MODEL LEARNING APPARATUS, SATISFACTION ESTIMATION APPARATUS, MODEL LEARNING METHOD, SATISFACTION ESTIMATION METHOD, AND PROGRAM
A model learning apparatus includes an utterance feature reconstruction model learning unit configured to train an utterance feature reconstruction model that is a neural network model that randomly selects some of utterance feature sequences that are sequences of utterance features corresponding to respective utterances of a target speaker and replaces the selected utterance feature sequences with predetermined masking information to mask the utterance feature sequences, and estimates utterance features of the masked utterance feature sequences, and output the trained utterance feature reconstruction model as an unsupervised pre-trained model.
1 . A model learning apparatus comprising:
processing circuitry configured to
train an utterance feature reconstruction model that is a neural network model that randomly selects some of utterance feature sequences that are sequences of utterance features corresponding to respective utterances of a target speaker and replace the selected utterance feature sequences with predetermined masking information to mask the utterance feature sequences, and estimate utterance features of the masked utterance feature sequences; and
output the trained utterance feature reconstruction model as an unsupervised pre-trained model.
2 . The model learning apparatus according to claim 1 ,
the processing circuitry configured to
perform supervised learning on a satisfaction level estimation model that is a model for estimating an utterance satisfaction level and a conversation satisfaction level by using parameters of the utterance feature reconstruction model as initial values of model parameters and using utterance feature sequences and corresponding utterance satisfaction level labels and conversation satisfaction level labels as learning data.
3 . The model learning apparatus according to claim 1 , wherein the utterance features are any of prosody features, conversation features, and linguistic features.
4 . A satisfaction estimation apparatus comprising
processing circuitry configured to
estimate an utterance satisfaction level and a conversation satisfaction level corresponding to an utterance of a target speaker on the basis of a satisfaction level estimation model trained by using, as initial values of model parameters, parameters of an utterance feature reconstruction model that is a neural network model that randomly selects some of utterance feature sequences that are sequences of utterance features corresponding to respective utterances of a target speaker and replace the selected utterance feature sequences with predetermined masking information to mask the utterance feature sequences, and estimate utterance features of the masked utterance feature sequences, and using utterance feature sequences and corresponding utterance satisfaction level labels and conversation satisfaction level labels as learning data.
5 . A model learning method executed by a model learning apparatus, the model learning method comprising a step of learning an utterance feature reconstruction model that is a neural network model that randomly selects some of utterance feature sequences that are sequences of utterance features corresponding to respective utterances of a target speaker and replaces the selected utterance feature sequences with predetermined masking information to mask the utterance feature sequences, and estimates utterance features of the masked utterance feature sequences, and outputting the trained utterance feature reconstruction model as an unsupervised pre-trained model.
6 . A satisfaction estimation method executed by a satisfaction estimation apparatus, the satisfaction estimation method comprising a step of estimating an utterance satisfaction level and a conversation satisfaction level corresponding to an utterance of a target speaker on the basis of a satisfaction level estimation model trained by using, as initial values of model parameters, parameters of an utterance feature reconstruction model that is a neural network model that randomly selects some of utterance feature sequences that are sequences of utterance features corresponding to respective utterances of a target speaker and replaces the selected utterance feature sequences with predetermined masking information to mask the utterance feature sequences, and estimates utterance features of the masked utterance feature sequences, and using utterance feature sequences and corresponding utterance satisfaction level labels and conversation satisfaction level labels as learning data.
7 . A non-transitory computer readable medium storing a computer program for causing a computer to function as the model learning apparatus according to claim 1 .