Microsoft Technology Licensing, LLC

Apple Inc.

Murat Akbacak

Dilek Zeynep Hakkani-Tur

Gokhan Tur

Larry Paul Heck

Benoit F Dumoulin

Gunnar Evermann

Bryan Hansen

Systems and processes for identifying of a voice input providing one or more user credentials are provided. In one example process, a voice input can be received. A first character, a phrase identifying a second character, and a word can be identified based on the voice input. In response to the identification, the first character, the second character, and the word can be converted to text. The text can be caused to display, with a display, in a sequence corresponding to an order of the first character, the second character, and the word in the voice input.

Identification of voice inputs providing credentials

Systems and methods are provided for improving language models for speech recognition by personalizing knowledge sources utilized by the language models to specific users or user-population characteristics. A knowledge source, such as a knowledge graph, is personalized for a particular user by mapping entities or user actions from usage history for the user, such as query logs, to the knowledge source. The personalized knowledge source may be used to build a personal language model by training a language model with queries corresponding to entities or entity pairs that appear in usage history. In some embodiments, a personalized knowledge source for a specific user can be extended based on personalized knowledge sources of similar users.

Knowledge source personalization to improve language models

Systems and methods are provided for training language models using in-domain-like data collected automatically from one or more data sources. The data sources (such as text data or user-interactional data) are mined for specific types of data, including data related to style, content, and probability of relevance, which are then used for language model training. In one embodiment, a language model is trained from features extracted from a knowledge graph modified into a probabilistic graph, where entity popularities are represented and the popularity information is obtained from data sources related to the knowledge. Embodiments of language models trained from this data are particularly suitable for domain-specific conversational understanding tasks where natural language is used, such as user interaction with a game console or a personal assistant application on personal device.

Language modeling for conversational understanding domains using semantic web resources

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Burlingame, CA, United States of America

Murat Akbacak