According to a first aspect of the present disclosure, a method for facilitating the detection of one or more time series patterns is conceived, comprising building one or more artificial neural networks, wherein, for at least one time series pattern to be detected, a specific one of said artificial neural networks is built. According to a second aspect of the present disclosure, a corresponding computer program is provided. According to a third aspect of the present disclosure, a non-transitory computer-readable medium is provided that comprises a computer program of the kind set forth. According to a fourth aspect of the present disclosure, a corresponding system for facilitating the detection of one or more time series patterns is provided.

According to a first aspect of the present disclosure, a method for facilitating the detection of one or more time series patterns is conceived, comprising building one or more artificial neural networks, wherein, for at least one time series pattern to be detected, a specific one of said artificial neural networks is built. According to a second aspect of the present disclosure, a corresponding computer program is provided. According to a third aspect of the present disclosure, a non-transitory computer-readable medium is provided that comprises a computer program of the kind set forth. According to a fourth aspect of the present disclosure, a corresponding system for facilitating the detection of one or more time series patterns is provided.

An audio data processing method is provided. The method includes: obtaining multi-path audio data in an environmental space, obtaining a speech data set based on the multi-path audio data, and separately generating, in a plurality of enhancement directions, enhanced speech information corresponding to the speech data set; matching a speech hidden feature in the enhanced speech information with a target matching word, and determining an enhancement direction corresponding to the enhanced speech information having a highest degree of matching with the target matching word as a target audio direction; obtaining speech spectrum features in the enhanced speech information, and obtaining, from the speech spectrum features, a speech spectrum feature in the target audio direction; and performing speech authentication on the speech hidden feature and the speech spectrum feature that are in the target audio direction based on the target matching word, to obtain a target authentication result.

An audio data processing method is provided. The method includes: obtaining multi-path audio data in an environmental space, obtaining a speech data set based on the multi-path audio data, and separately generating, in a plurality of enhancement directions, enhanced speech information corresponding to the speech data set; matching a speech hidden feature in the enhanced speech information with a target matching word, and determining an enhancement direction corresponding to the enhanced speech information having a highest degree of matching with the target matching word as a target audio direction; obtaining speech spectrum features in the enhanced speech information, and obtaining, from the speech spectrum features, a speech spectrum feature in the target audio direction; and performing speech authentication on the speech hidden feature and the speech spectrum feature that are in the target audio direction based on the target matching word, to obtain a target authentication result.

An account adding method is performed by a social networking application running at a mobile terminal when communicating with a second terminal (e.g., using a chat session). The method includes: recording voice information from the second terminal using the social networking application; extracting character string information and voiceprint information from the voice information; sending the character string information and the voiceprint information to a server; receiving an account that matches the character string information and the voiceprint information and that is sent by the server; and adding the account to a contact list of the social networking application. For example, the social networking application is started before starting a telephone call with the second terminal and the voice information is recorded during the telephone call.

A method of modelling speech of a user of a headset comprising a microphone, the method comprising: receiving a first sample, from a bone-conduction sensor, representing bone-conducted speech of the user; obtaining a measure of fundamental frequency of the bone-conducted speech in each of a plurality of speech frames of the first sample; obtaining a first distribution of the fundamental frequencies of the bone-conducted speech over the plurality of speech frames; receiving, from the microphone, a second sample; determining a first acoustic condition at the headset based on the second signal; performing a biometric process based on the first distribution of fundamental frequencies and the first acoustic condition.

The present application relates to an extended reality service providing method and system for operation of an industrial installation. More specifically, various types of data required for operation (e.g., inspection, examination, maintenance, repair, and reinforcement) of an industrial installation are digitalized, extended reality content, such as an augmented reality image or a mixed reality image based on the digitalized data, is provided to a site worker or a remote place manager, and the worker and the manager can communicate via a video call in real-time, whereby the work efficiency of the worker and the manager can be enhanced.

For example, the accuracy of voice recognition is improved.

A signal processing device includes: a single speech detection unit that detects whether one channel of an input voice signal is a speech of a single speaker; a cluster information updating unit that updates cluster information based on a voice feature quantity when the input voice signal is a speech of a single speaker; a voice segment detection unit that detects a speech segment of a target speaker based on the cluster information; and a voice extraction unit that extracts only the voice signal of the target speaker from a mixed voice signal containing the voice of the target speaker.

For example, the accuracy of voice recognition is improved.

A signal processing device includes: a single speech detection unit that detects whether one channel of an input voice signal is a speech of a single speaker; a cluster information updating unit that updates cluster information based on a voice feature quantity when the input voice signal is a speech of a single speaker; a voice segment detection unit that detects a speech segment of a target speaker based on the cluster information; and a voice extraction unit that extracts only the voice signal of the target speaker from a mixed voice signal containing the voice of the target speaker.

A device may receive audio data identifying a plurality of speakers and may process the audio data, with a plurality of clustering models, to identify a plurality of speaker segments. The device may determine a plurality of diarization error rates for the plurality of speaker segments and may identify a plurality of errors in the plurality of speaker segments. The device may select rectification models to rectify the plurality of errors and may segment and/or re-segment the audio data with the rectification models to generate re-segmented audio data. The device may determine a plurality of modified diarization error rates for the plurality of speaker segments based on the re-segmented audio data and may select one of the plurality of speaker segments based on the plurality of modified diarization error rates. The device may calculate an empathy score based on the selected speaker segment and may perform actions based on the empathy score.