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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, Pricing and Promotion Optimization
US, WA, Seattle
Amazon's Pricing & Promotions Science is seeking a driven Applied Scientist to harness planet scale multi-modal datasets, and navigate a continuously evolving competitor landscape, in order to regularly generate fresh customer-relevant prices on billions of Amazon and Third Party Seller products worldwide. We are looking for a talented, organized, and customer-focused applied researchers to join our Pricing and Promotions Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning and reinforcement learning modeling expertise, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator, who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Key job responsibilities - See the big picture. Understand and influence the long term vision for Amazon's science-based competitive, perception-preserving pricing techniques - Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale - Stay informed. Establish mechanisms to stay up to date on latest scientific advancements in machine learning, neural networks, natural language processing, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems - Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. - Successfully execute & deliver. Apply your exceptional technical machine learning expertise to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring an applied scientist to drive our pricing optimization initiatives. The Price Optimization science team drives cross-domain and cross-system improvements through: - invent and deliver price optimization, simulation, and competitiveness tools for Sellers. - shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. - Promotion optimization initiatives exploring CX, discount amount, and cross-product optimization opportunities. - Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into many partner-team architectures, and is highly relevant to the customer, therefore this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. About the team About the team: the Pricing Discovery and Optimization team within P2 Science owns price quality, discovery and discount optimization initiatives, including criteria for internal price matching, price discovery into search, p13N and SP, pricing bandits, and Promotion type optimization. We leverage planet scale data on billions of Amazon and external competitor products to build advanced optimization models for pricing, elasticity estimation, product substitutability, and optimization. We preserve long term customer trust by ensuring Amazon's prices are always competitive and error free.
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