ariya Rastrow, Nathan Susanj, Athanasios Mouchtaris

Mask the bias: Improving domain-adaptive generalization of CTC-based ASR with internal language model estimation
Nilaksh Das, Monica Sunkara, Sravan Bodapati, Jason Cai, Devang Kulshreshtha, Jeff Farris, Katrin Kirchhoff

On-the-fly text retrieval for end-to-end ASR adaptation
Bolaji Yusuf, Aditya Gourav, Ankur Gandhe, Ivan Bulyko

Robust acoustic and semantic contextual biasing in neural transducers for speech recognition
Xuandi Fu, Kanthashree Mysore Sathyendra, Ankur Gandhe, Jing Liu, Grant Strimel, Ross McGowan, Athanasios Mouchtaris

Code generation

Conversational text-to-SQL: An odyssey into state-of-the-art and challenges ahead
Sree Hari Krishnan Parthasarathi, Lu Zeng, Dilek Hakkani-Tür

Conversational text-to-SQL.png
A proposed text-to-SQL system has three parts: (a) multitasking on coherent tasks with discrete prompts; (b) constrained decoding; and (c) N-best list reranking with a query plan model and a schema linking algorithm. From "Conversational text-to-SQL: An odyssey into state-of-the-art and challenges ahead".

Commonsense reasoning

CLICKER: Attention-based cross-lingual commonsense knowledge transfer
Ruolin Su, Zhongkai Sun, Sixing Lu, Chengyuan Ma, Chenlei Guo

Continual learning

Quantifying catastrophic forgetting in continual federated learning
Christophe Dupuy, Jimit Majmudar, Jixuan Wang, Tanya Roosta, Rahul Gupta, Clement Chung, Jie Ding, Salman Avestimehr

Endpoint detection

Adaptive endpointing with deep contextual multi-armed bandits
Do June Min, Andreas Stolcke, Anirudh Raju, Colin Vaz, Di He, Venkatesh Ravichandran, Viet Anh Trinh

Towards accurate and real-time end-of-speech estimation
Yifeng Fan, Colin Vaz, Di He, Jahn Heymann, Viet Anh Trinh, Zhe Zhang, Venkatesh Ravichandran

Keyword spotting

Dual-attention neural transducers for efficient wake word spotting in speech recognition
Saumya Sahai, Jing Liu, Thejaswi Muniyappa, Kanthashree Mysore Sathyendra, Anastasios Alexandridis, Grant Strimel, Ross McGowan, Ariya Rastrow, Feng-Ju Chang, Athanasios Mouchtaris, Siegfried Kunzmann

Dual-attention transducers.png
From "Dual-attention neural transducers for efficient wake word spotting in speech recognition".

Fixed-point quantization aware training for on-device keyword-spotting
Sashank Macha, Om Oza, Alex Escott, Francesco Caliva, Robbie Armitano, Santosh Kumar Cheekatmalla, Sree Hari Krishnan Parthasarathi, Yuzong Liu

Self-supervised speech representation learning for keyword-spotting with light-weight transformers
Chenyang Gao, Yue Gu, Francesco Caliva, Yuzong Liu

Small-footprint slimmable networks for keyword spotting
Zuhaib Akhtar, Mohammad Omar Khursheed, Dongsu Du, Yuzong Liu

Language learning

Phonetic RNN-transducer for mispronunciation diagnosis
Daniel Zhang, Soumya Saha, Sarah Campbell

Machine learning

Prune then distill: Dataset distillation with importance sampling
Anirudh Sundar, Gokce Keskin, Chander Chandak, I-Fan Chen, Pegah Ghahremani, Shalini Ghosh

Role of bias terms in dot-product attention
Mahdi Namazifar, Devamanyu Hazarika, Dilek Hakkani-Tür

Natural-language understanding

Distill-quantize-tune: Leveraging large teachers for low-footprint efficient multilingual NLU on edge
Pegah Kharazmi, Zhewei Zhao, Clement Chung, Samridhi Choudhary

Pyramid dynamic inference: Encouraging faster inference via early exit boosting
Ershad Banijamali, Pegah Kharazmi, Sepehr Eghbali, Jixuan Wang, Clement Chung, Samridhi Choudhary

Personalized speech recognition

Dialog-act-guided adaptor.png
From "Dialog act guided contextual adapter for personalized speech recognition".

Dialog act guided contextual adapter for personalized speech recognition
Feng-Ju (Claire) Chang, Thejaswi Muniyappa, Kanthashree Mysore Sathyendra, Kai Wei, Grant Strimel, Ross McGowan

PROCTER: Pronunciation-aware contextual adapter for personalized speech recognition in neural transducers
Rahul Pandey, Roger Ren, Qi Luo, Jing Liu, Ariya Rastrow, Ankur Gandhe, Denis Filimonov, Grant Strimel, Andreas Stolcke, Ivan Bulyko

Slot-triggered contextual biasing for personalized speech recognition using neural transducers
Sibo Tong, Philip Harding, Simon Wiesler

Query rewriting

KG-ECO: Knowledge graph enhanced entity correction for query rewriting
Jason Cai, Mingda Li, Ziyan Jiang, Eunah Cho, Zheng Chen, Yang Liu, Xing Fan, Chenlei Guo

KG-ECO.png
From "KG-ECO: Knowledge graph enhanced entity correction for query rewriting".

Self-learning

Federated self-learning with weak supervision for speech recognition
Milind Rao, Gopinath Chennupati, Gautam Tiwari, Anit Kumar Sahu, Anirudh Raju, Ariya Rastrow, Jasha Droppo

Self-healing through error detection, attribution, and retraining
Ansel MacLaughlin, Anna Rumshisky, Rinat Khaziev, Anil Ramakrishna, Yuval Merhav, Rahul Gupta

Signal processing

A framework for unified real-time personalized and non-personalized speech enhancement
Zhepei Wang, Ritwik Giri, Devansh Shah, Jean-Marc Valin, Michael M. Goodwin, Paris Smaragdis

Augmentation robust self-supervised learning for human activity recognition
Cong Xu, Yuhang Li, Dae Lee, Andrew Park, Hongda Mao, Huyen Do, Jonathan Chung, Dinesh Nair

Retraction.png
The concept of retraction, mapping a point in the tangent space back to the manifold. From "Generative modeling based manifold learning for adaptive filtering guidance".

Generative modeling based manifold learning for adaptive filtering guidance
Karim Helwani, Paris Smaragdis, Michael M. Goodwin

SPADE: Self-supervised pretraining for acoustic disentanglement
John Harvill, Jarred Barber, Arun Nair, Ramin Pishehvar

Spoken-language understanding

End-to-end spoken language understanding using joint CTC loss and self-supervised, pretrained acoustic encoders
Jixuan Wang, Martin Radfar, Kai Wei, Clement Chung

Exploring subgroup performance in end-to-end speech models
Alkis Koudounas, Eliana Pastor, Giuseppe Attanasio, Vittorio Mazzia, Manuel Giollo, Thomas Gueudre, Luca Cagliero, Luca de Alfaro, Elena Baralis, Daniele Amberti

Multilingual end-to-end spoken language understanding for ultra-low footprint applications
Markus Mueller, Anastasios Alexandridis, Zach Trozenski, Joel Whiteman, Grant Strimel, Nathan Susanj, Athanasios Mouchtaris, Siegfried Kunzmann

Text-to-speech

Framewise WaveGAN: High speed adversarial vocoder in time domain with very low computational complexity
Ahmed Mustafa, Jean-Marc Valin, Jan Buethe, Paris Smaragdis, Mike Goodwin

Modelling low-resource accents without accent-specific TTS frontend
Georgi Tinchev, Marta Czarnowska, Kamil Deja, Kayoko Yanagisawa, Marius Cotescu

Video

ModEFormer: Modality-preserving embedding for audio-video synchronization using transformers
Akash Gupta, Rohun Tripathi, Wondong Jang

Multi-scale compositional constraints for representation learning on videos
Georgios Paraskevopoulos, Chandrashekhar Lavania, Lovish Chum, Shiva Sundaram

Voice communication

Low-bitrate redundancy coding of speech using a rate-distortion-optimized variational autoencoder
Jean-Marc Valin, Jan Buethe, Ahmed Mustafa

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Applied Scientist, AI Automation, Prime Video
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are forming a new organization within Prime Video to redefine our operational landscape through the power of artificial intelligence. As a Applied Scientist within this initiative, you will be a technical leader helping to design and build the intelligent systems that power our vision. You will tackle complex and ambiguous problems, designing and delivering scalable and resilient agentic AI and ML solutions from the ground up. You will not only write high-quality, maintainable software and models, but also mentor other scientists, influence our technical strategy, and drive engineering best practices across the team. Your work will directly contribute to making Prime Video's operations more efficient and will set the technical foundation for years to come. We're seeking candidates with strong experience in computer vision and generative AI technologies. In this role, you'll apply cutting-edge techniques in image and video understanding, visual content generation, and multimodal AI systems to transform how Prime Video operates at scale. Key job responsibilities • Lead the design and architecture of highly scalable, available, and resilient services for our AI automation platform. • Write high-quality, maintainable, and robust code to solve complex business problems, building flexible systems without over-engineering. • Act as a technical leader and mentor for other engineers on the team, assisting with career growth and encouraging excellence. • Work through ambiguous requirements, cut through complexity, and translate business needs into scalable technical solutions. • Take ownership of the full software development lifecycle, including design, testing, deployment, and operations. • Work closely with product managers, scientists, and other engineers to build and launch new features and systems. About the team This role offers a unique opportunity to shape the future of one of Amazon's most exciting businesses through the application of AI technologies. If you're passionate about leveraging AI to drive real-world impact at massive scale, we want to hear from you.
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