Abstract: A real-time dialogue system that provides real-time transcription of the spoken text, with a sub-second delay by keeping track of word timings and word accuracy is provided. The system uses a grammar or a list of keywords to produce the transcripts by using a statistical language model. In addition, the system uses a deep neural network based I-vector system to constantly analyze the audio quality to assess and to identify additional metadata such as gender, language, accent, age, emotion and identity of an end user to enhance the response. The present invention provides a conversational dialogue system, to robustly identify certain specific user commands or intents, while otherwise allowing for a natural conversation, without switching between grammar based and natural language modes.

Abstract: A real-time dialogue system that provides real-time transcription of the spoken text, with a sub-second delay by keeping track of word timings and word accuracy is provided. The system uses a grammar or a list of keywords to produce the transcripts by using a statistical language model. In addition, the system uses a deep neural network based I-vector system to constantly analyze the audio quality to assess and to identify additional metadata such as gender, language, accent, age, emotion and identity of an end user to enhance the response. The present invention provides a conversational dialogue system, to robustly identify certain specific user commands or intents, while otherwise allowing for a natural conversation, without switching between grammar based and natural language modes.