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August 26, 20265 min readDiscounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives. -
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The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
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Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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Computer Assisted Language Learning Journal2022The research community has long studied computer-assisted pronunciation training (CAPT) methods in non-native speech. Researchers focused on studying various model architectures, such as Bayesian networks and deep learning methods, as well as on the analysis of different representations of the speech signal. Despite significant progress in recent years, existing CAPT methods are not able to detect pronunciation
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Interspeech 20222022The goal of automatic dubbing is to perform speech-to-speech translation while achieving audiovisual coherence. This entails isochrony, i.e., translating the original speech by also matching its prosodic structure into phrases and pauses, especially when the speaker’s mouth is visible. In previous work, we introduced a prosodic alignment model to address isochrone or on-screen dubbing. In this work, we
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Interspeech 20222022We introduce the task of isochrony-aware machine translation which aims at generating translations suitable for dubbing. Dubbing of a spoken sentence requires transferring the content as well as the speech-pause structure of the source into the target language to achieve audiovisual coherence. Practically, this implies correctly projecting pauses from the source to the target and ensuring that target speech
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Interspeech 20222022Most real-world datasets contain inherent label noise which leads to memorization and overfitting when such data is used to train over-parameterized deep neural networks. While memorization in DNNs has been studied extensively in computer vision literature, the impact of noisy labels and various mitigation strategies in Spoken Language Understanding tasks is largely under-explored. In this paper, we perform
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Interspeech 20222022Automatic Speech Recognition (ASR) is increasingly used by edge applications such as intelligent virtual assistants. However, state-of-the-art ASR models such as Recurrent Neural Network - Transducer (RNN-T) are computationally intensive on resource-constrained edge devices. Knowledge Distillation (KD) is a promising approach to compress large models by using a large model (”teacher”) to train a small model
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
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