Abstract: A method of managing audio in a multi-speaker environment, performed by a wearable audio device may be provided. The method may include generating, based on a binaural audio signal captured by the wearable audio device, a virtual sound source map indicating a localized position of one or more sources of sound. The method may include estimating, based on the virtual sound source map, one or more target sources indicating sources of sound of interest to a user of the wearable audio device. The method may include transmitting metadata associated with the wearable audio device, to an electronic device coupled to the wearable audio device, to cause the electronic device to refine the one or more target sources based on the metadata. The method may include receiving, from the electronic device, a processed audio signal associated with at least one refined target source.

Abstract: A method for detecting a wakeup command for a voice assistant is provided. The method includes receiving an audio signal from one or more sources and determining at least one of acoustic parameters or an environmental context of the user. Further, the method includes generating an embedding vector representation associated with the received audio signal and comparing the generated embedding vector representation with one or more prestored embedding vector representations. Furthermore, the method includes detecting the wakeup command in the received audio signal.

Abstract: Various embodiments of the disclosure relate to an apparatus for supporting automatic speech recognition in an electronic device capable of performing natural language processing, and an operating method thereof. To this end, based on one or more partial texts recognized sequentially from an audio signal input from a microphone, the electronic device may identify an utterance type for the input audio signal. The electronic device may adaptively determine a hangover time, based on the utterance type, and may detect an end of an utterance of the audio signal by applying the determined hangover time.

Abstract: Various embodiments of the disclosure relate to an apparatus for supporting automatic speech recognition in an electronic device capable of performing natural language processing, and an operating method thereof. To this end, based on one or more partial texts recognized sequentially from an audio signal input from a microphone, the electronic device may identify an utterance type for the input audio signal. The electronic device may adaptively determine a hangover time, based on the utterance type, and may detect an end of an utterance of the audio signal by applying the determined hangover time.

Abstract: A method for controlling an electronic device is provided. The method includes identifying one or more user interface (UI) elements displayed on a screen of an electronic device, determining a characteristic(s) of one or more identified UI elements, generating a data base based on the characteristic of one or more identified UI elements, where the database comprises to predict NL utterances of one or more identified UI elements, where the NL utterances are predicted based on the at least one characteristic of one or more identified UI elements, receiving a voice input of a user of the electronic device, where the voice input comprises an utterance indicative of the at least one characteristic of one or more identified UI elements presented in the database, and automatically accessing UI element(s) of one or more UI elements in response to determining that the utterances of the received voice input from the user matches with the predicted NL utterances of one or more identified UI elements.

Abstract: Embodiments herein disclose methods for handling a sound source in a media by an electronic device. The method includes: determining and classifying a relevant sound source in a media as a primary sound source, a secondary sound source, and a non-subject sound source based on a determined context; generating an output sound by suppressing at least one of the secondary sound source and the non-subject sound source in the media and optimizing the primary sound source in the media using a data driven model based on the determination and classification; or generating the output sound by partially suppressing at least one of the secondary sound source and the non-subject sound source in the media and optimizing the primary sound source in the media using the data driven model based on the determination and classification.

Abstract: A system and a method for determining semantic points in a human-to-human conversation is provided. The method includes identifying the human-to-human conversation including a plurality of dialogue turns and determining natural language (NL) attributes form each dialogue turn. Further, the method includes deriving a transient state, based on the one or more NL attributes. Further, the method includes deriving one or more conversation nuances associated with the human-to-human conversation based on the one or more NL attributes. Moreover, the method includes dynamically storing information associated with the human-to-human conversation based on the one or more NL attributes, the transient state, and the one or more conversation nuances associated with each dialogue turn and determining one or more semantic relations and associated dialogue timelines within the human-to-human conversation based on the dynamically stored information.

Abstract: An electronic device presents a virtual behavior of a participant in a Metaverse. The electronic device determines a context of the Metaverse including the people meeting with the participant. The electronic device determines a real-world behavior of the participant while immersed in the Metaverse. The electronic device generates virtual behavior of the participant based on the context of the Metaverse and the real-world behavior of the participant while immersed in the Metaverse. The electronic device renders an avatar of the participant having the virtual behavior of the participant.

Abstract: A method for detecting a wakeup command for a voice assistant is provided. The method includes receiving an audio signal from one or more sources and determining at least one of acoustic parameters or an environmental context of the user. Further, the method includes generating an embedding vector representation associated with the received audio signal and comparing the generated embedding vector representation with one or more prestored embedding vector representations. Furthermore, the method includes detecting the wakeup command in the received audio signal.

Abstract: Various embodiments of the disclosure relate to an apparatus for supporting automatic speech recognition in an electronic device capable of performing natural language processing, and an operating method thereof. To this end, based on one or more partial texts recognized sequentially from an audio signal input from a microphone, the electronic device may identify an utterance type for the input audio signal. The electronic device may adaptively determine a hangover time, based on the utterance type, and may detect an end of an utterance of the audio signal by applying the determined hangover time.

Abstract: A method for controlling an electronic device is provided. The method includes identifying one or more user interface (UI) elements displayed on a screen of an electronic device, determining a characteristic(s) of one or more identified UI elements, generating a data base based on the characteristic of one or more identified UI elements, where the database comprises to predict NL utterances of one or more identified UI elements, where the NL utterances are predicted based on the at least one characteristic of one or more identified UI elements, receiving a voice input of a user of the electronic device, where the voice input comprises an utterance indicative of the at least one characteristic of one or more identified UI elements presented in the database, and automatically accessing UI element(s) of one or more UI elements in response to determining that the utterances of the received voice input from the user matches with the predicted NL utterances of one or more identified UI elements.

Abstract: An electronic device is provided. The electronic device includes a housing, a user interface, a battery positioned inside the housing, a driving unit disposed at the housing or connected to the housing to move the housing, at least one sensor positioned at the housing or inside the housing, a wireless communication circuit positioned inside the housing, a processor operatively connected to the user interface, the driving unit, the at least one sensor, and the wireless communication circuit, and a memory operatively connected to the processor. The processor determines one sequence performed by connecting or merging the first task and the second task, based at least partly on at least one of a type, execution target, execution time, or execution location of the first task and at least one of a type, execution target, execution time, or execution location of the second task.

Abstract: An electronic device is provided. The electronic device includes a housing, a user interface, a battery positioned inside the housing, a driving unit disposed at the housing or connected to the housing to move the housing, at least one sensor positioned at the housing or inside the housing, a wireless communication circuit positioned inside the housing, a processor operatively connected to the user interface, the driving unit, the at least one sensor, and the wireless communication circuit, and a memory operatively connected to the processor. The processor determines one sequence performed by connecting or merging the first task and the second task, based at least partly on at least one of a type, execution target, execution time, or execution location of the first task and at least one of a type, execution target, execution time, or execution location of the second task.