Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing audio data using neural networks.

Abstract: Methods and systems for automatic speech recognition and methods and systems for training acoustic language models are disclosed. In accordance with one automatic speech recognition method, an acoustic input data set is analyzed to identify portions of the input data set that conform to a general language and to identify portions of the input data set that conform to at least one dialect of the general language. In addition, a general language model and at least one dialect language model is applied to the input data set to perform speech recognition by dynamically selecting between the models in accordance with each of the identified portions. Further, speech recognition results obtained in accordance with the application of the models is output.

Abstract: Methods and systems for automatic speech recognition and methods and systems for training acoustic language models are disclosed. In accordance with one automatic speech recognition method, an acoustic input data set is analyzed to identify portions of the input data set that conform to a general language and to identify portions of the input data set that conform to at least one dialect of the general language. In addition, a general language model and at least one dialect language model is applied to the input data set to perform speech recognition by dynamically selecting between the models in accordance with each of the identified portions. Further, speech recognition results obtained in accordance with the application of the models is output.

Abstract: Systems and methods for training networks are provided. A method for training networks comprises receiving an input from each of a plurality of neural networks differing from each other in at least one of architecture, input modality, and feature type, connecting the plurality of neural networks through a common output layer, or through one or more common hidden layers and a common output layer to result in a joint network, and training the joint network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for large vocabulary continuous speech recognition. One method includes receiving audio data representing an utterance of a speaker. Acoustic features of the audio data are provided to a recurrent neural network trained using connectionist temporal classification to estimate likelihoods of occurrence of whole words based on acoustic feature input. Output of the recurrent neural network generated in response to the acoustic features is received. The output indicates a likelihood of occurrence for each of multiple different words in a vocabulary. A transcription for the utterance is generated based on the output of the recurrent neural network. The transcription is provided as output of the automated speech recognition system.

Abstract: Methods and systems for automatic speech recognition and methods and systems for training acoustic language models are disclosed. In accordance with one automatic speech recognition method, an acoustic input data set is analyzed to identify portions of the input data set that conform to a general language and to identify portions of the input data set that conform to at least one dialect of the general language. In addition, a general language model and at least one dialect language model is applied to the input data set to perform speech recognition by dynamically selecting between the models in accordance with each of the identified portions. Further, speech recognition results obtained in accordance with the application of the models is output.

Abstract: Systems and methods for spoken term detection are provided. A method for spoken term detection, comprises receiving phone level out-of-vocabulary (OOV) keyword queries, converting the phone level OOV keyword queries to words, generating a confusion network (CN) based keyword searching (KWS) index, and using the CN based KWS index for both in-vocabulary (IV) keyword queries and the OOV keyword queries.

Abstract: Systems and methods for training networks are provided. A method for training networks comprises receiving an input from each of a plurality of neural networks differing from each other in at least one of architecture, input modality, and feature type, connecting the plurality of neural networks through a common output layer, or through one or more common hidden layers and a common output layer to result in a joint network, and training the joint network.

Abstract: Systems and methods for training networks are provided. A method for training networks comprises receiving an input from each of a plurality of neural networks differing from each other in at least one of architecture, input modality, and feature type, connecting the plurality of neural networks through a common output layer, or through one or more common hidden layers and a common output layer to result in a joint network, and training the joint network.

Abstract: Systems and methods for processing a query include determining a plurality of sets of match candidates for a query using a processor, each of the plurality of sets of match candidates being independently determined from a plurality of diverse word lattice generation components of different type. The plurality of sets of match candidates is merged by generating a first score for each match candidate to provide a merged set of match candidates. A second score is computed for each match candidate of the merged set based upon features of that match candidate. The first score and the second score are combined to provide a final set of match candidates as matches to the query.

Abstract: Systems and methods for spoken term detection are provided. A method for spoken term detection, comprises receiving phone level out-of-vocabulary (OOV) keyword queries, converting the phone level OOV keyword queries to words, generating a confusion network (CN) based keyword searching (KWS) index, and using the CN based KWS index for both in-vocabulary (IV) keyword queries and the OOV keyword queries.

Abstract: Systems and methods for spoken term detection are provided. A method for spoken term detection, comprises receiving phone level out-of-vocabulary (OOV) keyword queries, converting the phone level OOV keyword queries to words, generating a confusion network (CN) based keyword searching (KWS) index, and using the CN based KWS index for both in-vocabulary (IV) keyword queries and the OOV keyword queries.

Abstract: Methods and systems for automatic speech recognition and methods and systems for training acoustic language models are disclosed. In accordance with one automatic speech recognition method, an acoustic input data set is analyzed to identify portions of the input data set that conform to a general language and to identify portions of the input data set that conform to at least one dialect of the general language. In addition, a general language model and at least one dialect language model is applied to the input data set to perform speech recognition by dynamically selecting between the models in accordance with each of the identified portions. Further, speech recognition results obtained in accordance with the application of the models is output.

Abstract: Systems and methods for spoken term detection are provided. A method for spoken term detection, comprises receiving phone level out-of-vocabulary (OOV) keyword queries, converting the phone level OOV keyword queries to words, generating a confusion network (CN) based keyword searching (KWS) index, and using the CN based KWS index for both in-vocabulary (IV) keyword queries and the OOV keyword queries.

Abstract: Systems and methods for training networks are provided. A method for training networks comprises receiving an input from each of a plurality of neural networks differing from each other in at least one of architecture, input modality, and feature type, connecting the plurality of neural networks through a common output layer, or through one or more common hidden layers and a common output layer to result in a joint network, and training the joint network.

Abstract: Systems and methods for processing a query include determining a plurality of sets of match candidates for a query using a processor, each of the plurality of sets of match candidates being independently determined from a plurality of diverse word lattice generation components of different type. The plurality of sets of match candidates is merged by generating a first score for each match candidate to provide a merged set of match candidates. A second score is computed for each match candidate of the merged set based upon features of that match candidate. The first score and the second score are combined to provide a final set of match candidates as matches to the query.

Abstract: Methods and systems for automatic speech recognition and methods and systems for training acoustic language models are disclosed. One system for automatic speech recognition includes a dialect recognition unit and a controller. The dialect recognition unit is configured to analyze acoustic input data to identify portions of the acoustic input data that conform to a general language and to identify portions of the acoustic input data that conform to at least one dialect of the general language. In addition, the controller is configured to apply a general language model and at least one dialect language model to the input data to perform speech recognition by dynamically selecting between the models in accordance with each of the identified portions.