Training device, estimation device, training method, estimation method, training program, and estimation program
AssigneeNTT, Inc.
InventorsAkira Morikawa, Ryo Ishii, Hajime Noto
Life of this patent
Estimating the term…
Abstract
A learning device includes processing circuitry configured to acquire speech data of a speaker, information on the speaker, conversation data of a listener, information on the listener, and a classification label of a quick response included in the conversation data of the listener, and create a learned model of estimating a type of the quick response of the listener to a conversation of the speaker using the information acquired with the classification label of the quick response as correct answer data.
Claims6
A learning device comprising:
- processing circuitry configured to:
- obtain first multimodal data of utterances, wherein the first multimodal data includes one or more expressions, actions, and voices of speakers;
- obtain second multimodal data of backchannel responses, wherein the second multimodal data includes one or more expressions, actions, and voices of listeners;
- obtain classification data including classification labels associated with the backchannel responses, wherein each classification label indicates one of (i) a positive response, (ii) a neutral response, (iii) a non-positive response, (iv) an emotional word, (v) a confirmation, (vi) a repetition of a speaker's utterance, (vii) a response providing a topic, and (viii) a summarization or paraphrase of a speaker's utterance;
- and
- train, using the first multimodal data, the second multimodal data, and the classification data, a model, wherein the first and second multimodal data are used as training input, and the classification data is used as training output.
A learning method performed by a learning device, the learning method comprising:
- obtaining first multimodal data of utterances, wherein the first multimodal data includes one or more expressions, actions, and voices of speakers;
- obtaining second multimodal data of backchannel responses, wherein the second multimodal data includes one or more expressions, actions, and voices of listeners;
- obtaining classification data including classification labels associated with the backchannel responses, wherein each classification label indicates one of (i) a positive response, (ii) a neutral response, (iii) a non-positive response, (iv) an emotional word, (v) a confirmation, (vi) a repetition of a speaker's utterance, (vii) a response providing a topic, and (viii) a summarization or paraphrase of a speaker's utterance;
- and
- training, using the first multimodal data, the second multimodal data, and the classification data, a model, wherein the first and second multimodal data are used as training input, and the classification data is used as training output.
A non-transitory computer-readable recording medium storing therein a learning program that causes a computer to execute a process comprising:
- obtaining first multimodal data of utterances, wherein the first multimodal data includes one or more expressions, actions, and voices of speakers;
- obtaining second multimodal data of backchannel responses, wherein the second multimodal data includes one or more expressions, actions, and voices of listeners;
- obtaining classification data including classification labels associated with the backchannel responses, wherein each classification label indicates one of (i) a positive response, (ii) a neutral response, (iii) a non-positive response, (iv) an emotional word, (v) a confirmation, (vi) a repetition of a speaker's utterance, (vii) a response providing a topic, and (viii) a summarization or paraphrase of a speaker's utterance;
- and
- training, using the first multimodal data, the second multimodal data, and the classification data, a model, wherein the first and second multimodal data are used as training input, and the classification data is used as training output.
Dependencies are inferred from the claim text.