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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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Wireless Science Manager, Device Connectivity
US, CA, Sunnyvale
Come join the Device connectivity team in building the next generation of innovative wireless solution that create a magical experience on our products and services. We actively engage in strategic initiatives, foster partnerships with industry and academia, leverage foundational artificial intelligence and large language models to stay at the forefront of the technological advancements. We are seeking an experienced Applied Science Manager to lead and grow a team of applied scientists who are pushing the boundaries of AI/ML in wireless connectivity and sensing. In this role, you will combine deep technical expertise with strong people leadership to drive scientific innovation that directly impacts millions of customers worldwide. Key job responsibilities As a Applied Science Manager in the team, you will: Build, mentor, and develop a high-performing team of applied scientists, setting the technical bar through code reviews, design reviews, and hands-on contributions while fostering a culture of scientific excellence, innovation, and operational rigor. Define and drive the AI/ML science roadmap for wireless solutions by developing a deep understanding of Amazon's Devices and Services offerings, translating complex business problems into well-defined scientific challenges, identifying high-risk and high-impact technical directions, and guiding your team to deliver them from conception through production. Collaborate cross-functionally with engineering, product, and business partners to drive ML development from research through optimization and onto production devices, aligning science investments with product goals while meeting on-device performance, latency, and resource constraints. Balance exploratory research with production delivery timelines, ensuring the team maintains scientific rigor while meeting business commitments. Represent the team's AI innovations to both internal leadership and the external scientific community through leadership reviews, publications, patents, and conference presentations, providing clear articulation of science strategy, progress, and impact. About the team About the team Device Connectivity team is empowering possibilities through wireless innovation on our devices and through services, our vision is to design and develop transformative products and services that consistently exceed our customers' expectations.
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