Customer-obsessed science
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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
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July 9, 202610 min read
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ICASSP 20222022Confidence estimation for Speech Emotion Recognition (SER) is instrumental in improving the reliability in the behavior of downstream applications. In this work we propose (1) a novel confidence metric for SER based on the relationship between emotion primitives: arousal, valence, and dominance (AVD) and emotion categories (ECs), (2) EmoConfidNet - a DNN trained alongside the EC recognizer to predict the
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ICASSP 20222022We propose a novel multi-task pre-training method for Speech Emotion Recognition (SER). We pre-train SER model simultaneously on Automatic Speech Recognition (ASR) and sentiment classification tasks to make the acoustic ASR model more “emotion aware”. We generate targets for the sentiment classification using text-to-sentiment model trained on publicly available data. Finally, we fine-tune the acoustic
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AISTATS 20222022Multivariate time series anomaly detection has become an active area of research in recent years, with Deep Learning models outperforming previous approaches on benchmark datasets. Among reconstruction-based models, most previous work has focused on Variational Autoencoders and Generative Adversarial Networks. This work presents DGHL, a new family of generative models for time series anomaly detection,
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ICASSP 20222022End-to-end (E2E) automatic speech recognition models like Recurrent Neural Networks Transducer (RNN-T) are becoming a popular choice for streaming ASR applications like voice assistants. While E2E models are very effective at learning representation of the training data they are trained on, their accuracy on unseen domains remains a challenging problem. Additionally, these models require paired audio and
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ICASSP 20222022Accurate and timely recognition of the trigger keyword is vital for a good customer experience on smart devices. In the traditional keyword spotting task, there is typically a trade-off needed between accuracy and latency, where higher accuracy can be achieved by waiting for more context. In this paper, we propose a deep learning model that separates the keyword spotting task into three phases in order
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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
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
