An intelligent voice recognition method, voice recognition apparatus and intelligent computing device are disclosed. An intelligent voice recognition method of a voice recognition apparatus according to an embodiment of the present invention detects a voice of a user, receives an authentication request from the user, and performs authentication for the user on the basis of a result of determination of whether authentication for the user has recently been performed and a result of recognition of the voice of the user, thereby reducing a time and the quantity of calculations necessary for user authentication. One or more of the voice recognition apparatus and the intelligent computing device can be associated with artificial intelligence (AI) modules, unmanned aerial vehicle (UAV) robots, augmented reality (AR) devices, virtual reality (VR) devices, 5G service related devices, etc.

Techniques for generating conversational responses for a conversational user interface are disclosed. In one embodiment, a method is disclosed comprising obtaining user input from a user via a conversational user interface, using the user input to obtain a user emotion and a user intent, obtaining candidate probabilities for a fragment of a response to the user input using the obtained user emotion, the obtained user intent and the user input, generating the response to the user input using the candidate probabilities obtained for the fragment to select a candidate for the fragment of the response, and communicating the response to the user via the conversational user interface.

Techniques for generating conversational responses for a conversational user interface are disclosed. In one embodiment, a method is disclosed comprising obtaining user input from a user via a conversational user interface, using the user input to obtain a user emotion and a user intent, obtaining candidate probabilities for a fragment of a response to the user input using the obtained user emotion, the obtained user intent and the user input, generating the response to the user input using the candidate probabilities obtained for the fragment to select a candidate for the fragment of the response, and communicating the response to the user via the conversational user interface.

Estimation accuracies of a conversation satisfaction and a speech satisfaction are improved. A learning data storage unit (10) stores learning data including a conversation voice containing a conversation including a plurality of speeches, a correct answer value of a conversation satisfaction for the conversation, and a correct answer value of a speech satisfaction for each speech included in the conversation. A model learning unit (13) learns a satisfaction estimation model using a feature quantity of each speech extracted from the conversation voice, the correct answer value of the speech satisfaction, and the correct answer value of the conversation satisfaction, the satisfaction estimation model configured by connecting a speech satisfaction estimation model part that receives a feature quantity of each speech and estimates the speech satisfaction of each speech with a conversation satisfaction estimation model part that receives at least the speech satisfaction of each speech and estimates the conversation satisfaction.

Estimation accuracies of a conversation satisfaction and a speech satisfaction are improved. A learning data storage unit (10) stores learning data including a conversation voice containing a conversation including a plurality of speeches, a correct answer value of a conversation satisfaction for the conversation, and a correct answer value of a speech satisfaction for each speech included in the conversation. A model learning unit (13) learns a satisfaction estimation model using a feature quantity of each speech extracted from the conversation voice, the correct answer value of the speech satisfaction, and the correct answer value of the conversation satisfaction, the satisfaction estimation model configured by connecting a speech satisfaction estimation model part that receives a feature quantity of each speech and estimates the speech satisfaction of each speech with a conversation satisfaction estimation model part that receives at least the speech satisfaction of each speech and estimates the conversation satisfaction.

A system and method for providing avatar device status indicators for voice assistants in multi-zone vehicles. The method comprises: receiving at least one signal from a plurality of microphones, wherein each microphone is associated with one of a plurality of spatial zones, and one of a plurality of avatar devices; wherein the at least one signal further comprises a speech signal component from a speaker; wherein the speech signal component is a voice command or question; sending zone information associated with the speaker and with one of the plurality of spatial zones to an avatar; activating one the plurality of avatar devices in a respective one of the plurality of spatial zones associated with the speaker.

A system and method for providing avatar device status indicators for voice assistants in multi-zone vehicles. The method comprises: receiving at least one signal from a plurality of microphones, wherein each microphone is associated with one of a plurality of spatial zones, and one of a plurality of avatar devices; wherein the at least one signal further comprises a speech signal component from a speaker; wherein the speech signal component is a voice command or question; sending zone information associated with the speaker and with one of the plurality of spatial zones to an avatar; activating one the plurality of avatar devices in a respective one of the plurality of spatial zones associated with the speaker.

Certain aspects of the disclosure are directed to monitoring user-data communications corresponding to a user-generated message. According to a specific example, user-data communications, which are addressed to a client among a plurality of remotely-situated client entities, are directed to a message recording system. Each of the plurality of remotely-situated client entities are respectively configured and arranged to interface with a data communications server providing data communications services on a subscription basis. During recording of a message associated with the user-data communications and on the message recording system, speech characteristic parameters of the message may be analyzed, and a sentiment score and a criticality score for the message, may be determined. During the recording of the message, the user-data communications may be routed based on the determined sentiment score and criticality score.

Certain aspects of the disclosure are directed to monitoring user-data communications corresponding to a user-generated message. According to a specific example, user-data communications, which are addressed to a client among a plurality of remotely-situated client entities, are directed to a message recording system. Each of the plurality of remotely-situated client entities are respectively configured and arranged to interface with a data communications server providing data communications services on a subscription basis. During recording of a message associated with the user-data communications and on the message recording system, speech characteristic parameters of the message may be analyzed, and a sentiment score and a criticality score for the message, may be determined. During the recording of the message, the user-data communications may be routed based on the determined sentiment score and criticality score.

Authentication is performed based on a user narrative. A narrative, such as a personal story, can be requested during a setup process. Content, voice signature, and emotion can be determined or inferred from analyzing the narrative. Subsequently, a user can provide vocal input associated with the narrative, such as by retelling the narrative or answering questions regarding the narrative. The vocal input can be analyzed for content, voice signature and emotion, and compared with the initial narrative. An authentication score can then generated based on the comparison.