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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

Spectro-temporal features for excitation signal quantization in a speech reconstruction system

Models of non-disturbed voiced excitation signals are trained using pitch and short-term spectral features, including dynamic pitch as well as inter-frame and intra-frame phase shift

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Self-learning speaker identification

Current speech recognition systems suffer from variation of voice characteristics between speakers as they are usually based on speaker independent speech models. In order to resolve this

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A practical beamformer-postfilter system for adaptive speech enhancement in non-stationary noise environments

In this contribution we present an adaptive beamformer-postfilter system which can be used to suppress non-stationary noises. The emphasis lies on the spatial filtering property

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Dynamic signal combining for distributed microphone systems in car environments

Distributed microphone systems in cars usually provide dedicated microphones for several speakers where each micro phone captures the desired speech signal at the best. The

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Evolution of an adaptive unsupervised speech controlled system

In this paper we present a self-learning speech controlled system comprising speech recognition, speaker identification and speaker adaptation. Our goal is the automatic personalization of

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Adaptive receive side equalization for improved intelligibility in automotive hands-free systems

The intelligibility of the received audio signal in automotive hands-free systems typically suffers from limited bandwidth as well as from being rendered in noisy environment.

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Simultaneous speech recognition and speaker identification

In this paper we present a self-learning speech controlled system comprising speech recognition, speaker identification and speaker adaptation for a small number of users, e.g.

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Robust adaptive cancellation of interfering speakers for distributed microphone systems in cars

Hands-free systems in cars aim to capture the speech of different speakers at the best. Therefor distributed microphones can be aligned to each of these

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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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