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A quick guide to Amazon’s 40-plus papers at Interspeech 2022

Speech recognition and text-to-speech predominate, but other topics include audio watermarking, automatic dubbing, and compression.

By Staff writer
September 6, 2022
6 min read
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Of Amazon’s more than 40 papers at this year’s Interspeech, automatic speech recognition and text-to-speech account for about half. But the others cover a range of topics, from acoustic watermarking and automatic dubbing to quantization and fairness.

Acoustic watermarking

Practical over-the-air perceptual acoustic watermarking
Ameya Agaskar

Audio classification

CNN-based audio event recognition for automated violence classification and rating for Prime Video content
Tarun Gupta, Mayank Sharma, Kenny Qiu, Xiang Hao, Raffay Hamid

Impact of acoustic event tagging on scene classification in a multi-task learning framework
Rahil Parikh, Harshavardhan Sundar, Ming Sun, Chao Wang, Spyros Matsoukas

Automatic dubbing

Isochrony-aware neural machine translation for automatic dubbing
Derek Tam, Surafel Melaku Lakew, Yogesh Virkar, Prashant Mathur, Marcello Federico

Prosodic alignment for off-screen automatic dubbing
Yogesh Virkar, Marcello Federico, Robert Enyedi, Roberto Barra-Chicote

Automatic speech recognition

Compute cost amortized transformer for streaming ASR
Yi Xie, Jonathan Macoskey, Martin Radfar, Feng-Ju Chang, Brian King, Ariya Rastrow, Athanasios Mouchtaris, Grant Strimel

Cost amortized Transformer.png
"Compute cost amortized Transformer for streaming ASR" proposes a mechanism that toggles components of Transformer blocks on and off to use computational resources more efficiently.

Content-context factorized representations for automated speech recognition
David M. Chan, Shalini Ghosh

ConvRNN-T: Convolutional augmented recurrent neural network transducers for streaming speech recognition
Martin Radfar, Rohit Barnwal, Rupak Vignesh Swaminathan, Feng-Ju Chang, Grant Strimel, Nathan Susanj, Athanasios Mouchtaris

Directed speech separation for automatic speech recognition of long-form conversational speech
Rohit Paturi, Sundararajan Srinivasan, Katrin Kirchhoff, Daniel Garcia-Romero

Domain prompts: Towards memory and compute efficient domain adaptation of ASR systems
Saket Dingliwa, Ashish Shenoy, Sravan Bodapati, Ankur Gandhe, Ravi Teja Gadde, Katrin Kirchhoff

Incremental learning for RNN-Transducer based speech recognition models
Deepak Baby, Pasquale D'Alterio, Valentin Mendelev

Knowledge distillation via module replacing for automatic speech recognition with recurrent neural network transducer
Kaiqi Zhao, Hieu Duy Nguyen, Animesh Jain, Nathan Susanj, Athanasios Mouchtaris, Lokesh Gupta, Ming Zhao

Learning to rank with BERT-based confidence models in ASR rescoring
Ting-Wei Wu, I-FAN CHEN, Ankur Gandhe

Reducing geographic disparities in automatic speech recognition via elastic weight consolidation
Viet Anh Trinh, Pegah Ghahremani, Brian King, Jasha Droppo, Andreas Stolcke, Roland Maas

RefTextLAS: Reference text biased listen, attend, and spell model for accurate reading evaluation
Phani Sankar Nidadavolu, Na Xu, Nick Jutila, Ravi Teja Gadde, Aswarth Abhilash Dara, Joseph Savold, Sapan Patel, Aaron Hoff, Veerdhawal Pande, Kevin Crews, Ankur Gandhe, Ariya Rastrow, Roland Maas

RNN-T lattice enhancement by grafting of pruned paths
Mirek Novak, Pavlos Papadopoulos

Using data augmentation and consistency regularization to improve semi-supervised speech recognition
Ashtosh Sapru

Dialogue

Contextual acoustic barge in classification for spoken dialog systems
Dhanush Bekal, Sundararajan Srinivasan, Sravan Bodapati, Srikanth Ronanki, Katrin Kirchhoff

Adversarial reweighting.png
The method presented in "Adversarial reweighting for speaker verification fairness" uses an adversarial network to identify underperforming groups in a speaker verification dataset (green) and adjusts their contribution to the training loss (bottom).

Fairness

Toward fairness in speech recognition: Discovery and mitigation of performance disparities
Pranav Dheram, Murugesan Ramakrishnan, Anirudh Raju, I-Fan Chen, Brian King, Katherine Powell, Melissa Saboowala, Karan Shetty, Andreas Stolcke

Keyword spotting

Latency control for keyword spotting
Christin Jose, Joe Wang, Grant Strimel, Mohammad Omar Khursheed, Yuriy Mishchenko, Brian Kulis

Language identification

A multimodal strategy for singing language identification
Wo Jae Lee, Emanuele Coviello

Multidevice processing

Challenges and opportunities in multi-device speech processing
Gregory Ciccarelli, Jarred Barber, Arun Nair, Israel Cohen, Tao Zhang

Multiparty speech

Separator-transducer-segmenter: Streaming recognition and segmentation of multi-party speech
Ilya Sklyar, Anna Piunova, Christian Osendorfer

Natural-language understanding

Phonetic embedding for ASR robustness in entity resolution
Xiaozhou Zhou, Ruying Bao, William M. Campbell

Quantization

Squashed weight distribution for low bit quantization of deep models
Nikko Ström, Haidar Khan, Wael Hamza

Sub-8-bit quantization aware training for 8-bit neural network accelerator with on device speech recognition
Kai Zhen, Hieu Duy Nguyen, Raviteja Chinta, Nathan Susanj, Athanasios Mouchtaris, Tariq Afzal, Ariya Rastrow

Sub-8-bit quantization.png
The training behavior of the algorithm proposed in "Sub-8-bit quantization aware training for 8-bit neural network accelerator with on device speech recognition", in which weights are optimized to lower quantization loss.

Signal processing

Clock skew robust acoustic echo cancellation
Karim Helwani, Erfan Soltanmohammadi, Michael M. Goodwin, Arvindh Krishnaswamy

Real-time packet loss concealment with mixed generative and predictive model
Jean-Marc Valin, Ahmed Mustafa, Christopher Montgomery, Timothy B. Terriberry, Michael Klingbeil, Paris Smaragdis, Arvindh Krishnaswamy

Speaker identification/verification

Adversarial reweighting for speaker verification fairness
Minho Jin, Chelsea J.-T. Ju, Zeya Chen, Yi Chieh Liu, Jasha Droppo, Andreas Stolcke

Graph-based multi-view fusion and local adaptation: Mitigating within household confusability for speaker identification
Long Chen, Yixiong Meng, Venkatesh Ravichandran, Andreas Stolcke

Graph fusion and fairness.png
The method proposed in "Graph-based multi-view fusion and local adaptation" propagates labels across a graph whose nodes represent utterances and whose weighted edges quantify the similarity between utterances.

Spoken-language understanding

Learning under label noise for robust spoken language understanding systems
Anoop Kumar, Pankaj Sharma, Aravind Illa, Sriram Venkatapathy, Subhrangshu Nandi, Pritam Varma, Anurag Dwarakanath, Aram Galstyan

On joint training with interfaces for spoken language understanding
Anirudh Raju, Milind Rao, Gautam Tiwari, Pranav Dheram, Bryan Anderson, Zhe Zhang, Chul Lee, Bach Bui, Ariya Rastrow

Text-to-speech

Automatic evaluation of speaker similarity
Kamil Deja, Ariadna Sanchez, Julian Roth, Marius Cotescu

CopyCat2: A single model for multi-speaker TTS and many-to-many fine-grained prosody transfer
Sri Karlapati, Penny Karanasou, Mateusz Lajszczak, Ammar Abbas, Alexis Moinet, Peter Makarov, Ray Li, Arent van Korlaar, Simon Slangen, Thomas Drugman

Creating new voices.png
Voices created through the method presented in "Creating new voices using normalizing flows" (green) are spread across the embedding space of voices from the training set (blue), confirming that the method can generate a variety of new voices.

Creating new voices using normalizing flows
Piotr Biliński, Tom Merritt, Abdelhamid Ezzerg, Kamil Pokora, Sebastian Cygert, Kayoko Yanagisawa, Roberto Barra-Chicote, Daniel Korzekwa

Cross-lingual style transfer with conditional prior VAE and style loss
Dino Ratcliffe, You Wang, Alex Mansbridge, Penny Karanasou, Alexis Moinet, Marius Cotescu

End-to-end LPCNet: A neural vocoder with fully-differentiable LPC estimation
Krishna Subramani, Jean-Marc Valin, Umut Isik, Paris Smaragdis, Arvindh Krishnaswamy

Expressive, variable, and controllable duration modelling in TTS
Ammar Abbas, Tom Merritt, Alexis Moinet, Sri Karlapati, Ewa Muszynska, Simon Slangen, Elia Gatti, Thomas Drugman

GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversion
Magdalena Proszewska, Grzegorz Beringer, Daniel Saez Trigueros, Tom Merritt, Abdelhamid Ezzerg, Roberto Barra-Chicote

L2-GEN: A neural phoneme paraphrasing approach to L2 speech synthesis for mispronunciation diagnosis
Daniel Zhang, Ashwinkumar Ganesan, Sarah Campbell, Daniel Korzekwa

Low data? No problem: low resource, language-agnostic conversational text-to-speech via F0- conditioned data augmentation
Giulia Comini, Goeric Huybrechts, Manuel Sam Ribeiro, Adam Gabrys, Jaime Lorenzo Trueba

Mix and match: An empirical study on training corpus composition for polyglot text-to-speech (TTS)
Ziyao Zhang, Alessio Falai, Ariadna Sanchez, Orazio Angelini, Kayoko Yanagisawa

Simple and effective multi-sentence TTS with expressive and coherent prosody
Peter Makarov, Ammar Abbas, Mateusz Lajszczak, Arnaud Joly, Sri Karlapati, Alexis Moinet, Thomas Drugman, Penny Karanasou

Unify and conquer: How phonetic feature representation affects polyglot text-to-speech (TTS)
Ariadna Sanchez, Alessio Falai, Ziyao Zhang, Orazio Angelini, Kayoko Yanagisawa

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Applied Scientist, FAR (Frontier AI & Robotics)
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As an Applied Scientist, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, scene understanding, sim2real transfer, multi-modal foundation models, and multi-task learning, designing novel algorithms that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Drive independent research initiatives in robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Lead technical projects from conceptualization through deployment, ensuring robust performance in production environments - Collaborate with platform teams to optimize and scale models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures, leveraging our extensive compute infrastructure to train and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through ground breaking 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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