Lattice Speech Corrections

A system comprising:

• data processing hardware;
• and
• memory hardware in communication with the data processing hardware and storing instructions that when executed on the data processing hardware causes the data processing hardware to perform operations comprising:
• receiving audio data corresponding to a query spoken by a user;
• processing, using a speech recognizer, the audio data to generate multiple candidate hypotheses, each candidate hypothesis corresponding to a candidate transcription for the query and represented by a respective sequence of hypothesized terms;
• for a corresponding candidate hypothesis among the multiple candidate hypotheses represented by the respective sequence of hypothesized terms that includes a source phrase from a list of phrase correction pairs each comprising a corresponding source phrase and a corresponding target phrase replacing the source phrase:
• generating a corresponding additional candidate hypothesis that replaces the source phrase included in the respective sequence of hypothesized terms with the corresponding target phrase;
• obtaining a first sequence of n-grams that includes the source phrase included in the respective sequence of hypothesized terms and one or more other terms that precede and/or are subsequent to the source phrase;
• obtaining a second sequence of n-grams that replaces the source phrase in the first sequence of n-grams with the corresponding target phrase, the second corrected sequence of n-grams including the corresponding target phrase and the same one or more other terms that precede and/or are subsequent to the source phrase;
• conditioning a language model on the first sequence of n-grams and the second sequence of n-grams to determine a higher prior likelihood score for a number of n-grams from the second sequence of n-grams that includes the corresponding target phrase than for a same number of n-grams from the first sequence of n-grams that includes the source phrase;
• and
• for the corresponding additional candidate hypothesis, determining, using the language model conditioned on the first sequence of n-grams and the second sequence of n-grams, a corresponding prior likelihood score;
• ranking the multiple candidate hypotheses and the corresponding additional candidate hypothesis based on the corresponding prior likelihood score for the corresponding additional candidate hypothesis;
• and
• generating a transcription of the query spoken by the user by selecting the highest ranking one of the multiple candidate hypotheses and the corresponding additional candidate hypothesis.