Customer-obsessed science


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March 24, 2023By leveraging neural vocoding, Amazon Chime SDK’s new deep-redundancy (DRED) technology can reconstruct long sequences of lost packets with little bandwidth overhead.
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March 21, 2023Tailoring neighborhood sizes and sampling probability to nodes’ degree of connectivity improves the utility of graph-neural-network embeddings by as much as 230%.
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March 20, 2023With Alexa Arena, developers can create simulated missions in which humans interact with virtual robots, providing a natural way to build generalizable AI models.
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April 30 - May 4, 2023
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May 1 - 5, 2023
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May 3 - 5, 2023
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March 27, 2023Initiative will advance artificial intelligence and machine learning research within speech, language, and multimodal-AI domains.
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March 23, 2023The center will support UIUC researchers in their development of novel approaches to conversational AI systems.
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March 23, 2023How Amazon is shaping a set of initiatives to enable academia-based talent to harmonize their passions, life stations, and career ambitions.
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March 15, 2023The submission period opens March 15 and closes on April 26.
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2023Current endpointing (EP) solutions learn in a supervised framework, which does not allow the model to incorporate feedback and improve in an online setting. Also, it is common practice to utilize costly grid-search to find the best configuration for an endpointing model. In this paper, we aim to provide a solution for adaptive endpointing by proposing an efficient method for choosing an optimal endpointing
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2023We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity. In contrast to conventional neural language model (LM) based ASR rescoring/reranking models, our approach focuses on acoustic information and conducts the rescoring collaboratively among utterances, instead of individually. Experiments on the VCTK dataset
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2023Personalization in multi-turn dialogs has been a long standing challenge for end-to-end automatic speech recognition (E2E ASR) models. Recent work on contextual adapters has tackled rare word recognition using user catalogs. This adaptation, however, does not incorporate an important cue, the dialog act, which is available in a multi-turn dialog scenario. In this work, we propose a dialog act guided contextual
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2023It is challenging to extract semantic meanings directly from audio signals in spoken language understanding (SLU), due to the lack of textual information. Popular end-to-end (E2E) SLU models utilize sequence-to-sequence automatic speech recognition (ASR) models to extract textual embeddings as input to infer semantics, which, however, require computationally expensive auto-regressive decoding. In this work
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2023End-to-End (E2E) automatic speech recognition (ASR) systems used in voice assistants often have difficulties recognizing infrequent words personalized to the user, such as names and places. Rare words often have non-trivial pronunciations, and in such cases, human knowledge in the form of a pronunciation lexicon can be useful. We propose a PROnunCiation-aware conTextual adaptER (PROCTER) that dynamically
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March 08, 2023This year’s cohort is researching, among other topics, online changepoint detection algorithms and automated reasoning.
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March 01, 2023New fellows include PhD candidates in operations research and computer science.
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February 21, 2023University teams are competing to develop a bot that best responds to customer commands in a virtual world.
Working at Amazon
View allMeet the people driving the innovation essential to being the world’s most customer-centric company.
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March 14, 2023Ren Zhang and her team tackle the interesting science challenges behind surfacing the most relevant offerings.
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February 28, 2023How the former astrobiology professor is charting new territory as a scientist for Amazon Flex.
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February 08, 2023How her background helps her manage a team charged with assisting internal partners to answer questions about the economic impacts of their decisions.