Nuance Communications, Inc.

Harman Becker Automotive Systems Gmbh

Franz Gerl

Tobias Herbig

Gerhard Uwe Schmidt

Daniel Willett

Raymond Brueckner

Matthias Schulz

Markus Buck

Tim Haulick

Mohamed Krini

Markus Schwarz

Christian Hillebrecht

Udo Haiber

Marc Biedert

Oliver Gaupp

Gerhard Schmidt

Ulrich Schatz

Ronald Römer

Chistian Hillebrecht

This invention provides a method for determining, in a speech dialog system issuing speech prompts, a score value as an indicator for the presence of a wanted signal component in an input signal stemming from a microphone, comprising the steps of: using a first likelihood function to determine a first likelihood value for the presence of the wanted signal component in the input signal, using a second likelihood function to determine a second likelihood value for the presence of a noise signal component in the input signal, and determining a score value based on the first and the second likelihood values, wherein the first likelihood function is based on a predetermined reference wanted signal, and the second likelihood function is based on a predetermined reference noise signal.

Method for determining the presence of a wanted signal component

A method for detecting barge-in in a speech dialog system comprising determining whether a speech prompt is output by the speech dialog system, and detecting whether speech activity is present in an input signal based on a time-varying sensitivity threshold of a speech activity detector and/or based on speaker information, where the sensitivity threshold is increased if output of a speech prompt is determined and decreased if no output of a speech prompt is determined. If speech activity is detected in the input signal, the speech prompt may be interrupted or faded out. A speech dialog system configured to detect barge-in is also disclosed.

Detecting barge-in in a speech dialogue system

A system enhances the quality of a digital speech signal that may include noise. The system identifies vocal expressions that correspond to the digital speech signal. A signal-to-noise ratio of the digital speech signal is measured before a portion of the digital speech signal is synthesized. The selected portion of the digital speech signal may have a signal-to-noise ratio below a predetermined level and the synthesis of the digital speech signal may be based on speaker identification.

Partial speech reconstruction

The invention provides a speech recognition method for selecting a combination of list elements via a speech input, wherein a first list element of the combination is part of a first set of list elements and a second list element of the combination is part of a second set of list elements, the method comprising the steps of receiving the speech input, comparing each list element of the first set with the speech input to obtain a first candidate list of best matching list elements, processing the second set using the first candidate list to obtain a subset of the second set, comparing each list element of the subset of the second set with the speech input to obtain a second candidate list of best matching list elements, and selecting a combination of list elements using the first and the second candidate list.

Speech recognition method for selecting a combination of list elements via a speech input

A method for adapting a codebook for speech recognition, wherein the codebook is from a set of codebooks comprising a speaker-independent codebook and at least one speaker dependent codebook. A speech input is received and a feature vector based on the received speech input is determined. For each of the Gaussian densities, a first mean vector is estimated using an expectation process and taking into account the determined feature vector. For each of the Gaussian densities, a second mean vector using an Eigenvoice adaptation is determined taking into account the determined feature vector. For each of the Gaussian densities, the mean vector is set to a convex combination of the first and the second mean vector. Thus, this process allows for adaptation during operation and does not require a lengthy training phase.

Method for adapting a codebook for speech recognition

A system distinguishes a primary audio source and background noise to improve the quality of an audio signal. A speech signal from a microphone may be improved by identifying and dampening background noise to enhance speech. Stochastic models may be used to model speech and to model background noise. The models may determine which portions of the signal are speech and which portions are noise. The distinction may be used to improve the signal's quality, and for speaker identification or verification.

System for distinguishing desired audio signals from noise

A system and method for detecting a refrain in an audio file having vocal components. The method and system includes generating a phonetic transcription of a portion of the audio file, analyzing the phonetic transcription and identifying a vocal segment in the generated phonetic transcription that is repeated frequently. The method and system further relate to the speech-driven selection based on similarity of detected refrain and user input.

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Neu-Ulm, Germany

Franz Gerl