Abstract: A computer system that classifies audio content is described. During operation, the computer system may receive audio content. Then, the computer system may determine a representation of the audio content (such as a signal-processing representation) by performing a transformation on the audio content. In some embodiments, the transformation may include a neural network and/or the representation may include word embedding or sense embedding of words in the audio content. Moreover, the computer system may analyze the representation using a predetermined neural network. Next, the computer system may classify, based at least in part on an output of the predetermined neural network, the audio content as being fake or real, where the fake audio content is, at least in part, computer-generated. Furthermore, the computer system may selectively perform a remedial action based at least in part on the classification.

Abstract: A computer system that trains a neural network is described. During operation, the computer system may receive information specifying a new attack vector corresponding to fake audio content. In response, the computer system may generate a synthetic training dataset based at least in part on the new attack vector. Then, the computer system may access a predetermined neural network that classifies real audio content and fake audio content, where the predetermined neural network was training without synthetic audio content corresponding to the new attack vector. Next, the computer system may train the neural network based at least in part on the synthetic training dataset and the predetermined neural network, where the training of the neural network may include modifying predetermined weights associated with the predetermined neural network, and where a training time for training the neural network may be less than a training time for training the predetermined neural network.

Abstract: A computer system that generates output speech is described. During operation, the computer system may receive an input associated with a type of interaction. Then, the computer system may generate, using a voice synthesis engine, the output speech corresponding to an individual based at least in part on the input, where the voice synthesis engine predicts positions and duration of a prosodic characteristic of speech by the individual, and selectively adds the prosodic characteristic of the speech by the individual in the output speech based at least in part on the prediction. Note that the prosodic characteristic may include: pauses in the speech by the individual, and/or disfluences in the speech by the individual.

Abstract: A computer system that classifies audio content is described. During operation, the computer system may receive audio content. Then, the computer system may determine a representation of the audio content (such as a signal-processing representation) by performing a transformation on the audio content. In some embodiments, the transformation may include a neural network and/or the representation may include word embedding or sense embedding of words in the audio content. Moreover, the computer system may analyze the representation using a predetermined neural network. Next, the computer system may classify, based at least in part on an output of the predetermined neural network, the audio content as being fake or real, where the fake audio content is, at least in part, computer-generated. Furthermore, the computer system may selectively perform a remedial action based at least in part on the classification.

Abstract: A computer system that classifies audio content is described. During operation, the computer system may receive audio content. Then, the computer system may determine a representation of the audio content (such as a signal-processing representation) by performing a transformation on the audio content. In some embodiments, the transformation may include a neural network and/or the representation may include word embedding or sense embedding of words in the audio content. Moreover, the computer system may analyze the representation using a predetermined neural network. Next, the computer system may classify, based at least in part on an output of the predetermined neural network, the audio content as being fake or real, where the fake audio content is, at least in part, computer-generated. Furthermore, the computer system may selectively perform a remedial action based at least in part on the classification.

Abstract: A computer system that classifies audio content is described. During operation, the computer system may receive audio content. Then, the computer system may determine a representation of the audio content (such as a signal-processing representation) by performing a transformation on the audio content. In some embodiments, the transformation may include a neural network and/or the representation may include word embedding or sense embedding of words in the audio content. Moreover, the computer system may analyze the representation using a predetermined neural network. Next, the computer system may classify, based at least in part on an output of the predetermined neural network, the audio content as being fake or real, where the fake audio content is, at least in part, computer-generated. Furthermore, the computer system may selectively perform a remedial action based at least in part on the classification.

Abstract: A computer system that generates output speech is described. During operation, the computer system may receive an input associated with a type of interaction. Then, the computer system may generate, using a voice synthesis engine, the output speech corresponding to an individual based at least in part on the input, where the voice synthesis engine predicts positions and duration of a prosodic characteristic of speech by the individual, and selectively adds the prosodic characteristic of the speech by the individual in the output speech based at least in part on the prediction. Note that the prosodic characteristic may include: pauses in the speech by the individual, and/or disfluences in the speech by the individual.

Abstract: A computer system that trains a neural network is described. During operation, the computer system may receive information specifying a new attack vector corresponding to fake audio content. In response, the computer system may generate a synthetic training dataset based at least in part on the new attack vector. Then, the computer system may access a predetermined neural network that classifies real audio content and fake audio content, where the predetermined neural network was training without synthetic audio content corresponding to the new attack vector. Next, the computer system may train the neural network based at least in part on the synthetic training dataset and the predetermined neural network, where the training of the neural network may include modifying predetermined weights associated with the predetermined neural network, and where a training time for training the neural network may be less than a training time for training the predetermined neural network.

Abstract: A system is configured to identify an entity referred to in speech or text by comparing the text of the entity mention to a database of the domain of the entity. The system may obtain a group of potential matches from the database and may then discriminatively rank those potential matches according to specific features identified for the context of the speech and/or text.

Abstract: Features are disclosed for processing user queries into a form that can produce relevant results. Spoken user queries can be transcribed into textual queries. Textual queries can be processed using a statistical model to identify entities within the queries. Running searches using the entities rather than the original search query can produce relevant results even when no result would have been obtained by running the original search query. In some embodiments, attributes may be identified and used during the search to narrow the results and potentially produce results that are more relevant.