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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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Speech enhancement



Leading the industry in speech technology
Nuance’s speech enhancement group in Ulm, Germany, has been a research leader in the field of speech and audio signal processing for over 15 years. The team provides custom-tailored solutions for robust speech recognition and enhanced speech communication across many platforms and in a variety of environments. Our core research competencies include:

  • Single-microphone noise reduction approaches
  • Acoustic echo cancellation for voice user interfaces and telecommunication
  • Cutting-edge spatial filtering techniques for beam forming and interference cancellation

Using speech to enhance user-experience in other industries
Our research also encompasses more diverse speech enhancement topics such as speech reconstruction, bandwidth extension, dynamic signal mixing, or adaptive equalization. These efforts have resulted in an integrated speech enhancement front-end, which is deployed in various automotive, home, and mobile applications.
Our technology supports speech interaction with virtual personal assistants, even in the most challenging acoustic environments. As a leading expert for automotive speech enhancement, we also provide solutions for high-quality hands-free telephony as well as systems to improve in-car communication between front and rear passengers in large sedans and vans.

Explore recent publications by Nuance Speech Enhancement researchers.



Selected articles

Modeling subjectively perceived annoyance of H.264 video as a function of perceived artifact strength

This paper is concerned with the subjective perception of video coding artifacts in H.264/AVC encoded and decoded video. Our objective is to model the perceived

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Acoustic array processing for speech enhancement
Fast adaptation of speech and speaker characteristics for enhanced speech recognition in adverse intelligent environments

In this paper we present a technique for fast adaptation of speech and speaker related information. Fast learning is particularly useful for automatic personalization of

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A generalized view on microphone array postfilters

In this paper a generic transfer function for microphone array postfilters is derived and discussed. This function comprises a matrix which can be designed freely

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Influence of blocking matrix design on microphone array postfilters

In this paper the role of the blocking matrix is investigated in the context of microphone array postfilters. A generic stability criterion, which depends on

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Speaker tracking in an unsupervised speech controlled system

In this paper we present a technique to increase the robustness of a self-learning speech controlled system comprising speech recognition, speaker identification and speaker adaptation.

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Detection of unknown speakers in an unsupervised speech controlled system

In this paper we investigate the capability of our self-learning speech controlled system comprising speech recognition, speaker identification and speaker adaptation to detect unknown users.

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Evaluation of two approaches for speaker specific speech recognition

In this paper we examine two approaches for the automatic personalization of speech controlled systems. Speech recognition may be significantly improved by continuous speaker adaptation

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Robust voice activity detection for distributed microphones by modeling of power ratios

In this paper a method for a robust frequency selective voice activity detection (VAD) is presented that evaluates the power ratios between several distributed microphones.

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Phoneme-dependent speech enhancement

The majority of current speech enhancement systems are based on generalized signal-to-noise ratio dependent weighting rules and do not take into account the characteristics of

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