Abstract: A learning device collects a moving image with a voice from the Web, and extracts a series of face images and a voice of a person from the collected moving image. In addition, the learning device estimates an age of the person in the series of extracted face images by using a first NN (“neural network”) that estimates an age of a person in face images. Further, the learning device estimates an age of the person of the extracted voice by using a second NN that estimates an age of a person using a voice. Next, the learning device updates each parameter of the first NN or the second NN such that a difference between the age of the person estimated by the first NN and the age of the person estimated by the second NN is decreased. The learning device performs learning by repeatedly executing the processing.

Abstract: Provided is a model learning technology to learn a model in consideration of a difference in label assignment accuracy between experts and non-experts.

Abstract: The present invention provides emotion recognition technology that achieves high emotion recognition accuracy for all speakers.

Abstract: A recognition apparatus includes a classification unit that estimates a non-linguistic and para-linguistic information label to be imparted by an n-th listener from an acoustic feature amount of speech data to be recognized using an n-th classification model, and an integration unit that integrates estimation results of the non-linguistic and para-linguistic information labels for N listeners and obtains non-linguistic and para-linguistic information estimation results as a recognition apparatus for the speech data to be recognized, and the n-th classification model is a classification model trained using training speech data and a non-linguistic and para-linguistic information label imparted to the training speech data by the n-th listener as training data.

Abstract: A learning apparatus includes: a speaker vector learning unit configured to learn a speaker vector extraction parameter ? based on one or more items of learning speech voice data in a speaker vector voice database; a non-speaker-individuality sound model learning unit configured to create a probability distribution model using a frequency component of one or more items of non-speaker-individuality sound data in a non-speaker-individuality sound database and calculate an internal parameter of the probability distribution model; and an age level estimation model learning unit configured to extract a speaker vector from voice data in an age level estimation model-learning voice database using the speaker vector extraction parameter ?, calculate a non-speaker-individuality sound likelihood vector from voice data in the age level estimation model-learning voice database using the internal parameters ? and ?, and learn, with input of the speaker vector and the non-speaker-individuality sound likelihood vector, a pa