Abstract: A computing system obtains text that includes words and provides the text as input to an emotional classifier model that has been trained based upon emotional classification. The computing system obtains a textual embedding of the computer-readable text as output of the emotional classifier model. The computing system generates a phoneme sequence based upon the words of the text. The computing system, generates, by way of an encoder of a text to speech (TTS) model, a phoneme encoding based upon the phoneme sequence. The computing system provides the textual embedding and the phoneme encoding as input to a decoder of the TTS model. The computing system causes speech that includes the words to be played over a speaker based upon output of the decoder of the TTS model, where the speech reflects an emotion underlying the text due to the textual embedding provided to the encoder.

Abstract: Methods, systems, and computer storage media for providing speech synthesis using a code-mixed speech engine in a speech synthesis system. A code-mixed speech engine supports generating natural and intelligible speech in a target speaker voice—for code-mixed-text of two or more languages—based on a code-mixed speech model that supports both code-mixing and cross-locale voice transfer scenarios. In operation, code-mixed training data associated with a plurality of different languages is accessed. A code-mixed speech model—associated with a training engine and an inference engine that support generating code-mixed synthesized speech—is generated. The code-mixed speech model is deployed. A request being received for synthesized speech of a speech synthesis service. An instance of code-mixed synthesized speech is generated. The instance of code-mixed synthesized speech is generated using the code-mixed speech model.

Abstract: In order to predict an optimized send time for one or more end users in an email campaign, one or more times in a day are identified for sending the email campaign to the one or more end users. The email campaign may then be transmitted at one of the one or more times identified to the one or more end users.

Abstract: The present application describes various methods and devices for providing content to users. In one aspect, a method includes, for each content item of a set of content items, obtaining a score for the content item using a recommender system, the score corresponding to a calculation of subsequent repeated engagement by a user with the content item. The method also includes ranking the set of content items based on the respective scores and providing recommendation information to the user for one or more highest ranked content items in the set of content items.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a set of candidate values for a field in a page. The set of candidate values can be evaluated for accuracy based at least in part on a machine learning model, wherein the machine learning model outputs a respective score for each candidate value that measures an accuracy of the candidate value for the field in the page. A best scoring candidate value can be determined from the set of candidate values. The field in the page can be associated with the best scoring candidate value.

Abstract: A text to speech (TTS) model is trained based on training data including text samples. The text samples are provided to a text embedding model for outputting text embeddings for the text samples. The text embeddings are clustered into several clusters of text embeddings. The several clusters are representative of variations in emotion. The TTS model is then trained based upon the several clusters of text embeddings. Upon being trained, the TTS model is configured to receive text input and output a spoken utterance that corresponds to the text input. The TTS model is configured to output the spoken utterance with emotion. The emotion is based upon the text input and the training of the TTS model.

Abstract: A computing system obtains text that includes words and provides the text as input to an emotional classifier model that has been trained based upon emotional classification. The computing system obtains a textual embedding of the computer-readable text as output of the emotional classifier model. The computing system generates a phoneme sequence based upon the words of the text. The computing system, generates, by way of an encoder of a text to speech (TTS) model, a phoneme encoding based upon the phoneme sequence. The computing system provides the textual embedding and the phoneme encoding as input to a decoder of the TTS model. The computing system causes speech that includes the words to be played over a speaker based upon output of the decoder of the TTS model, where the speech reflects an emotion underlying the text due to the textual embedding provided to the encoder.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a set of candidate values for a field in a page. The set of candidate values can be evaluated for accuracy based at least in part on a machine learning model, wherein the machine learning model outputs a respective score for each candidate value that measures an accuracy of the candidate value for the field in the page. A best scoring candidate value can be determined from the set of candidate values. The field in the page can be associated with the best scoring candidate value.