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IEEE SaTML 20242024We revisit the problem of differentially private squared error linear regression. We observe that existing state- of-the-art methods are sensitive to the choice of hyperparameters — including the “clipping threshold” that cannot be set optimally in a data-independent way. We give a new algorithm for private linear regression based on gradient boosting. We show that our method consistently improves over
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2024While word error rates of automatic speech recognition (ASR) systems have consistently fallen, natural language understanding (NLU) applications built on top of ASR systems still attribute significant numbers of failures to low-quality speech recognition results. Existing assistant systems collect large numbers of these unsuccessful interactions, but these systems usually fail to learn from these interactions
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2024In this work, we propose a novel sequence-discriminative training criterion for automatic speech recognition (ASR) based on the Conformer Transducer. Inspired by the large-margin classifier framework, we separate the “good” and the “bad” hypotheses in an N-best list produced from a pre-trained transducer model by a margin (τ ), hence the term, Max-Margin Transducer (MMT) loss. It is observed that fine-tuning
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2024Speech codec enhancement methods are designed to remove distortions added by speech codecs. While classical methods are very low in complexity and add zero delay, their effectiveness is rather limited. Compared to that, DNN-based methods deliver higher quality but they are typically high in complexity and/or require delay. The recently proposed Linear Adaptive Coding Enhancer (LACE) addresses this problem
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2024Automated speaker identification (SID) is a crucial step for the per-sonalization of a wide range of speech-enabled services. Typical SID systems use a symmetric enrollment-verification framework with a single model to derive embeddings both offline for voice profiles extracted from enrollment utterances, and online from runtime utter-ances. Due to the distinct circumstances of enrollment and runtime, such
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December 18, 2018At a recent press event on Alexa's latest features, Alexa’s head scientist, Rohit Prasad, mentioned multistep requests in one shot, a capability that allows you to ask Alexa to do multiple things at once. For example, you might say, “Alexa, add bananas, peanut butter, and paper towels to my shopping list.” Alexa should intelligently figure out that “peanut butter” and “paper towels” name two items, not four, and that bananas are a separate item.
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December 13, 2018Language models are a key component of automatic speech recognition systems, which convert speech into text. A language model captures the statistical likelihood of any particular string of words, so it can help decide between different interpretations of the same sequence of sounds.
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December 11, 2018Suppose that you say to Alexa, “Alexa, play Mary Poppins.” Alexa must decide whether you mean the book, the video, or the soundtrack. How should she do it?
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December 7, 2018In the past few years, advances in artificial intelligence have captured our imaginations and led to the widespread use of voice services on our phones and in our homes.