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A quick guide to Amazon's papers at Interspeech 2023

Speech recognition predominates, but Amazon's research takes in data representation, dialogue management, question answering, and more.

By Staff writer
August 18, 2023
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Amazon's papers at Interspeech 2023, sorted by research topic.

Automatic speech recognition

A metric-driven approach to conformer layer pruning for efficient ASR inference
Dhanush Bekal, Karthik Gopalakrishnan, Karel Mundnich, Srikanth Ronanki, Sravan Bodapati, Katrin Kirchhoff

Conmer: Streaming Conformer without self-attention for interactive voice assistants
Martin Radfar, Paulina Lyskawa, Brandon Trujillo, Yi Xie, Kai Zhen, Jahn Heymann, Denis Filimonov, Grant Strimel, Nathan Susanj, Athanasios Mouchtaris

DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer
Goeric Huybrechts, Srikanth Ronanki, Xilai Li, Hadis Nosrati, Sravan Bodapati, Katrin Kirchhoff

Distillation strategies for discriminative speech recognition rescoring
Prashanth Gurunath Shivakumar, Jari Kolehmainen, Yi Gu, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko

Effective training of attention-based contextual biasing adapters with synthetic audio for personalised ASR
Burin Naowarat, Philip Harding, Pasquale D'Alterio, Sibo Tong, Bashar Awwad Shiekh Hasan

Human transcription quality improvement
Jian Gao, Hanbo Sun, Cheng Cao, Zheng Du

Human transcription quality.png
In “Human transcription quality improvement”, Amazon researchers use machine learning models to align and score multiple transcription hypotheses from crowd workers.

Learning when to trust which teacher for weakly supervised ASR
Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath (Nath) Chennupati, Andreas Stolcke

Model-internal slot-triggered biasing for domain expansion in neural transducer ASR models
Edie Lu, Philip Harding, Kanthashree Mysore Sathyendra, Sibo Tong, Xuandi Fu, Jing Liu, Feng-Ju (Claire) Chang, Simon Wiesler, Grant Strimel

Multi-view frequency-attention alternative to CNN frontends for automatic speech recognition
Belen Alastruey Lasheras, Lukas Drude, Jahn Heymann, Simon Wiesler

Multilingual contextual adapters to improve custom word recognition in low-resource languages
Devang Kulshreshtha, Saket Dingliwal, Brady Houston, Sravan Bodapati

Multilingual contextual adapters.png
“Multilingual contextual adapters to improve custom word recognition in low-resource languages” proposes a three-stage process for training multilingual contextual adapters. Stage I trains a multilingual encoder; stage II learns multilingual contextual adapters by freezing the encoder; and stage III jointly optimizes both components on the target language.

PATCorrect: Non-autoregressive phoneme-augmented transformer for ASR error correction
Ziji Zhang, Zhehui Wang, Raj Kamma, Sharanya Eswaran, Narayanan Sadagopan

Personalization for BERT-based discriminative speech recognition rescoring
Jari Kolehmainen, Yi Gu, Aditya Gourav, Prashanth Gurunath Shivakumar, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko

Personalized predictive ASR for latency reduction in voice assistants
Andreas Schwarz, Di He, Maarten Van Segbroeck, Mohammed Hethnawi, Ariya Rastrow

Record deduplication for entity distribution modeling in ASR transcripts
Tianyu Huang, Chung Hoon Hong, Carl Wivagg, Kanna Shimizu

Scaling laws for discriminative speech recognition rescoring models
Yi Gu, Prashanth Gurunath Shivakumar, Jari Kolehmainen, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko

Selective biasing with trie-based contextual adapters for personalised speech recognition using neural transducers
Philip Harding, Sibo Tong, Simon Wiesler

Streaming speech-to-confusion network speech recognition
Denis Filimonov, Prabhat Pandey, Ariya Rastrow, Ankur Gandhe, Andreas Stolcke

Data representation

Don’t stop self-supervision: Accent adaptation of speech representations via residual adapters
Anshu Bhatia, Sanchit Sinha, Saket Dingliwal, Karthik Gopalakrishnan, Sravan Bodapati, Katrin Kirchhoff

Dialogue management

Parameter-efficient low-resource dialogue state tracking by prompt tuning
Mingyu Derek Ma, Jiun-Yu Kao, Shuyang Gao, Arpit Gupta, Di Jin, Tagyoung Chung, Violet Peng

Parameter efficient low resource dialogue state tracking.png
“Parameter-efficient low-resource dialogue state tracking by prompt tuning” proposes a method for using language-model prompts to do dialogue state tracking, with a separate, fixed-length embedding for each input segment.

Grapheme-to-phoneme conversion

Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings
Sam Ribeiro, Giulia Comini, Jaime Lorenzo Trueba

Keyword spotting

On-device constrained self-supervised speech representation learning for keyword spotting via knowledge distillation
Gene-Ping Yang, Yue Gu, Qingming Tang, Dongsu Du, Yuzong Liu

Natural-language understanding

Quantization-aware and tensor-compressed training of transformers for natural language understanding
Zi Yang, Samridhi Choudhary, Siegfried Kunzmann, Zheng Zhang

Sampling bias in NLU models: Impact and mitigation
Zefei Li, Anil Ramakrishna, Anna Rumshisky, Andy Rosenbaum, Saleh Soltan, Rahul Gupta

Understanding disrupted sentences using underspecified abstract meaning representation
Angus Addlesee, Marco Damonte

Paralinguistics

Towards paralinguistic-only speech representations for end-to-end speech emotion recognition
George Ioannides, Michael Owen, Andrew Fletcher, Viktor Rozgic, Chao Wang

Utility-preserving privacy-enabled Speech embeddings for emotion detection
Chandrashekhar Lavania, Sanjiv Das, Xin Huang, Kyu Han

Question answering

Question content alignment.png
In “Question-context alignment and answer-context dependencies for effective answer sentence selection,” Amazon researchers propose a method that uses the sentences surrounding answer candidates as additional context. Given probability distributions over sequences of words, the method aligns questions with answer candidates and context by using optimal transport to move probability from one distribution to another.

Question-context alignment and answer-context dependencies for effective answer sentence selection
Minh Van Nguyen, Kishan K C, Toan Nguyen, Thien Nguyen, Ankit Chadha, Thuy Vu

Speaker diarization

Lexical speaker error correction: Leveraging language models for speaker diarization error correction
Rohit Paturi, Sundararajan Srinivasan, Xiang Li

Speech translation

Knowledge distillation on joint task end-to-end speech translation

Khandokar Md. Nayem, Ran Xue, Ching-Yun (Frannie) Chang, Akshaya Vishnu Kudlu Shanbhogue

Text-to-speech

Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech
Guangyang Zhang, Tom Merritt, Sam Ribeiro, Biel Tura Vecino, Kayoko Yanagisawa, Kamil Pokora, Abdelhamid Ezzerg, Sebastian Cygert, Ammar Abbas, Piotr Bilinski, Roberto Barra-Chicote, Daniel Korzekwa, Jaime Lorenzo Trueba

Cross-lingual prosody transfer for expressive machine dubbing
Jakub Swiatkowski, Duo Wang, Mikolaj Babianski, Patrick Tobing, Ravi chander Vipperla, Vincent Pollet

Diffusion-based accent modelling in speech synthesis
Kamil Deja, Georgi Tinchev, Marta Czarnowska, Marius Cotescu, Jasha Droppo

eCat: An end-to-end model for multi-speaker TTS & many-to-many fine-grained prosody transfer
Ammar Abbas, Sri Karlapati, Bastian Schnell, Penny Karanasou, Marcel Granero Moya, Amith Nagaraj, Ayman Boustati, Nicole Peinelt, Alexis Moinet, Thomas Drugman

Expressive machine dubbing through phrase-level cross-lingual prosody transfer
Jakub Swiatkowski, Duo Wang, Mikolaj Babianski, Giuseppe Coccia, Patrick Tobing, Ravi chander Vipperla, Viacheslav Klimkov, Vincent Pollet

Expressive machine dubbing.png
The architecture proposed in “Expressive machine dubbing through phrase-level cross-lingual prosody transfer” relies on a reference encoder that explicitly models noise.

Multilingual context-based pronunciation learning for text-to-speech
Giulia Comini, Sam Ribeiro, Fan Yang, Heereen Shim, Jaime Lorenzo Trueba

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Data Scientist II , AWS Energy Team
US, WA, Seattle
AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. In 2019, Amazon co-founded The Climate Pledge and made a commitment to achieve net-zero carbon by 2040 —10 years ahead of the Paris Agreement. We invited others to join us and there are now more than 300 businesses and organizations across 51 industries and 29 countries that have signed the Pledge, which means we are collectively coming at the climate crisis from nearly every sector and nearly every angle. As part of our efforts to decarbonize our business, we became the world’s largest corporate purchaser of renewable energy in 2020, and last year, we reached 85% renewable energy across our business, and are on a path to power our operations with 100% renewable energy by 2025. We recently announced that AWS will be water positive by 2030, returning more water to communities than it uses in its direct operations. The company also announced its 2021 global water use efficiency (WUE) metric of 0.25 liters of water per kilowatt-hour, demonstrating AWS’s leadership in water efficiency among cloud providers. To learn more about AWS’s water+ commitment visit: Water Stewardship. Come join the team that is building the tools and innovative technology to manage our growing portfolio of renewable energy investments, including solar, on-shore and off-shore wind farms. Key job responsibilities As an data scientist, you will employ machine learning and analytics to create scalable solutions for problems in sustainable energy space. You will dissect large historical business data sets to enhance and streamline essential processes. You will partner with data and software teams to create models for predictive insights and establish automated methods for large data analysis. A day in the life To learn more, you can visit: amazon sustainability in the cloud About the team Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Principal Applied Scientist, Automated Reasoning in Identity
US, CA, Santa Clara
Are you passionate about applying automated reasoning and program analysis to real world problems? Do you want to create products that help customers? If so, then we have an exciting opportunity for you. We’re looking for an Applied Scientist to help strengthen our customers' security with automation for managed controls. AWS Identity provides the bedrock for secure and continuous access to all AWS services. By quickly connecting millions of users, across the world we empower organizations and enterprises to accelerate their cloud and digital transformation. In this role, you will interact with internal teams and external customers to understand their requirements. You will apply your knowledge to propose innovative solutions, create software prototypes, and productize prototypes into production systems using software development tools and methodologies. In addition, you will support and scale your solutions to meet the ever growing demand of customer use. Key job responsibilities * Interact with various teams to develop an understanding of their security and safety requirements. * Apply the acquired knowledge to build tools and algorithms, find problems, or show the absence of security/safety problems. * Implement these capabilities through the use of Automated Reasoning and various concepts from programming languages. * Perform analysis of the customer systems using tools developed in-house or externally provided * Create software prototypes to verify and validate the devised solutions methodologies; integrate the prototypes into production systems using standard software development tools and methodologies. About the team About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. This team is part of AWS Utility Computing: Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services.
Economist, Prime Science
US, WA, Seattle
Amazon Prime is looking for an ambitious Economist to help create econometric insights for world-wide Prime. Prime is Amazon's premiere membership program, with over 200M members world-wide. This role is at the center of many major company decisions that impact Amazon's customers. These decisions span a variety of industries, each reflecting the diversity of Prime benefits. These range from fast-free e-commerce shipping, digital content (e.g., exclusive streaming video, music, gaming, photos), and grocery offerings. Prime Science creates insights that power these decisions. As an economist in this role, you will create statistical tools that embed causal interpretations. You will utilize massive data, state-of-the-art scientific computing, econometrics (causal, counterfactual/structural, time-series forecasting, experimentation), and machine-learning, to do so. Some of the science you create will be publishable in internal or external scientific journals and conferences. You will work closely with a team of economists, applied scientists, data professionals (business analysts, business intelligence engineers), product managers, and software engineers. You will create insights from descriptive statistics, as well as from novel statistical and econometric models. You will create internal-to-Amazon-facing automated scientific data products to power company decisions. You will write strategic documents explaining how senior company leaders should utilize these insights to create sustainable value for customers. These leaders will often include the senior-most leaders at Amazon. The team is unique in its exposure to company-wide strategies as well as senior leadership. It operates at the cutting-edge of utilizing data, econometrics, artificial intelligence, and machine-learning to form business strategies. A successful candidate will have demonstrated a capacity for building, estimating, and defending statistical models (e.g., causal, counterfactual, time-series, machine-learning) using software such as R, Python, or STATA. They will have a willingness to learn and apply a broad set of statistical and computational techniques to supplement deep-training in one area of econometrics. For example, many applications on the team use structural econometrics, machine-learning, and time-series forecasting. They rely on building scalable production software, which involves a broad set of world-class software-building skills often learned on-the-job. As a consequence, already-obtained knowledge of SQL, machine learning, and large-scale scientific computing using distributed computing infrastructures such as Spark-Scala or PySpark would be a plus. Additionally, this candidate will show a track-record of delivering projects well and on-time, preferably in collaboration with other team members (e.g. co-authors). Candidates must have very strong writing and emotional intelligence skills (for collaborative teamwork, often with colleagues in different functional roles), a growth mindset, and a capacity for dealing with a high-level of ambiguity. Endowed with these traits and on-the-job-growth, the role will provide the opportunity to have a large strategic, world-wide impact on the customer experiences of Prime members.
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