Apple Inc.

Matthias Neeracher

Jerome Rene Bellegarda

Devang Kalidas Naik

Kim Silverman

Kim E A Silverman

Kevin Bartlett Aitken

Ladan Golipour

Ramya Rasipuram

Kim Ea Silverman

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

Methods, apparatus, systems, and computer program products are provided for synthesizing speech. One method includes matching a first level of units of a received input string to audio segments from a plurality of audio segments including using properties of or between first level units to locate matching audio segments from a plurality of selections, parsing unmatched first level units into second level units, matching the second level units to audio segments using properties of or between the units to locate matching audio segments from a plurality of selections and synthesizing the input string, including combining the audio segments associated with the first and second units.

Multi-unit approach to text-to-speech synthesis

Systems, apparatus, methods and computer program products are described for producing text-to-speech synthesis with non-speech sounds. In general, some of the pauses or silences that would otherwise be generated in synthesized speech are instead synthesized as non-speech sounds such as breaths. Non-speech sounds can be identified from pre-recorded speech that can include meta-data such as the grammatical and phrasal structure of words and sounds that precede and succeed non-speech sounds. A non-speech sound can be selected for use in synthesized speech based on the words, punctuation, grammatical and phrasal structure of text from which the speech is being synthesized, or other characteristics.

Using non-speech sounds during text-to-speech synthesis

A method and system for training a user authentication by voice signal are described. In one embodiment, during training, a set of all spectral feature vectors for a given speaker is globally decomposed into speaker-specific decomposition units and a speaker-specific recognition unit. During recognition, spectral feature vectors are locally decomposed into speaker-specific characteristic units. The speaker-specific recognition unit is used together with selected speaker-specific characteristic units to compute a speaker-specific comparison unit. If the speaker-specific comparison unit is within a threshold limit, then the voice signal is authenticated. In addition, a speaker-specific content unit is time-aligned with selected speaker-specific characteristic units. If the alignment is within a threshold limit, then the voice signal is authenticated. In one embodiment, if both thresholds are satisfied, then the user is authenticated.

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Santa Clara, CA, United States of America

Matthias Neeracher