Abstract: The A pattern recognition apparatus using domain adaptation 10 comprises an estimation unit 11. The estimation unit 11 estimates PLDA (Probabilistic Linear Discriminant Analysis) parameters and transformation parameters from features of a first domain data and a second domain data so as to maximize/minimize an objective function with respect to the features.

Abstract: The accuracy of ear authentication is ensured over a range from an audible range to a non-audible range. An earphone includes a sound emitting unit, a sound collecting unit, a housing containing the sound emitting unit and the sound collecting unit, a sound hole formed inside the housing, the sound hole being configured to propagate an emitted sound to a predetermined direction and propagate a sound coming from the predetermined direction to the sound collecting unit, and an ear pad covering at least a part of the sound hole, in which an ear-pad side end face is configured so as not to project beyond a sound-hole side end face at least in the predetermined direction, the ear-pad side end face being an end face of the ear pad, the sound-hole side end face being an end face of the sound hole in the predetermined direction.

Abstract: Please delete the Abstract of the Disclosure, and replace it with the following: Provided is a parameter calculation device or the like that calculates a parameter with which it is possible to produce a model that is a basis for correctly classifying data. A parameter calculation device calculates a value following a predetermined distribution for relevance information and generates a class vector including the calculated value. The relevance information represents a relevance among data, the value following the predetermined distribution, an between-class scatter degree of the data, and a within-class scatter degree of the data. The data is classified into the class.

Abstract: A speech processing apparatus, method and non-transitory computer-readable storage medium are disclosed. A speech processing apparatus may include a memory storing instructions, and at least one processor configured to process the instructions to calculate an acoustic diversity degree value representing a degree of variation in types of sounds included in a speech signal representing a speech, on a basis of the speech signal, and compensate for a recognition feature value calculated to recognize specific attribute information from the speech signal, using the acoustic diversity degree value.

Abstract: A pattern recognition apparatus for discriminative training includes: a similarity calculator that calculates similarities among training data; a statistics calculator that calculates statistics from the similarities in accordance with current labels for the training data; and a discriminative probabilistic linear discriminant analysis (PLDA) trainer that receives the training data, the statistics of the training data, the current labels and PLDA parameters, and updates the PLDA parameters and the labels of the training data.

Abstract: A speech processing device includes at least one memory configured to store instructions and at least one processor configured to execute the instructions to: store one or more acoustic models; calculate an acoustic feature from a received speech signal, and by using the acoustic feature calculated and the acoustic model stored, calculate an acoustic diversity that is a vector representing a degree of variations of types of sounds; by using the calculated acoustic diversity and a selection coefficient, calculate a weighted acoustic diversity, and by using the weighted acoustic diversity calculated and the acoustic feature, calculate a recognition feature for recognizing identity of a speaker that concerns the speech signal; and calculate a feature vector by using the recognition feature calculated.

Abstract: Provided is a personal authentication device capable of simply securing security with little psychological and physical burden of a user to be authenticated.

Abstract: A pattern recognition apparatus according to the present invention includes: a memory; and at least one processor coupled to the memory, the processor performing operations, the operations comprising: transforming noisy feature vectors into denoised feature vectors; classifying the denoised feature vectors into related classes and estimated classes; calculating a cost by using the denoised feature vectors, clean feature vectors, the estimated classes, and feature vector labels; and updating parameters for transforming the noisy feature vectors according to the cost.

Abstract: To provide a personal authentication device capable of simply securing security with little psychological and physical burden of a user to be authenticated.

Abstract: Provided is to prevent a false determination due to an attachment condition of an apparatus that transmits and receives an acoustic signal, and perform accurate personal authentication. A personal authentication device includes: a personal authentication means that authenticates an individual by using first information at least including an acoustic characteristic calculated from an acoustic signal propagating through the head of the user, which is detected by an apparatus being attached on a head of a user for transmitting and receiving the acoustic signal, and a feature amount extracted from the acoustic characteristic; an attachment trouble rule storage means that stores an attachment trouble rule for detecting an attachment trouble with the apparatus; and an attachment trouble detection means that detects a trouble with an attachment state of the apparatus when the first information satisfies the attachment trouble rule.

Abstract: A speaker identification device includes: a primary speaker identification unit that computes, for each pre-stored registered speaker, a score that indicates the similarity between input speech and speech of the registered speakers; a similar speaker selection unit that selects a plurality of the registered speakers as similar speakers according to the height of the scores thereof; a learning unit that creates a classifier for each similar speaker by sorting the speech of a certain similar speaker among the similar speakers as a positive instance and the speech of the other similar speakers as negative instances; and a secondary speaker identification unit that computes, for each classifier, a score of the classifier with respect to the input speech, and outputs an identification result.

Abstract: A personal authentication device includes: acoustic signal transmission means 701 for transmitting a first acoustic signal to a part of a head of a user; acoustic signal observation means 702 for observing a second acoustic signal which is an acoustic signal after the first acoustic signal propagates through the part of the head; acoustic property calculation means 703 for calculating an acoustic property from the first acoustic signal and the second acoustic signal; and user identification means 704 for identifying the user, based on the acoustic property or a feature value extracted from the acoustic property and relating to the user.

Abstract: The A pattern recognition apparatus using domain adaptation 10 comprises an estimation unit 11. The estimation unit 11 estimates PLDA (Probabilistic Linear Discriminant Analysis) parameters and transformation parameters from features of a first domain data and a second domain data so as to maximize/minimize an objective function with respect to the features.

Abstract: A monitoring device includes a crowd behavior analysis unit 21 and an abnormality degree calculation unit 24. The crowd behavior analysis unit 21 specifies a behavior pattern of a crowd from input video. The abnormality degree calculation unit 24 calculates an abnormality degree from a change of the behavior pattern.

Abstract: An action analysis device includes: an acoustic analysis unit 1 for analyzing input acoustic information, and generating acoustic analysis information indicating a feature of the acoustic information; a time difference determination unit 2 for determining a time difference between when an acoustic event identified by the acoustic analysis information occurs and when an event corresponding to the acoustic event occurs in input video obtained by capturing an image of a crowd; and an action analysis unit 3 for analyzing an action of the crowd corresponding to the acoustic event, using the input video, the acoustic analysis information, and the time difference.

Abstract: A speech processing apparatus, method and non-transitory computer-readable storage medium are disclosed. A speech processing apparatus may include a memory storing instructions, and at least one processor configured to process the instructions to calculate an acoustic diversity degree value representing a degree of variation in types of sounds included in a speech signal representing a speech, on a basis of the speech signal, and compensate for a recognition feature value calculated to recognize specific attribute information from the speech signal, using the acoustic diversity degree value.

Abstract: An action analysis device includes: an acoustic analysis unit 1 for analyzing input acoustic information, and generating acoustic analysis information indicating a feature of the acoustic information; a time difference determination unit 2 for determining a time difference between when an acoustic event identified by the acoustic analysis information occurs and when an event corresponding to the acoustic event occurs in input video obtained by capturing an image of a crowd; and an action analysis unit 3 for analyzing an action of the crowd corresponding to the acoustic event, using the input video, the acoustic analysis information, and the time difference.

Abstract: A monitoring device includes a crowd behavior analysis unit 21 and an abnormality degree calculation unit 24. The crowd behavior analysis unit 21 specifies a behavior pattern of a crowd from input video. The abnormality degree calculation unit 24 calculates an abnormality degree from a change of the behavior pattern.

Abstract: The present invention can increase the types of noises that can be dealt with enough to enable speech recognition with a speech recognition rate of high accuracy.

Abstract: A speaker identification device includes: a primary speaker identification unit that computes, for each pre-stored registered speaker, a score that indicates the similarity between input speech and speech of the registered speakers; a similar speaker selection unit that selects a plurality of the registered speakers as similar speakers according to the height of the scores thereof; a learning unit that creates a classifier for each similar speaker by sorting the speech of a certain similar speaker among the similar speakers as a positive instance and the speech of the other similar speakers as negative instances; and a secondary speaker identification unit that computes, for each classifier, a score of the classifier with respect to the input speech, and outputs an identification result.