Nuance Communications

About: Nuance Communications is a company organization based out in Vienna, Austria. It is known for research contribution in the topics: Speech processing & Voice activity detection. The organization has 1518 authors who have published 1701 publications receiving 54891 citations. The organization is also known as: ScanSoft & ScanSoft Inc..

Methods And Apparatus For Selective Microphone Signal Combining

Abstract: Methods and apparatus for frequency selective signal mixing for speech enhancement. In one embodiment frequency-based channel selection is performed for signal magnitude, signal energy, and noise estimate using speaker activity detection information, signal-to- noise ratio, ami/or signal level, Freqneney-based channel selection is performed for a dynamic spectral floor to adjust the noise estimate using speaker dominance information. Noise reduction is performed on the signal for the selected channel.

Generating objectively evaluated sufficiently natural synthetic speech from text by using selective paraphrases

Abstract: A synthetic speech system includes a phoneme segment storage section for storing multiple phoneme segment data pieces; a synthesis section for generating voice data from text by reading phoneme segment data pieces representing the pronunciation of an inputted text from the phoneme segment storage section and connecting the phoneme segment data pieces to each other; a computing section for computing a score indicating the unnaturalness of the voice data representing the synthetic speech of the text; a paraphrase storage section for storing multiple paraphrases of the multiple first phrases; a replacement section for searching the text and replacing with appropriate paraphrases; and a judgment section for outputting generated voice data on condition that the computed score is smaller than a reference value and for inputting the text after the replacement to the synthesis section to cause the synthesis section to further generate voice data for the text.

System and method for using a library to interactively design natural language spoken dialog systems

Abstract: Aspects of the invention pertain to a system for using a library to interactively design natural language spoken dialog systems. The system may include a processing device and an extractor. The processing device may be configured to receive user input selections indicating ones of a group of spoken dialog data stored in a library. The extractor may be configured to extract the ones of the group of spoken dialog data and a model building module may be configured to build one of a Spoken Language Understanding (SLU) model or an Automatic Speech Recognition (ASR) model based on the extracted ones of the plurality of spoken dialog data.

Power pack for an autoinjector

Abstract: A power pack, for use in an autoinjector to deliver a drug from a container at an injection rate includes a power pack body configured to hold a liquefied gas and a flow regulator, the power pack body further including at least one outlet through which some of the liquefied gas can exit the power pack in a gaseous state such that, during use, the flow regulator will control a rate of exit flow such that the liquefied gas in the canister is maintained at substantially its vapor pressure, while the gas leaving the outlet applies an adaptively changing force to the drug in the container so that delivery of the drug will occur at about a constant injection rate.

Automorphism Groups of Graphical Models and Lifted Variational Inference

Abstract: Using the theory of group action, we first introduce the concept of the automorphism group of an exponential family or a graphical model, thus formalizing the general notion of symmetry of a probabilistic model. This automorphism group provides a precise mathematical framework for lifted inference in the general exponential family. Its group action partitions the set of random variables and feature functions into equivalent classes (called orbits) having identical marginals and expectations. Then the inference problem is effectively reduced to that of computing marginals or expectations for each class, thus avoiding the need to deal with each individual variable or feature. We demonstrate the usefulness of this general framework in lifting two classes of variational approximation for MAP inference: local LP relaxation and local LP relaxation with cycle constraints; the latter yields the first lifted inference that operate on a bound tighter than local constraints. Initial experimental results demonstrate that lifted MAP inference with cycle constraints achieved the state of the art performance, obtaining much better objective function values than local approximation while remaining relatively efficient.