Abstract: Aspects detect or recognize shifts in topics in computer implemented speech recognition processes as a function of mapping keywords to non-verbal cues. An initial topic is mapped to one or more keywords extracted from a first spoken query within a user keyword ontology mapping. A query spoken subsequent in time to the first query is identified and distinguished by recognizing one or more non-verbal cues associated with the audio data input that include a time elapsed between the queries, and in some aspects a user's facial expression or motion activity. Aspects determine whether the second spoken query is directed to the initial topic or to a new topic that is different from the initial topic, as a function of mappings of the keyword(s) extracted from the first query to one or more keywords extracted from the second query and to the non-verbal cue(s) within the user ontology mapping.

Abstract: Aspects detect or recognize shifts in topics in computer implemented speech recognition processes as a function of mapping keywords to non-verbal cues. An initial topic is mapped to one or more keywords extracted from a first spoken query within a user keyword ontology mapping. A query spoken subsequent in time to the first query is identified and distinguished by recognizing one or more non-verbal cues associated with the audio data input that include a time elapsed between the queries, and in some aspects a user's facial expression or motion activity. Aspects determine whether the second spoken query is directed to the initial topic or to a new topic that is different from the initial topic, as a function of mappings of the keyword(s) extracted from the first query to one or more keywords extracted from the second query and to the non-verbal cue(s) within the user ontology mapping.

Abstract: Aspects detect or recognize shifts in topics in computer implemented speech recognition processes as a function of mapping keywords to non-verbal cues. An initial topic is mapped to one or more keywords extracted from a first spoken query within a user keyword ontology mapping. A query spoken subsequent in time to the first query is identified and distinguished by recognizing one or more non-verbal cues associated with the audio data input that include a time elapsed between the queries, and in some aspects a user's facial expression or motion activity. Aspects determine whether the second spoken query is directed to the initial topic or to a new topic that is different from the initial topic, as a function of mappings of the keyword(s) extracted from the first query to one or more keywords extracted from the second query and to the non-verbal cue(s) within the user ontology mapping.

Abstract: Aspects detect or recognize shifts in topics in computer implemented speech recognition processes as a function of mapping keywords to non-verbal cues. An initial topic is mapped to one or more keywords extracted from a first spoken query within a user keyword ontology mapping. A query spoken subsequent in time to the first query is identified and distinguished by recognizing one or more non-verbal cues associated with the audio data input that include a time elapsed between the queries, and in some aspects a user's facial expression or motion activity. Aspects determine whether the second spoken query is directed to the initial topic or to a new topic that is different from the initial topic, as a function of mappings of the keyword(s) extracted from the first query to one or more keywords extracted from the second query and to the non-verbal cue(s) within the user ontology mapping.