A voice control method and apparatus, a chip, earphones, and a system. The method includes: recognizing (001) whether a voice signal includes a keyword; in response to the voice signal including the keyword, executing (001a) an instruction corresponding to the keyword or sending the instruction; before recognizing whether the voice signal includes the keyword, determining (002) whether the voice signal is from a target user and, in response to the voice signal being from the target user, starting to recognize (001) whether the voice signal includes the keyword; or during recognizing whether the voice signal includes the keyword, determining (002) whether the voice signal is from the target user and, in response to the voice signal being from a non-target user, stopping recognizing (003a) whether the voice signal includes the keyword. The voice control method reduces the power consumption of voice control and improves the endurance.

Techniques are described for detecting a conversation between at least two people, and for reducing noise during the conversation. In certain embodiments, at least one speech metric is generated based on spectral analysis of an audio signal and is used to determine that the audio signal represents speech from a first person. Responsive to determining that the speech is part of a conversation between the first person and a second person an operating state of a device in a physical environment is adjusted such that a volume level of sound contributed by or associated with the device is reduced. The sound contributed by or associated with the device corresponds to noise, at least for the duration of the conversation. Therefore, reducing the volume level of sound contributed by or associated with the device reduces the overall noise level in the environment, resulting in a reduction in conversational effort.

A control device includes at least one memory, and at least one processor configured to detect a voice segment from sound data, the sound data being detected while a controlled object operates, and stop the controlled object based on following conditions: a speaking speed is a predetermined speed threshold or greater, the speaking speed being calculated based on a portion of the sound data in the voice segment; and a length of the voice segment is a predetermined length threshold or less.

Techniques for automatically muting or unmuting an acoustic transducer include receiving a signal representing an output by a voice accelerometer of a device, determining whether the signal is indicative of a presence or absence of voice activity by a user of the device, and generating a control signal that causes an acoustic transducer to be muted responsive to determining that the signal is indicative of an absence of voice activity by the user, or that causes the acoustic transducer to be unmuted response to determining that the signal is indicative of a presence of voice activity by the user.

Certain aspects of the present disclosure provide a method for performing voice activity detection, including: receiving audio data from an audio source of an electronic device; generating a plurality of model input features using a hardware-based feature generator based on the received audio data; providing the plurality of model input features to a hardware-based voice activity detection model; receiving an output value from the hardware-based voice activity detection model; and determining a presence of voice activity in the audio data based on the output value.

An anomaly detection apparatus includes a device identification database that stores device identification information for identifying a specific device for each type of a device, a hierarchical conditional vector generation unit that generates a hierarchical conditional vector based on the device identification information, an extraction unit that extracts a target device feature amount vector indicating a feature amount of an acoustic signal acquired from a target device by analyzing the acoustic signal, a hierarchical condition adversarial neural network that outputs background noise level information indicating a background noise level of a surrounding environment of the target device and true/false determination information indicating true/false of the target device feature amount vector by analyzing the hierarchical conditional vector and the target device feature amount vector, and an anomaly determination unit that determines whether an anomaly exists in the target device feature amount vector.

An audio signal classification method includes determining, according to voice activity of a current audio frame, whether to obtain a frequency spectrum fluctuation of the current audio frame and store the frequency spectrum fluctuation in a frequency spectrum fluctuation memory, and updating, according to whether the audio frame is percussive music or activity of a historical audio frame, frequency spectrum fluctuations stored in the frequency spectrum fluctuation memory, and classifying the current audio frame as a speech frame or a music frame according to statistics of a part or all of effective data of the frequency spectrum fluctuations stored in the frequency spectrum fluctuation memory.

A first electronic device includes an interface; microphone circuitry; memory circuitry; and processor circuitry. The first electronic device is configured to discover, via the interface, a first wireless network. The first electronic device is configured to receive, via the interface, from a second electronic device discovering the first wireless network, a voice biometric indicative of a second user. The first electronic device is configured to activate the microphone circuitry to detect an input audio signal. The first electronic device is configured to determine whether the detected input audio signal satisfies one or more criteria. The first electronic device is configured to, when the detected input audio signal satisfies the one or more criteria, determine a parameter indicative of a level of risk of exposure; and generate, based on the parameter, contact data.

An electronic apparatus and a controlling method thereof are provided. The controlling method includes, based on an audio signal being received through a microphone, determining whether a user is on a public transport; detecting whether the audio signal includes a voice signal output through an acoustic device of the public transport; determining whether the voice signal from the acoustic device includes a voice signal for guiding at least one stop from among a plurality of stops; and outputting information on the at least one stop.

A method for optimizing speech recognition includes receiving a first acoustic segment characterizing a hotword detected by a hotword detector in streaming audio captured by a user device, extracting one or more hotword attributes from the first acoustic segment, and adjusting, based on the one or more hotword attributes extracted from the first acoustic segment, one or more speech recognition parameters of an automated speech recognition (ASR) model. After adjusting the speech recognition parameters of the ASR model, the method also includes processing, using the ASR model, a second acoustic segment to generate a speech recognition result. The second acoustic segment characterizes a spoken query/command that follows the first acoustic segment in the streaming audio captured by the user device.