18 results about "Voice activity" patented technology

A method for audio processing performed by an electronic device is provided. The method includes detecting at least one of a first voice activity near a first device and a second voice activity near a second device using a voice recognition module associated with the first device, comparing the first voice activity and the second voice activity to determine whether the first voice activity and the second voice activity exceed a predetermined threshold, and outputting the at least one of the first voice activity or the second voice activity through the second device in response to determining that the first voice activity and the second voice activity exceed the predetermined threshold.

This invention discloses a safe driving voice and visual interaction control method. The method includes: synchronously collecting and preprocessing the driver's visual data and voice interaction data; extracting key visual features based on the preprocessed visual data and calculating visual state feature values; triggering standardized voice interaction based on the visual state feature values, calculating a voice activity score in conjunction with the preprocessed voice interaction data, and determining the driver's voice response delay level based on the voice activity score; matching based on a predefined 3D virtual guide action sequence according to the voice response delay level, triggering a linkage response after matching to form a non-intrusive driving reminder with light, sound, and shape linkage; and completing closed-loop feedback control based on the execution state of the 3D virtual guide action sequence and the non-intrusive driving reminder with light, sound, and shape linkage. The method provided by this invention can reduce the monitoring misjudgment rate and achieve non-intrusive safety reminders.

Audio systems, methods, and processor instructions are provided herein that detect voice activity of a user and provide an output voice signal. The systems, methods, and instructions receive a plurality of microphone signals and combine the plurality of microphone signals according to a first combination and a second combination. The first combination produces a primary signal having an enhanced response in a direction of a mouth of the user, and the second combination produces a reference signal having a reduced response in the direction of the mouth of the user. The primary signal and the reference signal are added and subtracted to produce a sum signal and a difference signal, respectively. The sum signal is compared to the difference signal and an output voice signal is provided based on the comparison.

A method for audio processing performed by an electronic device is provided. The method includes detecting at least one of a first voice activity near a first device and a second voice activity near a second device using a voice recognition module associated with the first device, comparing the first voice activity and the second voice activity to determine whether the first voice activity and the second voice activity exceed a predetermined threshold, and outputting the at least one of the first voice activity or the second voice activity through the second device in response to determining that the first voice activity and the second voice activity exceed the predetermined threshold.

The present document describes a method (600) for encoding a multi-channel input signal (101) which comprises N different channels. The method (600) comprises, for a current frame of a sequence of frames, determining (601) whether the current frame is an active frame or an inactive frame using a signal and / or a voice activity detector, and determining (602) a downmix signal (103) based on the multi-channel input signal (101), wherein the downmix signal (103) comprises N channels or less. In addition, the method (600) comprises determining (603) upmixing metadata (105) comprising a set of parameters for generating, based on the downmix signal (103), a reconstructed multi-channel signal (111) comprising N channels, wherein the upmixing metadata (105) is determined in dependance of whether the current frame is an active frame or an inactive frame. The method (600) further comprises encoding (604) the upmixing metadata (105) into a bitstream.

A method for detecting activity of the own voice of a wearer of a hearing device by way of a signal processing apparatus of the hearing device. A first input signal is generated by a first input transducer, and a second input signal is generated by a second input transducer. The two input signals are supplied to a detection unit of the signal processing apparatus, which has a neural network and an input stage, which is connected in front of the neural network. Information signals are generated by the input stage on the basis of the two input signals and the information signals are evaluated by the neural network. A detection result is output by the detection unit based on the evaluation of the information signals by the neural network.

Exemplary system and methods use a combination of application modules and neural network architecture for multi-speaker and multi-language speech analysis. The exemplary system can receive a natural language input, which it decomposes into plural segments. A sub-group of the plural segments are accumulated in a buffer where each segment representing a period during which voice activity is detected. The sub-groups are analyzed for voice activity of multiple speakers and one or more text segments are generated based on the speakers. A semantic vector for each text segment is generated and stored in vector memory. Relevant data associated with each semantic vector is retrieved from the vector memory based on a similarity measure; and a response including specified information extracted from the one or more text segments is generated based on at least the relevant data.

The specification provides a speech enhancement method based on a dual microphone array, the method comprising: acquiring surrounding acoustic signals collected by two microphones in a microphone array respectively. Based on the acoustic signals collected by the two microphones respectively, coherence information between the two microphones is determined. Based on the coherence information, a speech activity state is judged, and in the case that the speech activity state is a speech missing state, a noise covariance matrix of the spatial filter is updated; and a gain function value of the post-filter is determined based on the coherence information. The two collected acoustic signals are subjected to beamforming processing by the spatial filter to obtain acoustic signals of at least one beam direction. The acoustic signals of any beam direction are subjected to filtering processing by the post-filter to obtain an estimation result of a speech component contained in the acoustic signals of the any beam direction.

This invention discloses a method and system for real-time accompaniment and structured memory generation based on scene perception, relating to the fields of artificial intelligence, augmented reality, and mobile computing. The system includes core modules such as an audio and video acquisition module, a scene intent recognition and scheduling module, a sensor parameter dynamic configuration module, a real-time dialogue perception engine, and a structured information extraction module. This invention achieves dynamic optimization of hardware parameters driven by scene semantics, balancing perception accuracy and device power consumption. It realizes speaker recognition without training through a large language model, and adopts a local priority architecture to protect data privacy. It can achieve seamless continuous accompaniment after a single trigger, completing real-time understanding of dialogue content, multi-dimensional structured information extraction, and local memory generation. At the same time, through a voice activity adaptive acquisition mechanism and a runtime voice command reconfiguration mechanism, it achieves low-power acquisition and touchless parameter adjustment, making it suitable for various scenarios such as medical consultation, business negotiation, and daily social interaction.

This invention discloses an adaptive voice control system and method for low-bandwidth and noisy environments, belonging to the field of speech recognition and intelligent control technology. It includes: a noise reduction processing module for enhancing the original audio signal; an environmental perception module for detecting speech activity and monitoring network status based on the enhanced noise-reduced signal; a collaborative control module for dynamically and collaboratively deciding on the target encoding bitrate and speech recognition model version by querying a built-in mapping table based on the perceived information; a dynamic encoding module for compressing the enhanced noise-reduced signal at the target bitrate; and a lightweight inference module for loading the model version to recognize the enhanced noise-reduced signal and generate control commands. This invention effectively solves the technical challenge of simultaneously ensuring low latency and high accuracy in voice control systems under dynamically changing low-bandwidth and high-noise environments by introducing real-time collaborative optimization of noise reduction preprocessing and encoding / recognition strategies. It is suitable for complex scenarios such as smart homes and industrial control.

This invention belongs to the field of speech recognition technology. It discloses a system and method for generating gastrointestinal endoscopy reports based on medical speech recognition. The system includes a speech acquisition and processing module for acquiring real-time spoken speech from doctors during gastrointestinal endoscopy, performing adaptive noise reduction and mask occlusion compensation on the spoken speech, and outputting a speech segment sequence; a speech activity detection module for receiving the speech segment sequence, calculating the prior probability of speech activity based on the transition relationships of different operational states in the endoscopy procedure, and performing speech activity detection on the speech segment sequence based on the prior probability of speech activity to filter out speech event sequences consistent with the examination procedure; and a speech-image alignment module for transcribing the speech event sequences into medical speech, acquiring endoscopic images at corresponding times, and completing the ambiguous spoken content in the speech event sequence based on the endoscopic images to generate speech description data. This improves the automation level of examination recording.

The invention relates to a spatial filtering system (4) comprising: - at least one first spatial filter (8), each filter being arranged to attenuate noise in a predefined direction; - a second spatial filter (9), arranged to attenuate noise in an optimized direction defined in real time; - a filter selector (10), arranged to select an optimal spatial filter; - a voice activity detector (12) arranged to detect the target voice; at least one parameter of the second spatial filter (9) being recalculated when an output signal of the voice activity detector is equal to a first value representative of an absence of the target voice.

Disclosed are an electronic device and method for controlling the same that may increase the performance of sensing and recognizing a user's voice even in ambient noise by installing an Air Conduction Microphone (ACM) and a Bone Conduction Microphone (BCM) together and selectively using at least one of the ACM and the BCM depending on a use environment, the electronic device including a user input unit, an Air Conduction Microphone (ACM), a Bone Conduction Microphone (BCM), and a controller configured to execute an application, detect a voice activity of a user based on a BCM sensing signal received from the BCM, and control a mixing signal generated by synthesizing the BCM sensing signal and an ACM sensing signal received from the ACM to be inputted to the application in response to detecting the voice activity.

This invention relates to a control device and method for activating vehicle functions by authenticating voice samples. The control device 100 detects voice commands using at least one sensor 102 installed in the vehicle. The control device 100 filters voice samples from the voice commands using a voice activity recognition module 104. The control device 100 examines the voice samples based on the recorded or imitated voice using a spoofing identification module 106, extracts a voice signature from the voice samples using a voice signature extractor 108, and authenticates the voice signature by referring to a pre-recorded voice signature from a user login process using an authenticator 110. The control device 100 extracts an entity and intent from the voice signature using an entity and intent extractor 112, and verifies the entity and intent simultaneously for multiple users by referring to pre-configured access data using a verification module 114. After verification, the control device 100 executes the intent to activate the vehicle functions.

The application discloses an overlapping speech separation method and system based on feature space orthogonal projection, a device and a medium, and relates to the technical field of speech recognition. The method comprises the following steps: real-time detection of a collected speech sequence is performed through a pre-trained segmentation model, speech active segments and non-active segments are divided, and multi-speaker overlapping speech segments are accurately located. For the overlapping segments, the identity of the main speaker is determined and the voiceprint features thereof are extracted by using context tracking information, and a known speaker feature subspace is constructed. Then, the mixed voiceprint features of the overlapping speech are extracted, the mixed voiceprint features are decomposed into a subspace parallel component and an orthogonal vertical component through matrix projection operation, so that residual features without the main speaker information are obtained, the cosine similarity with candidate speakers is calculated, the speaker with the highest similarity is selected as the secondary speaker, and the logical separation of the overlapping speech is realized. The known speaker features are eliminated through mathematical projection operation, and unknown speakers can be accurately identified in the residual space.

Exemplary system and methods use a combination of application modules and neural network architecture for multi-speaker and multi-language speech analysis. The exemplary system can receive a natural language input, which it decomposes into plural segments. A sub-group of the plural segments are accumulated in a buffer where each segment representing a period during which voice activity is detected. The sub-groups are analyzed for voice activity of multiple speakers and one or more text segments are generated based on the speakers. A semantic vector for each text segment is generated and stored in vector memory. Relevant data associated with each semantic vector is retrieved from the vector memory based on a similarity measure; and a response including specified information extracted from the one or more text segments is generated based on at least the relevant data.