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Biometrics

Voice biometrics enables authentication through processing natural speech patterns. Commercial deployments of this technology have gained momentum due to its versatility and range of applications.

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Biometrics



Surpassing the standard
The Nuance team is dedicated to innovating faster, smaller, and more accurate algorithms to maintain and exceed the state-of-the-art with respect to speaker verification, speaker identification, speaker segmentation, speaker clustering, and language identification. Our dedication also extends to supporting the expanding number of devices and use-cases for the technology.

Where we’re headed next
There are many exciting projects in this field that exploit technology disciplines including digital signal processing, speech processing, pattern recognition, and numerical analysis. Some of our projects include:

  • Continuing accuracy improvements to our core text-dependent and text-independent speaker recognition technologies through refinements to the underlying I-Vector and scoring algorithms
  • Exploring various new technologies and signal detection strategies for improving robustness to playback attacks and other forms of biometric spoofing
  • Applying deep learning technology technologies such as end-to-end modeling, bottleneck features, and phoneme recognition towards improving our core speaker recognition technologies
  • Leveraging additional biometric technologies in our overall solutions for improved robustness and security

Explore recent publications by Nuance Biometric researchers.



Selected articles

Nuance – Politecnico di Torino’s 2012 NIST speaker recognition evaluation system

This paper describes the Nuance-Politecnico di Torino (NPT) speaker recognition system submitted to the NIST SRE12 evaluation campaign. Included are the results of post-evaluation tests,

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Adaptive systems for unsupervised speaker tracking and speech recognition

Speech recognition offers an intuitive and convenient interface to control technical devices. Improvements achieved through ongoing research activities enable the user to handle increasingly complex

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Loquendo-Politecnico Di Torino system for the 2009 NIST language recognition evaluation

This paper describes the system submitted by Loquendo and Politecnico di Torino (LPT) for the 2009 NIST Language Recognition Evaluation. The system is a combination

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Compensation of nuisance factors for speaker and language recognition

The variability of the channel and environment is one of the most important factors affecting the performance of text-independent speaker verification systems. The best techniques

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Stream-based speaker segmentation using speaker factors and eigenvoices

This paper presents a stream-based approach for unsupervised multi-speaker conversational speech segmentation. The main idea of this work is to exploit prior knowledge about the

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Speaker recognition – general classifier approaches and data fusion methods

Speaker recognition refers to the concept of recognizing a speaker by his/her voice or speech samples. Some of the important applications of speaker recognition include

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