Speech technology and research lab
Communicating with, and through, computer applications
The Speech Technology and Research (STAR) Laboratory brings together a multidisciplinary mix of engineers, computer scientists and linguists. Together, our experts build systems for a wide range of applications including signal processing; data indexing and mining; and computer-aided learning. SRI’s speech and language technologies allow us to interact more naturally with computing applications and provide a wealth of actionable information about our intentions, health, and emotional state.
Core technologies and applications
Real-world impact
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What’s next in AI-based translation?
Reviewing the Apple AirPods Pro 3, The New York Times turned to SRI’s Dimitra Vergyri to unpack the significance of recent advances in translation tech.
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SRI’s AI-driven voice analysis could help screen for mental health conditions
Researchers at SRI are developing tools to help clinicians keep a close eye on depression, PTSD, and other mental health issues.
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SRI is developing textiles that record audio
Turning piezoelectric materials and lithium-ion batteries into thread, innovators will weave fabrics that record sound.
Featured researchers
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Dimitra Vergyri
Director, Speech Technology and Research Laboratory (STAR)
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Horacio Franco
Chief Scientist, Speech Technology and Research Laboratory
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Aaron Lawson
Assistant Laboratory Director, Speech Technology and Research Laboratory
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Martin Graciarena
Technical Manager, Speech Technology and Research Laboratory
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Mitchell McLaren
Senior Computer Scientist, Speech Technology and Research Laboratory
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Harry Bratt
Senior Computer Scientist, Speech Technology and Research Laboratory
Platforms
Publications
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Toward Fail-Safe Speaker Recognition: Trial-Based Calibration with a Reject Option
In this work, we extend the TBC method, proposing a new similarity metric for selecting training data that results in significant gains over the one proposed in the original work.
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Resilient Data Augmentation Approaches to Multimodal Verification in the News Domain
Building on multimodal embedding techniques, we show that data augmentation via two distinct approaches improves results: entity linking and cross-domain local similarity scaling.
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Natural Language Access: When Reasoning Makes Sense
We argue that to use natural language effectively, we must have both a deep understanding of the subject domain and a general-purpose reasoning capability.