A method, computer program product, and computing system for generating a generative model representative of a plurality of natural biometric profiles. A plurality of random samples are generated from the generative model. A plurality of synthetic biometric profiles are generated based upon, at least in part, the plurality of random samples.

A method, computer program product, and computing system for generating a generative model representative of a plurality of natural biometric profiles. A plurality of random samples are generated from the generative model. A plurality of synthetic biometric profiles are generated based upon, at least in part, the plurality of random samples.

There is described a switchable microphone device which may be switched between a digital output mode and an analog output mode. There is further described a system for use of such a device, which allows for the switching between analog and digital computing modes.

The disclosure provides a speech control method, an electronic device and a storage medium. The method includes: obtaining a speech to be processed; obtaining a speech feature vector by performing feature analysis on the speech to be processed; determining whether the speech to be processed belongs to a target type based on the speech feature vector; and in response to the speech to be processed belonging to the target type, performing wake-up control on a target device based on the speech to be processed.

Systems and methods for establishing user persona from audio interactions are disclosed, including a voice-based conversational AI platform having an acoustic analytical record engine and audio signal codification optimizer. The engine receives an audio sample indicative of voice conversation between an end user and a bot and transforms it into quantifiable and machine-ingestible power spectrum and acoustic indicators that uniquely represent the audio sample in the form of a feature vector. The optimizer ingests and processes the indicators to estimate likelihood of an attribute value representing the audio sample by constructing a convolutional neural network model for each attribute category. The optimizer establishes user persona attribute values across different attribute categories for the audio sample based on the estimated likelihood. Finally, a Textual Latent Value Extractor of the system determines the issue's context window and estimates the statements polarity to provide distinguishable insight in business strategy and development.

Systems and methods for identifying potential bullying are disclosed. In various aspects, a system for identifying potential bullying includes a sound detector configured to provide samples of sounds over time, a processor, and a memory storing instructions. The instructions, when executed by the processor, cause the system to determine that a noise event has occurred by processing the samples to determine that the sounds exceed a sound level threshold over a time period that exceeds a time period threshold, process the samples to provide frequency spectrum information of the noise event, determine whether the noise event is a potential bullying occurrence based on comparing the frequency spectrum information of the noise event and at least one frequency spectrum profile, and initiate a bullying notification in a case of determining that the noise event is a potential bullying occurrence.

A voice verification and restriction method of the voice terminal includes: a) voice storage step including: inputting and registering voice of a user through a microphone of the voice terminal, receiving and analyzing the input voice using a language processing module, transmitting the analyzed voice to a plurality of voice authentication servers to verify and store each voice, and learning the stored voice using an AI processor; and b) voice verification step including: mutually comparing the input voice with voice stored in at least one server among voices stored in the plurality of voice authentication servers, performing approval and a voice command when the input voice matches the stored voice, and setting restrictions on all or some of functions of the voice terminal and executing a step-by-step action designated by the user when the input voice does not match the stored voice.

A system and method for voice-based control of sexual stimulation devices. In some configurations, the system and method involve receiving voice data, analyzing the voice data to detect spoken commands, and generating control signals based on the commands. In some configurations, the system and method involve receiving voice data, analyzing the voice data for non-speech vocalizations, detecting voice stress patterns, and generating control signals based on the detected patterns. In some configurations, the analyses of the voice data are performed by machine learning algorithms which may be trained on associations between speech and non-speech vocalizations of a user while the user engages in one or more voice-based training tasks, associating speech and non-speech vocalizations with controls of the sexual stimulation device. In some configurations, machine learning algorithms are used to make the associations. In some configurations, data from other biometric sensors is included in the associations.

An estimation apparatus clusters a group of voice signals including a voice signal having a speaker attribute to be estimated into a plurality of clusters. Subsequently, the estimation apparatus identifies, from the plurality of clusters, a duster to which the voice signal to be estimated belongs. Next, the estimation apparatus uses a speaker attribute estimation model to estimate speaker attributes of respective voice signals in the identified cluster. After that, the estimation apparatus estimates an attribute of the entire cluster, by using an estimation result of the speaker attributes of the voice signals in the identified cluster, and outputs an estimation result of the speaker attribute of the entire cluster, as an estimation result of the speaker attribute of the voice signal to be estimated.

A spoofing detection apparatus 100 includes a multi-channel spectrogram creation unit 10 and an evaluation unit 40. The multi-channel spectrogram creation unit 10 extracts different type of spectrograms from speech data and integrates the different type of spectrograms to create a multi-channel spectrogram. The evaluation unit 40 evaluates the created multi-channel spectrogram by applying the created multi-channel spectrogram to a classifier constructed using labeled multi-channel spectrograms as training data and classifies it to either genuine or spoof.