Abstract: Embodiments of the disclosure provide a method for detecting an inter-person conflict. The method includes receiving at least one image from an image data resource, where the at least one image is captured by at least one camera. The method further includes detecting human objects from the at least one image. The method additionally includes determining a distance between two of the detected human objects. The method additionally includes determining whether there is a conflict between the two of the detected human objects based on the determined distance.

Abstract: Embodiments of the disclosure provide a method for detecting an inter-person conflict. The method includes receiving at least one image from an image data resource, where the at least one image is captured by at least one camera. The method further includes detecting human objects from the at least one image. The method additionally includes determining a distance between two of the detected human objects. The method additionally includes determining whether there is a conflict between the two of the detected human objects based on the determined distance.

Abstract: The present disclosure relates to a system and a method for distinguishing a driver and passengers in an image captured inside a vehicle. The system may perform the method to: obtain a face set from the image, the face set including at least one human face inside the vehicle and coordinates of the at least one human face; obtain a candidate driver face set from the face set based on the coordinates of the at least one human face, the candidate driver face set including at least one candidate driver face and coordinates of the at least one candidate driver face; and identify a target driver face from the at least one candidate driver face based on the coordinates of the at least one candidate driver face.

Abstract: A system may determine first groups of image data from multiple groups, obtain a first identification model based on the first groups of image data, and classify the first groups of image data to generate a first classification result based on the first identification model in which the first groups of image data may be classified into a qualified dataset and an unqualified dataset. The system may obtain an initial second identification model with a second accuracy threshold and perform one or more iterations. In each of one or more iterations, the system may classify the unqualified dataset to generate a second classification result, update the qualified dataset and the unqualified dataset, and update, based on the updated qualified dataset, the second identification model. The system may further determine the cleaned dataset based on the updated qualified dataset.

Abstract: Embodiments of the disclosure provide a system for detecting a conflict in a vehicle. The system includes at least one camera, which is configured to capture a plurality of images in the vehicle. The system further includes a controller in communication with the at least one camera. The controller is configured to detect human objects from the plurality of images, estimate depth information of the respective human objects, and detect the conflict based on the depth information.

Abstract: Embodiments of the disclosure provide a system for detecting a conflict in a vehicle. The system includes at least one camera, which is configured to capture a plurality of images in the vehicle. The system further includes a controller in communication with the at least one camera. The controller is configured to detect human objects from the plurality of images, estimate depth information of the respective human objects, and detect the conflict based on the depth information.

Abstract: A modeling device comprises a front end which receives enrollment speech data from each target speaker, a reference anchor set generation unit which generates a reference anchor set using the enrollment speech data based on an anchor space, and a voice print generation unit which generates voice prints based on the reference anchor set and the enrollment speech data. By taking the enrollment speech and speaker adaptation technique into account, anchor models with a smaller size can be generated, so reliable and robust speaker recognition with a smaller size reference anchor set is possible.

Abstract: A converting circuit and a communication device are provided. The converting circuit includes: a sample hold circuit for receiving an analog signal; a Digital to Analog Converter (DAC); a comparator being connected with an output end of the sample hold circuit and an output end of the DAC; and a control circuit being connected with an output end of the comparator, wherein when the sample hold circuit receives an analog signal, the control circuit controls the sample hold circuit and the comparator to work, controls an output of the DAC based on an output of the comparator, and outputs a corresponding digital signal; and when the control circuit detects a digital signal is input, the control circuit controls the DAC to convert the digital signal into an corresponding analog signal and output the corresponding analog signal. The converting circuit can reduce chip area and chip cost.

Abstract: A device and method for pass-phrase modeling for speaker verification and a speaker verification system are provided. The device comprises a front end which receives enrollment speech from a target speaker, and a template generation unit which generates a pass-phrase template with a general speaker model based on the enrollment speech. With the device, method and system of the present disclosure, by taking the rich variations contained in a general speaker model into account, the robust pass-phrase modeling is ensured even the enrollment data is insufficient, even just one pass-phrase is available from a target speaker.

Abstract: A converting circuit and a communication device are provided. The converting circuit includes: a sample hold circuit for receiving an analog signal; a Digital to Analog Converter (DAC); a comparator being connected with an output end of the sample hold circuit and an output end of the DAC; and a control circuit being connected with an output end of the comparator, wherein when the sample hold circuit receives an analog signal, the control circuit controls the sample hold circuit and the comparator to work, controls an output of the DAC based on an output of the comparator, and outputs a corresponding digital signal; and when the control circuit detects a digital signal is input, the control circuit controls the DAC to convert the digital signal into an corresponding analog signal and output the corresponding analog signal. The converting circuit can reduce chip area and chip cost.

Abstract: One aspect of the embodiments discloses a model generation device for pattern recognition, a pattern recognition apparatus and methods thereof. A mixture-level variance sharing step generates a mixture-level variance sharing structure of a first model by using a second model. A first model generation step generates the first model with the variance sharing structure by using training data of the first model, wherein in the variance sharing structure, mixture components in respective states have the same shared variances in the same order. The embodiment can at least provide better model parameter estimation so as to provide better recognition performance in the case of limited training data.

Abstract: A modeling device and method for speaker recognition and a speaker recognition system are provided. The modeling device comprises a front end which receives enrollment speech data from each target speaker, a reference anchor set generation unit which generates a reference anchor set using the enrollment speech data based on an anchor space, and a voice print generation unit which generates voice prints based on the reference anchor set and the enrollment speech data. With the present disclosure, by taking the enrollment speech and speaker adaptation technique into account, anchor models with smaller size can be generated, so reliable and robust speaker recognition with smaller size reference anchor set is possible. It brings great advantages for computation speed improvement and great memory reduction.

Abstract: A device and method for pass-phrase modeling for speaker verification and a speaker verification system are provided. The device comprises a front end which receives enrollment speech from a target speaker, and a template generation unit which generates a pass-phrase template with a general speaker model based on the enrollment speech. With the device, method and system of the present disclosure, by taking the rich variations contained in a general speaker model into account, the robust pass-phrase modeling is ensured even the enrollment data is insufficient, even just one pass-phrase is available from a target speaker.

Abstract: The present invention provides a device that performs online self-adaption of anchor models for an acoustic space, and a method thereof, the anchor models being used for categorization of an AV stream which is performed based on an audio stream in the AV stream. The device divides an input audio stream into audio segments, each being estimated to have a single acoustic feature, and estimates a single probability model for each audio segment. Then, the device performs clustering on the estimated probability models and probability models stored therein, thereby generating a new anchor model.