Apple Inc.

Ramya Rasipuram

Ladan Golipour

David A Winarsky

Matthias Neeracher

William Beckman

Cheng-Chieh Yeh

Weicheng Zhang

Systems and processes for generating audio books from text are provided. An example process includes, at an electronic device having one or more processors and memory: receiving a text including at least a first subset and a second subset, wherein at least a portion of the first subset overlaps with at least a portion of the second subset; determining, based on the text, a prosody for a speech output, wherein the prosody is representative of a genre; determining a semantic meaning of the text; and generating, based on the prosody and the semantic meaning, the speech output of the text.

Generating genre appropriate voices for audio books

Systems and processes for operating an intelligent automated assistant to perform text-to-speech conversion are provided. An example method includes, at an electronic device having one or more processors, receiving a text corpus comprising unstructured natural language text. The method further includes generating a sequence of normalized text based on the received text corpus; and generating a pronunciation sequence representing the sequence of the normalized text. The method further includes causing an audio output to be provided to the user based on the pronunciation sequence. At least one of the sequence of normalized text and the pronunciation sequence is generated based on a data-driven learning network.

Text normalization based on a data-driven learning network

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Sunnyvale, CA, United States of America

Ramya Rasipuram