User-specific language models (LMs) that include internal word indexes to a word table specific to the user-specific LM rather than a word table specific to a system-wide LM. When the system-wide LM is updated, the word table of the user-specific LM may be updated to translate the user-specific indices to system-wide indices. This prevents having to update the internal indices of the user-specific LM every time the system-wide LM is updated.

Customized speech processing language models

A speech recognition engine is provided. The speech recognition engine combines multiple, different statistical language models to allow a single instance of a speech recognition engine to recognize words applicable to a first group of users and a second group of users where the second group of users is a subset of the first group of users. At least one of the multiple, different statistical language models comprises words specific to the second group of users.

System and methods for combining finite state transducer based speech recognizers

Techniques are described for updating an automatic speech recognition (ASR) system that, prior to the update, is configured to perform ASR using a first finite-state transducer (FST) comprising a first set of paths representing recognizable speech sequences. A second FST may be accessed, comprising a second set of paths representing speech sequences to be recognized by the updated ASR system. By analyzing the second FST together with the first FST, a patch may be extracted and provided to the ASR system as an update, capable of being applied non-destructively to the first FST at the ASR system to cause the ASR system using the first FST with the patch to recognize speech using the second set of paths from the second FST. In some embodiments, the patch may be configured such that destructively applying the patch to the first FST creates a modified FST that is globally minimized.

Techniques for updating an automatic speech recognition system using finite-state transducers

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Techniques for updating an automatic speech recognition system using finite-state transducers