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.

Abstract: A system and method for applying a linear transformation to classify and input event. In one aspect, a method for classification comprises the steps of capturing an input event; extracting an n-dimensional feature vector from the input event; applying a linear transformation to the feature vector to generate a pool of projections; utilizing different subsets from the pool of projections to classify the feature vector; and outputting a class identity of the classified feature vector. In another aspect, the step of utilizing different subsets from the pool of projections to classify the feature vector comprises the steps of, for each predefined class, selecting a subset from the pool of projections associated with the class; computing a score for the class based on the associated subset; and assigning, to the feature vector, the class having the highest computed score.