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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
5 min read
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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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The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will redesign how ads create personalized, relevant shopping experiences with customer value at the forefront. Key responsibilities include: - Design and develop solutions using GenAI, deep learning, multi-objective optimization and/or reinforcement learning to transform ad retrieval, auctions, whole-page relevance, and shopping experiences. - Partner with scientists, engineers, and product managers to build scalable, production-ready science solutions. - Apply industry advances in GenAI, Large Language Models (LLMs), and related fields to create innovative prototypes and concepts. - Improve the team's scientific and technical capabilities by implementing algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor junior scientists and engineers to build a high-performing, collaborative team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value.
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