Abstract: A system and method of recording utterances for building Named Entity Recognition (“NER”) models, which are used to build dialog systems in which a computer listens and responds to human voice dialog. Utterances to be uttered may be provided to users through their mobile devices, which may record the user uttering (e.g., verbalizing, speaking, etc.) the utterances and upload the recording to a computer for processing. The use of the user's mobile device, which is programmed with an utterance collection application (e.g., configured as a mobile app), facilitates the use of crowd-sourcing human intelligence tasking for widespread collection of utterances from a population of users. As such, obtaining large datasets for building NER models may be facilitated by the system and method disclosed herein.

Abstract: A system and method of recording utterances for building Named Entity Recognition (“NER”) models, which are used to build dialog systems in which a computer listens and responds to human voice dialog. Utterances to be uttered may be provided to users through their mobile devices, which may record the user uttering (e.g., verbalizing, speaking, etc.) the utterances and upload the recording to a computer for processing. The use of the user's mobile device, which is programmed with an utterance collection application (e.g., configured as a mobile app), facilitates the use of crowd-sourcing human intelligence tasking for widespread collection of utterances from a population of users. As such, obtaining large datasets for building NER models may be facilitated by the system and method disclosed herein.

Abstract: Systems and methods of providing text related to utterances, and gathering voice data in response to the text are provide herein. In various implementations, an identification token that identifies a first file for a voice data collection campaign, and a second file for a session script may be received from a natural language processing training device. The first file and the second file may be used to configure the mobile application to display a sequence of screens, each of the sequence of screens containing text of at least one utterance specified in the voice data collection campaign. Voice data may be received from the natural language processing training device in response to user interaction with the text of the at least one utterance. The voice data and the text may be stored in a transcription library.

Abstract: A system and method of recording utterances for building Named Entity Recognition (“NER”) models, which are used to build dialog systems in which a computer listens and responds to human voice dialog. Utterances to be uttered may be provided to users through their mobile devices, which may record the user uttering (e.g., verbalizing, speaking, etc.) the utterances and upload the recording to a computer for processing. The use of the user's mobile device, which is programmed with an utterance collection application (e.g., configured as a mobile app), facilitates the use of crowd-sourcing human intelligence tasking for widespread collection of utterances from a population of users. As such, obtaining large datasets for building NER models may be facilitated by the system and method disclosed herein.

Abstract: Systems and methods of providing text related to utterances, and gathering voice data in response to the text are provide herein. In various implementations, an identification token that identifies a first file for a voice data collection campaign, and a second file for a session script may be received from a natural language processing training device. The first file and the second file may be used to configure the mobile application to display a sequence of screens, each of the sequence of screens containing text of at least one utterance specified in the voice data collection campaign. Voice data may be received from the natural language processing training device in response to user interaction with the text of the at least one utterance. The voice data and the text may be stored in a transcription library.

Abstract: Systems and methods for receiving natural language queries and/or commands and execute the queries and/or commands. The systems and methods overcomes the deficiencies of prior art speech query and response systems through the application of a complete speech-based information query, retrieval, presentation and command environment. This environment makes significant use of context, prior information, domain knowledge, and user specific profile data to achieve a natural environment for one or more users making queries or commands in multiple domains. Through this integrated approach, a complete speech-based natural language query and response environment can be created. The systems and methods creates, stores and uses extensive personal profile information for each user, thereby improving the reliability of determining the context and presenting the expected results for a particular question or command.