Inventing a more human conversation with technology

Nuance’s R&D team of talented scientists, linguists and engineers pioneers innovation in human-machine intelligence and communication, resulting in groundbreaking strides in voice, language understanding and AI that redefine how people and technology co-exist.

Research areas

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Natural language & AI

Natural Language Processing and Artificial Intelligence technologies enable users to communicate with the digital technology they use everyday, whether for work or recreation

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Speech recognition

Turning speech into text is at the heart of an amazing variety of products and services that enrich peoples’ lives

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Voice biometrics enables authentication through processing natural speech patterns

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Speech enhancement

Speech enhancement is focused on evolving the technologies used in voice-driven interfaces for an enhanced user experience and greater application flexibility for manufacturers

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Text to speech

Text-to-Speech (TTS) is a corner-stone Nuance technology, with a long research history combining ideas integrated from several teams and new hires

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Recent articles

Textual Inference: getting logic from humans

This paper describes a manual investigation of the SICK corpus, which is the proposed testing set for a new system for natural language inference. The

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CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data

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Universal Dependencies for Portuguese

This paper describes the process of converting the Portuguese Bosque corpus to the Universal Dependencies scheme version 2. The conversion was done by applying to

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Low-Complexity Pitch Estimation Based on Phase Differences Between Low-Resolution Spectra

Detection of voiced speech and estimation of the pitch frequency are important tasks for many speech processing algorithms. Pitch information can be used, e.g., to

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The battle of the giants: a case study of GPU vs FPGA optimisation for real-time image processing

This paper focuses on a thorough comparison of the two main hardware targets for real-time optimization of a computer vision algorithm: GPU and FPGA. Based

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Adieu Features? End-to-end speech emotion recognition using a deep convolutional recurrent network

The automatic recognition of spontaneous emotions from speech is a challenging task. On the one hand, acoustic features need to be robust enough to capture

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Breaking news

Charlie Ortiz featured on CNET’s list of Most Influential Latinos in Tech

Charlie Ortiz, head of Nuance’s Sunnyvale based AI lab, is #15 on CNET’s list of most influentual latinos in the tech industry. He was also featured there last year.

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Upcoming event

- April 20

Nuance is a sponsor of ICASSP 2018 in Calgary.

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Featured researcher

Brian Delaney
Brian Delaney

Brian Delaney is a manager in the Clinical Language Understanding group with research interests in the areas of machine learning and natural language processing. ...

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Seen on What’s next

How the machines will adjust to us: A short story about “conversational AI” growing up

For years we have been trained on how to interact with machines – how to use a mouse, what to click for a specific action, and maybe even how to write code in a variety of languages. But talking, gestures, or facial expressions are natural ways for us to communicate. Machines that can understand these […]

The post How the machines will adjust to us: A short story about “conversational AI” growing up appeared first on What’s next.