The following invention is a vocally activated control system for controlling an apparatus in a surgical setting, the system comprises: a. a voice sensor configured to detect vocal commands generated by surgeons during surgery; b. a signal transmitter connected to the voice sensor, the transmitter is configured to convert a vocal command into a transmittable signal and transmit it; c. a processor connected to a signal transmitter configured to receive a transmittable vocal signal, the processor is configured to convert a vocal signal to a predetermined set of operative instructions associated with the apparatus, the predetermined set of operative instructions comprising at least one instruction; and d. control means connected to the processor and apparatus; the control means is configured to receive a predetermined set of operative instructions and to cause the apparatus to operate accordingly; Said voice sensor and said transmitter are integrated within a wearable element.

The following invention is a vocally activated control system for controlling an apparatus in a surgical setting, the system comprises: a. a voice sensor configured to detect vocal commands generated by surgeons during surgery; b. a signal transmitter connected to the voice sensor, the transmitter is configured to convert a vocal command into a transmittable signal and transmit it; c. a processor connected to a signal transmitter configured to receive a transmittable vocal signal, the processor is configured to convert a vocal signal to a predetermined set of operative instructions associated with the apparatus, the predetermined set of operative instructions comprising at least one instruction; and d. control means connected to the processor and apparatus; the control means is configured to receive a predetermined set of operative instructions and to cause the apparatus to operate accordingly; Said voice sensor and said transmitter are integrated within a wearable element.

This application is directed to a display assistant device that acts as a voice-activated user interface device. The display assistant device includes a base, a screen and a speaker. The base is configured for sitting on a surface. The screen has a rear surface and is supported by the base at the rear surface. A bottom edge of the screen is configured to be held above the surface by a predefined height, and the base is substantially hidden behind the screen from a front view of the display assistant device. The speaker is concealed inside the base and configured to project sound substantially towards the front view of the display assistant device.

This application is directed to a display assistant device that acts as a voice-activated user interface device. The display assistant device includes a base, a screen and a speaker. The base is configured for sitting on a surface. The screen has a rear surface and is supported by the base at the rear surface. A bottom edge of the screen is configured to be held above the surface by a predefined height, and the base is substantially hidden behind the screen from a front view of the display assistant device. The speaker is concealed inside the base and configured to project sound substantially towards the front view of the display assistant device.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for hotword detection on multiple devices are disclosed. In one aspect, a method includes the actions of receiving, by a first computing device, audio data that corresponds to an utterance. The actions further include determining a first value corresponding to a likelihood that the utterance includes a hotword. The actions further include receiving a second value corresponding to a likelihood that the utterance includes the hotword, the second value being determined by a second computing device. The actions further include comparing the first value and the second value. The actions further include based on comparing the first value to the second value, initiating speech recognition processing on the audio data.

A device may be configured to determine whether an audio file is a first type of audio file that is capable of being processed to recognize the voice query based on a characteristic of the audio file itself or a second type of audio file that may require speech recognition processing in order to recognize the voice query associated with the audio file. In determining whether the audio file is a first type of audio file or a second type of audio file, a query filter associated with the device may be configured to access one or more guidance queries. Using the one or more guidance queries, the device may classify the audio file as a first type of audio file or a second type of audio file based on receiving only a portion of the audio file, thereby improving the speed at which the audio file can be processed.

An information processing device including a control unit that performs control not to react to a user's expression, if the user's expression includes a representation of a predetermined non-response setting, until predetermined setting conditions are satisfied and to react to the user's expression if the user's expression does not include the representation of the non-response setting.

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.

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.

Disclosed are an electronic apparatus for dynamic note matching (DNM) and an operating method thereof, the method including acquiring a first section sequence by reducing a first sequence extracted from an input signal based on at least one first section in which the respective values are successively arranged; acquiring a second section sequence reduced from a pre-stored second sequence based on at least one second section in which the respective values are successively arranged; and calculating a similarity between the first section sequence and the second section sequence.