Abstract: Systems and processes for generating a shared pronunciation lexicon and using the shared pronunciation lexicon to interpret spoken user inputs received by a virtual assistant are provided. In one example, the process can include receiving pronunciations for words or named entities from multiple users. The pronunciations can be tagged with context tags and stored in the shared pronunciation lexicon. The shared pronunciation lexicon can then be used to interpret a spoken user input received by a user device by determining a relevant subset of the shared pronunciation lexicon based on contextual information associated with the user device and performing speech-to-text conversion on the spoken user input using the determined subset of the shared pronunciation lexicon.

Abstract: Systems and processes for generating a shared pronunciation lexicon and using the shared pronunciation lexicon to interpret spoken user inputs received by a virtual assistant are provided. In one example, the process can include receiving pronunciations for words or named entities from multiple users. The pronunciations can be tagged with context tags and stored in the shared pronunciation lexicon. The shared pronunciation lexicon can then be used to interpret a spoken user input received by a user device by determining a relevant subset of the shared pronunciation lexicon based on contextual information associated with the user device and performing speech-to-text conversion on the spoken user input using the determined subset of the shared pronunciation lexicon.

Abstract: Input signal recognition, such as speech recognition, can be improved by incorporating location-based information. Such information can be incorporated by creating one or more language models that each include data specific to a pre-defined geographic location, such as local street names, business names, landmarks, etc. Using the location associated with the input signal, one or more local language models can be selected. Each of the local language models can be assigned a weight representative of the location's proximity to a pre-defined centroid associated with the local language model. The one or more local language models can then be merged with a global language model to generate a hybrid language model for use in the recognition process.