Snap Inc.

Jacob Assa

Alan Bekker

Zach Moshe

Einav Itamar

Itamar Schen

Liron Harazi

Systems and methods are provided for performing automated speech recognition. The systems and methods perform operations comprising: accessing a language model that includes a plurality of n-grams, each of the plurality of n-grams comprising a respective sequence of words and corresponding LM score; selecting a target word to boost in the language model; receiving a boosting factor for the target word; identifying a target n-gram in the language model that includes the target word; identifying a subset of n-grams of the plurality of n-grams that include words in a portion of the target n-gram; and adjusting the LM score of the target n-gram based on the LM scores of the subset of n-grams and the boosting factor.

Boosting words in automated speech recognition

Systems and methods are provided for extracting entities from received speech. The systems and methods perform operations comprising receiving an audio file comprising speech input and processing, by a speech recognition engine, the audio file comprising the speech input to generate an initial character-based representation of the speech input. The operations further comprise processing, by an entity extractor, the initial character-based representation of the speech input to generate an estimated set of entities of the speech input. The operations further comprise generating, by the speech recognition engine, a textual representation of the speech input based on the estimated set of entities of the speech input.

Speech to entity

Systems and methods are provided for performing automated speech recognition. The systems and methods access a LM that includes a plurality of n-grams, each of the plurality of n-grams comprising a respective sequence of words and corresponding LM score and receive a list of words associated with a group classification, each word in the list of words being associated with a respective weight. The systems and method compute, based on the LM scores of the plurality of n-grams, a probability that a given word in the list of words associated with the group classification appears in an n-gram in the LM comprising an individual sequence of words and adds one or more new n-grams to the LM comprising one or more words in the list of words in combination with the individual sequence of words and associated with a particular LM score based on the computed probability.

Grouping similar words in a language model

Systems and methods are provided for providing emotion-based text to speech. The systems and methods perform operations comprising accessing a text string; storing a plurality of embeddings associated with a plurality of speakers, a first embedding for a first speaker being associated with a first emotion and a second embedding for a second speaker of the plurality of speakers being associated with a second emotion; selecting the first speaker to speak one or more words of the text string; determining that the one or more words are associated with the second emotion; generating, based on the first embedding and the second embedding, a third embedding for the first speaker associated with the second emotion; and applying the third embedding and the text string to a vocoder to generate an audio stream comprising the one or more words being spoken by the first speaker with the second emotion.

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New York, NY, United States of America

Jacob Assa