Amazon Technologies, Inc.

Natali Arieli

Yochai Zvik

Iftah Gamzu

Nissim Halabi

Sagi Bernstein

Marina Haikin

Yaniv Ben-Yehuda

Eran Fainman

Systems and methods are described for mitigating errors introduced during processing of user input such as voice input. A query may be derived from processed user input. A performance predictor analyzes the query and uses historical data to predict whether the query will return relevant results if executed. If the query's predicted performance is below a threshold, a query rewriter may identify potential alternatives to the query from a library of 'known good' queries. Different analyzers may be applied to identify different sets of alternatives, and machine learning models may be applied to rank the outputs of the analyzers. The best-matching alternatives from each analyzer may then be provided as inputs to a further machine learning model, which assesses the probability that each of the identified alternatives reflects the intent of the user. A most likely alternative may then be selected to execute in place of the original query.

Rewriting queries

Techniques for speech-to-text hypothesis generation and hypothesis selection described. A text input representing at least part of a voice recording is received from a speech-to-text component. A first text alternative is generated using a finite state transducer based at least in part on the text input. A hypothesis from a hypothesis set is selected using a language model that includes probabilities for sequences of words, the hypothesis set including the text input and the first text alternative. A selected hypothesis text associated with the selected hypothesis is sent to a search engine.

Hypothesis generation and selection for inverse text normalization for search

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Tel Aviv, Israel

Natali Arieli