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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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Sr. Applied Scientist, AWS Automated Reasoning
US, MA, Boston
Sr. Applied Scientists in AWS Automated Reasoning are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for automated reasoning, privacy, and sovereignty. Key job responsibilities The successful candidate will: - Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. - Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Diverse Experiences Amazon Automated Reasoning values diverse experiences. Even if you do not meet all of the 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 Amazon Automated Reasoning? At Amazon, automated reasoning is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for automated reasoning across all of Amazon's products and services. We offer talented automated reasoning professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Automated Reasoning, it's in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest automated reasoning challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & 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, training, 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.
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