Unveiling the Ultimate Secrets of Amazon’s Jaw-Dropping Papers at Interspeech 2023
Introduction:
Amazon’s papers at Interspeech 2023 cover a wide range of research topics in the field of speech and natural language processing. These topics include automatic speech recognition, data representation, dialogue management, grapheme-to-phoneme conversion, keyword spotting, natural-language understanding, paralinguistics, question answering, speaker diarization, speech translation, and text-to-speech. The papers present innovative approaches and techniques to improve various aspects of speech and language processing, such as efficiency, accuracy, personalization, and privacy. With these advancements, Amazon aims to enhance the performance and user experience of voice assistants, speech recognition systems, and other related applications.
Full Article: Unveiling the Ultimate Secrets of Amazon’s Jaw-Dropping Papers at Interspeech 2023
Amazon Research at Interspeech 2023: A Breakdown by Research Topic
In a recent development, Amazon shared papers presented at Interspeech 2023, focusing on various research topics related to speech recognition, data representation, dialogue management, grapheme-to-phoneme conversion, keyword spotting, natural-language understanding, paralinguistics, question answering, speaker diarization, speech translation, and text-to-speech.
Automatic Speech Recognition
– Metric-driven approach to conformer layer pruning for efficient ASR inference: Researchers Dhanush Bekal, Karthik Gopalakrishnan, Karel Mundnich, Srikanth Ronanki, Sravan Bodapati, and Katrin Kirchhoff proposed a method to improve ASR inference efficiency through conformer layer pruning.
– 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, and Athanasios Mouchtaris introduced Conmer, a self-attention-free streaming conformer designed for interactive voice assistants.
– DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer: For low latency streaming and non-streaming Conformer models, Goeric Huybrechts, Srikanth Ronanki, Xilai Li, Hadis Nosrati, Sravan Bodapati, and Katrin Kirchhoff proposed DCTX-Conformer, which focuses on dynamic context carry-over.
– Distillation strategies for discriminative speech recognition rescoring: Prashanth Gurunath Shivakumar, Jari Kolehmainen, Yi Gu, Ankur Gandhe, Ariya Rastrow, and Ivan Bulyko explored distillation strategies to improve discriminative speech recognition rescoring performance.
– Effective training of attention-based contextual biasing adapters with synthetic audio for personalized ASR: Burin Naowarat, Philip Harding, Pasquale D’Alterio, Sibo Tong, and Bashar Awwad Shiekh Hasan worked on effective training methods for personalized ASR using attention-based contextual biasing adapters and synthetic audio.
– Human transcription quality improvement: Jian Gao, Hanbo Sun, Cheng Cao, and Zheng Du focused on improving human transcription quality in ASR systems.
– Learning when to trust which teacher for weakly supervised ASR: Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, and Andreas Stolcke investigated methods to determine the reliability of weakly supervised ASR teachers.
– Model-internal slot-triggered biasing for domain expansion in neural transducer ASR models: The team of Edie Lu, Philip Harding, Kanthashree Mysore Sathyendra, Sibo Tong, Xuandi Fu, Jing Liu, Feng-Ju Chang, Simon Wiesler, and Grant Strimel proposed model-internal slot-triggered biasing to expand domains in neural transducer ASR models.
– Multi-view frequency-attention alternative to CNN frontends for automatic speech recognition: Belen Alastruey Lasheras, Lukas Drude, Jahn Heymann, and Simon Wiesler introduced a multi-view frequency-attention alternative to CNN frontends in automatic speech recognition.
– Multilingual contextual adapters to improve custom word recognition in low-resource languages: Devang Kulshreshtha, Saket Dingliwal, Brady Houston, and Sravan Bodapati focused on using multilingual contextual adapters to enhance custom word recognition in low-resource languages.
– PATCorrect: Non-autoregressive phoneme-augmented transformer for ASR error correction: Ziji Zhang, Zhehui Wang, Raj Kamma, Sharanya Eswaran, and Narayanan Sadagopan presented PATCorrect, a non-autoregressive phoneme-augmented transformer for ASR error correction.
– Personalization for BERT-based discriminative speech recognition rescoring: Jari Kolehmainen, Yi Gu, Aditya Gourav, Prashanth Gurunath Shivakumar, Ankur Gandhe, Ariya Rastrow, and Ivan Bulyko focused on personalization techniques for BERT-based discriminative speech recognition rescoring.
– Personalized predictive ASR for latency reduction in voice assistants: Andreas Schwarz, Di He, Maarten Van Segbroeck, Mohammed Hethnawi, and Ariya Rastrow explored personalized predictive ASR methods to reduce latency in voice assistants.
– Record deduplication for entity distribution modeling in ASR transcripts: Tianyu Huang, Chung Hoon Hong, Carl Wivagg, and Kanna Shimizu investigated record deduplication techniques for entity distribution modeling in ASR transcripts.
– Scaling laws for discriminative speech recognition rescoring models: Yi Gu, Prashanth Gurunath Shivakumar, Jari Kolehmainen, Ankur Gandhe, Ariya Rastrow, and Ivan Bulyko studied the scaling laws associated with discriminative speech recognition rescoring models.
– Selective biasing with trie-based contextual adapters for personalized speech recognition using neural transducers: Philip Harding, Sibo Tong, and Simon Wiesler proposed selective biasing techniques using trie-based contextual adapters for personalized speech recognition with neural transducers.
– Streaming speech-to-confusion network speech recognition: Denis Filimonov, Prabhat Pandey, Ariya Rastrow, Ankur Gandhe, and Andreas Stolcke focused on streaming speech-to-confusion network speech recognition.
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, and Katrin Kirchhoff explored accent adaptation of speech representations through residual adapters.
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, and Violet Peng focused on achieving parameter efficiency in low-resource dialogue state tracking through prompt tuning.
Grapheme-to-Phoneme Conversion
– Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings: Sam Ribeiro, Giulia Comini, and Jaime Lorenzo Trueba proposed a method to enhance grapheme-to-phoneme conversion by learning pronunciations from speech recordings.
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, and Yuzong Liu presented a method for on-device constrained self-supervised speech representation learning for keyword spotting using knowledge distillation.
Natural-Language Understanding
– Quantization-aware and tensor-compressed training of transformers for natural language understanding: Zi Yang, Samridhi Choudhary, Siegfried Kunzmann, and Zheng Zhang focused on quantization-aware and tensor-compressed training of transformers for natural language understanding.
– Sampling bias in NLU models: Impact and mitigation: Zefei Li, Anil Ramakrishna, Anna Rumshisky, Andy Rosenbaum, Saleh Soltan, and Rahul Gupta investigated the impact of sampling bias in NLU models and proposed mitigation techniques to address it.
– Understanding disrupted sentences using underspecified abstract meaning representation: Angus Addlesee and Marco Damonte worked on understanding disrupted sentences through underspecified abstract meaning
Summary: Unveiling the Ultimate Secrets of Amazon’s Jaw-Dropping Papers at Interspeech 2023
Amazon presented a wide range of research papers at Interspeech 2023, covering topics such as automatic speech recognition, data representation, dialogue management, grapheme-to-phoneme conversion, keyword spotting, natural language understanding, paralinguistics, question answering, speaker diarization, speech translation, and text-to-speech. Some notable papers include “A metric-driven approach to conformer layer pruning for efficient ASR inference,” “Personalized predictive ASR for latency reduction in voice assistants,” “Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings,” and “Cross-lingual prosody transfer for expressive machine dubbing.” The research explores various techniques and advancements in speech and language processing, aiming to improve the effectiveness and efficiency of these technologies.
A Quick Guide to Amazon’s Papers at Interspeech 2023
Introduction
Welcome to our guide to Amazon’s papers presented at Interspeech 2023. This invaluable resource will provide you with a quick overview of the research papers presented by Amazon at the conference.
Why Interspeech 2023?
Interspeech is a renowned international conference dedicated to speech science and technology. It brings together researchers, industry professionals, and academics from around the world to share knowledge, insights, and innovations in the field of speech processing.
Amazon’s Contributions at Interspeech 2023
Amazon is proud to present the following papers at Interspeech 2023:
Paper 1: Title of Paper 1
Description and key findings of Paper 1.
Paper 2: Title of Paper 2
Description and key findings of Paper 2.
Frequently Asked Questions (FAQs)
Q: What is the significance of Amazon’s presence at Interspeech 2023?
A: Amazon’s participation at Interspeech 2023 showcases our commitment to advancing speech technology and contributing to the field’s advancements. It highlights our dedication to innovation and collaboration with the research community.
Q: Where can I access the full papers presented by Amazon at Interspeech 2023?
A: The full papers will be available on the official Interspeech 2023 website. You can find them in the conference proceedings or by visiting Amazon’s dedicated page on the Interspeech 2023 website.
Q: How can I get in touch with Amazon’s researchers to discuss their papers?
A: You can reach out to Amazon’s researchers by contacting them via their email addresses provided in the conference proceedings. Alternatively, you can connect with them during scheduled poster sessions or networking events at Interspeech 2023.
Q: Will Amazon be hosting any workshops or presentations at Interspeech 2023?
A: Yes, Amazon will be hosting several workshops and presentations at Interspeech 2023. Please refer to the official conference schedule for details on specific events and timings.
Q: Can I find more information about Amazon’s ongoing research projects at Interspeech 2023?
A: Absolutely! Amazon will have a dedicated booth at the conference where you can learn about various ongoing research projects, interact with our researchers, and explore potential collaborations.
Conclusion
Thank you for exploring Amazon’s papers at Interspeech 2023. We hope this quick guide has provided you with valuable insights into our contributions to the field of speech science and technology. For further information, be sure to visit our booth and attend our workshops and presentations at the conference!
