Nuance Communications, Inc.

Microsoft Technology Licensing, LLC

Harman Becker Automotive Systems Gmbh

Daniel Willett

William F Ganong, Iii

Joel Praveen Pinto

Jianxiong Wu

Paul Joseph Vozila

Franz Gerl

Puming Zhan

Jean-Guy E Dahan

Chuang He

Raymond Brueckner

Yang Sun

Christian Gollan

Weiying Li

Venkatesh Nagesha

Josef Anastasiadis

Markus Schwarz

Dermot Connolly

Christian Plahl

Karl Jonas Lööf

Yue Pan

Lambert Mathias

William F Iii Ganong

Marcus Hennecke

Methods and apparatus for selectively performing speech processing in a hybrid speech processing system. The hybrid speech processing system includes at least one mobile electronic device and a network-connected server remotely located from the at least one mobile electronic device. The mobile electronic device is configured to use an embedded speech recognizer to process at least a portion of input audio to produce recognized text. A controller on the mobile electronic device determines whether to send information from the mobile electronic device to the server for speech processing. The determination of whether to send the information is based, at least in part, on an analysis of the input audio, the recognized text, or a semantic category associated with the recognized text.

Methods and apparatus for hybrid speech recognition processing

A system, method and computer-readable storage device provides an improved speech processing approach in which hyper parameters used for speech recognition are modified dynamically or in batch mode rather than fixed statically. The method includes estimating, via a model trained on audio data and/or metadata, a set of parameters useful for performing automatic speech recognition, receiving speech at an automatic speech recognition system, applying, by the automatic speech recognition system, the set of parameters to processing the speech to yield text and outputting the text from the automatic speech recognition system.

System and method for performing automatic speech recognition system parameter adjustment via machine learning

A method in accordance with the present disclosure may include receiving a message at a mobile computing device and performing natural language processing (NLP) based interpretation of the message. Embodiments may further include suggesting at least one of an action and an application configured to perform the action, the suggestion based upon, at least in part, the NLP-based interpretation of the message.

System and method for suggesting actions based upon incoming messages

A system and method for speech recognition is provided. Embodiments may include receiving an audio signal at a first deep neural network ('DNN') associated with a computing device. Embodiments may further include receiving the audio signal at a second deep neural network ('DNN') associated with a computing device, wherein the second deep neural network includes fewer parameters than the first deep neural network. Embodiments may also include determining whether to select an output from the first deep neural network or the second deep neural network and providing the selected output to a decoder with an overall objective of speeding up ASR.

Method for scoring in an automatic speech recognition system

A mobile device is adapted for automatic speech recognition (ASR). A user interface for interaction with a user includes an input microphone for obtaining speech inputs from the user for automatic speech recognition, and an output interface for system output to the user based on ASR results that correspond to the speech input. A local controller obtains a sample of non-ASR audio from the input microphone for ASR-adaptation to channel-specific ASR characteristics, and then provides a representation of the non-ASR audio to a remote ASR server for server-side adaptation to the channel-specific ASR characteristics, and then provides a representation of an unknown ASR speech input from the input microphone to the remote ASR server for determining ASR results corresponding to the unknown ASR speech input, and then provides the system output to the output interface.

Server-side ASR adaptation to speaker, device and noise condition via non-ASR audio transmission

Typical speech recognition systems usually use speaker-specific speech data to apply speaker adaptation to models and parameters associated with the speech recognition system. Given that speaker-specific speech data may not be available to the speech recognition system, information indicative of language skills is employed in adapting configurations of a speech recognition system. According to at least one example embodiment, a method and corresponding apparatus, for speech recognition comprise maintaining information indicative of language skills of users of the speech recognition system. A configuration of the speech recognition system for a user is determined based at least in part on corresponding information indicative of language skills of the user. Upon receiving speech data from the user, the configuration of the speech recognition system determined is employed in performing speech recognition.

Method and apparatus for exploiting language skill information in automatic speech recognition

A system and method for speech recognition is provided. Embodiments may include receiving, at a first computing device, a far-talk signal from a far-talk computing device, the far-talk signal transmitted using a first channel and corresponding to an audible sound. Embodiments may further include receiving, at the first computing device, a near-talk signal from a near-talk computing device, the near-talk signal transmitted using a second channel and corresponding to the audible sound, wherein the far-talk signal and the near-talk signal are received during an enrollment phase of a far-talk speech recognition system. Embodiments may also include updating, at the first computing device, one or more models associated with a far-talk speech recognition system based upon, at least in part, one or more characteristics of the far-talk signal and one or more characteristics of the near-talk signal.

Method for training an automatic speech recognition system

A computer-implemented method is described for front end speech processing for automatic speech recognition. A sequence of speech features which characterize an unknown speech input is received with a computer process. A first subset of the speech features is normalized with a computer process using a first feature normalizing function. A second subset of the speech features is normalized with a computer process using a second feature normalizing function different from the first feature normalizing function. The normalized speech features in the first and second subsets are combined with a computer process to produce a sequence of mixed normalized speech features for automatic speech recognition.

Feature normalization inputs to front end processing for automatic speech recognition

A computer-implemented method is described for front end speech processing for automatic speech recognition. A sequence of speech features which characterize an unknown speech input provided on an audio input channel and associated meta-data which characterize the audio input channel are received. The speech features are transformed with a computer process that uses a trained mapping function controlled by the meta-data, and automatic speech recognition is performed of the transformed speech features.

Meta-data inputs to front end processing for automatic speech recognition

A mobile device is described which is adapted for automatic speech recognition (ASR). A speech input receives an unknown speech input signal from a user. A local controller determines if a remote ASR processing condition is met, transforms the speech input signal into a selected one of multiple different speech representation types, and sends the transformed speech input signal to a remote server for remote ASR processing. A local ASR arrangement performs local ASR processing of the speech input including processing any speech recognition results received from the remote server.

Hybrid controller for ASR

A method for speech recognition is implemented in the specific form of computer processes that function in a computer processor. That is, one or more computer processes: process a speech input to produce a sequence of representative speech vectors and perform multiple recognition passes to determine a recognition output corresponding to the speech input. At least one generic recognition pass is based on a generic speech recognition arrangement using generic modeling of a broad general class of input speech. And at least one adapted recognition pass is based on a speech adapted arrangement using pre-adapted modeling of a specific sub-class of the general class of input speech.

Guest speaker robust adapted speech recognition

A computer-implemented method is described for speaker adaptation in automatic speech recognition. Speech recognition data from a particular speaker is used for adaptation of an initial speech recognition acoustic model to produce a speaker adapted acoustic model. A speaker dependent differential acoustic model is determined that represents differences between the initial speech recognition acoustic model and the speaker adapted acoustic model. In addition, an approach is also disclosed to estimate speaker-specific feature or model transforms over multiple sessions. This is achieved by updating the previously estimated transform using only adaptation statistics of the current session.

Differential acoustic model representation and linear transform-based adaptation for efficient user profile update techniques in automatic speech recognition

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Walluf, Germany

Daniel Willett