Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system and/or method receives speech input including an accent. The accent is classified with an accent classifier to yield an accent classification. Automatic speech recognition is performed based on the speech input and the accent classification to yield an automatic speech recognition output. Natural language understanding is performed on the speech recognition output and the accent classification determining an intent of the speech recognition output. Natural language generation generates an output based on the speech recognition output and the intent and the accent classification. An output is rendered using text to speech based on the natural language generation and the accent classification.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Method and apparatus for providing visual feedback on an electronic device in a client/server speech recognition system comprising the electronic device and a network device remotely located from the electronic device. The method comprises processing, by an embedded speech recognizer of the electronic device, at least a portion of input audio comprising speech to produce local recognized speech, sending at least a portion of the input audio to the network device for remote speech recognition, and displaying, on a user interface of the electronic device, visual feedback based on at least a portion of the local recognized speech prior to receiving streaming recognition results from the network device.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.