Abstract: Systems and methods for training a natural language model of a natural language understanding (NLU) system are disclosed herein. A text string including at least a content entity is received. A determination is made as to whether the text string includes an obsequious expression. In response to determining the text string includes an obsequious expression, a determination is made as to whether the obsequious expression describes the content entity. A query is forwarded in response to determining the text string includes an obsequious expression and in determining the obsequious expression describes the content entity. In response to determining the obsequious expression describes the content entity, the query includes the obsequious expression and in response to determining the obsequious expression does not describe the content entity, the query does not include the obsequious expression.

Abstract: The system receives a voice query at an audio interface and converts the voice query to text. The system can determine pronunciation information during conversion and generate metadata that indicates a pronunciation of one or more words of the query, include phonetic information in the text query, or both. A query includes one or more entities that may be more accurately identified based on pronunciation. The system searches for information, content, or both among one or more databases based on the generated text query, pronunciation information, user profile information, search histories or trends, and optionally other information. The system identifies one or more entities or content items that match the text query, and retrieves the identified information to provide to the user.

Abstract: The system receives a voice query at an audio interface and converts the voice query to text. The system can determine pronunciation information during conversion and generate metadata the indicates a pronunciation of one or more words of the query, include phonetic information in the text query, or both. A query includes one or more entities, which may be more accurately identified based on pronunciation. The system searches for information, content, or both among one or more databases based on the generated text query, pronunciation information, user profile information, search histories or trends, and optionally other information. The system identifies one or more entities or content items that match the text query, and retrieves the identified information to provide to the user.

Abstract: The system identifies one or more entities or content items among a plurality of stored information. The system generates an audio file based on a first text string that represents the entity or content item. Based on the first text string and at least one speech criterion, the system generating, using a speech-to-text module a second text string based on the audio file. The system then compares the text strings and stores the second text string if it is not identical to the first text string. The system generates metadata that includes results from text-speech-text conversions to forecast possible misidentifications when responding to voice queries during search operations.