Abstract: The disclosed computer-implemented method includes analyzing, by a speech detection system, a media file to detect lip movement of a speaker who is visually rendered in media content of the media file. The method additionally includes identifying, by the speech detection system, audio content within the media file, and improving accuracy of a temporal correlation of the speech detection system. The method may involve correlating the lip movement of the speaker with the audio content, and determining, based on the correlation between the lip movement of the speaker and the audio content, that the audio content comprises speech from the speaker. The method may further involve recording, based on the determination that the audio content comprises speech from the speaker, the temporal correlation between the speech and the lip movement of the speaker as metadata of the media file. Various other methods, systems, and computer-readable media are disclosed.

Abstract: Techniques for constructing and otherwise managing knowledge graphs in information processing system environments are disclosed. For example, a method comprises the following steps. The method collects data from a plurality of data sources. The method extracts structured data and unstructured data from the collected data, wherein unstructured data is extracted using an unsupervised machine learning process. The method forms a plurality of sub-graph structures comprising a sub-graph structure for each of the data sources based on at least a portion of the extracted structured data and unstructured data. The method combines the plurality of sub-graph structures to form a combined graph structure representing the collected data from the plurality of data sources. The resulting combined graph structure is a comprehensive knowledge graph.

Abstract: A method of presenting a signal to a speech processing engine is disclosed. According to an example of the method, an audio signal is received via a microphone. A portion of the audio signal is identified, and a probability is determined that the portion comprises speech directed by a user of the speech processing engine as input to the speech processing engine. In accordance with a determination that the probability exceeds a threshold, the portion of the audio signal is presented as input to the speech processing engine. In accordance with a determination that the probability does not exceed the threshold, the portion of the audio signal is not presented as input to the speech processing engine.

Abstract: A system and method for recognizing (reading) the tongue movements, vocalizations, and throat vibrations of a person and converting (translating) them into meaningful synthesized words, which could be pronounced by an electronic speaker and/or displayed on a display. Often a patient/person who has lost the ability to speak may still be able to move their tongues, or make unfathomable sounds, which cannot be recognized as intelligible words. The system and method can continuously record the movement of the patient's tongue, vocalizations, and throat sounds and extract small video segments corresponding to different words attempted by the patient. Each of these video segments can then be analyzed by AI software or other configured software to match the specific tongue movement with a pre-learned reference word, and once identified, the computer/system can speak or verbalize the word, and/or display it on a screen.

Abstract: Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, generating predictions based at least in part on recognizing significant words in unstructured text. In an embodiment, a method is provided. The method comprises: generating a plurality of word-level tokens for an input unstructured textual data object; and for each word-level token: determining a significance type and a significance subtype for the word-level token by using a significance recognition machine learning model, and assigning a significance token label or an insignificance token label to the word-level token.

Abstract: Disclosed are a method, a device, and a program for selecting a main speaker among speakers included in a sound source or a conversation record based on the sound source or the conversation record including conversation contents of at least one speaker and generating a summary based on the main speaker. A method of generating a summary for a sound source, the method being performed by at least one computing device, includes: generating a speak score for at least one speaker based on the sound source; determining a main speaker of the sound source based on a speak score for said at least one speaker; and generating a summary for the sound source in consideration of the determined main speaker.

Abstract: Methods, apparatus, and computer readable media are described related to automated assistants that proactively incorporate, into human-to-computer dialog sessions, unsolicited content of potential interest to a user. In various implementations, in an existing human-to-computer dialog session between a user and an automated assistant, it may be determined that the automated assistant has responded to all natural language input received from the user. Based on characteristic(s) of the user, information of potential interest to the user or action(s) of potential interest to the user may be identified. Unsolicited content indicative of the information of potential interest to the user or the action(s) may be generated and incorporated by the automated assistant into the existing human-to-computer dialog session.

Abstract: A computer-implemented system and method relate to natural language processing and knowledge representation and reasoning. A first dataset is created that includes input data and situational data. The situational data provides context for the input data. An encoder is configured to generate an encoded representation of the first dataset. The encoder includes at least an encoding network of a first pre-trained generative machine learning model, which relates to a generative knowledge graph. A decoder includes a decoding network of a second pre-trained generative machine learning model. The decoder is configured to generate response data based on the first dataset by decoding the encoded representation. The decoder is also configured to generate event-centric knowledge based on the first dataset by decoding the encoded representation. The input data and the response data are connected to the same event-centric knowledge via the generative knowledge graph.

Abstract: A method and device for encoding a stereo sound signal comprise stereo encoders using stereo modes operating in time domain (TD), in frequency domain (FD) or in modified discrete Fourier transform (MDCT) domain. A controller controls switching between the TD, FD and MDCT stereo modes. Upon switching from one stereo mode to the other, the switching controller may (a) recalculate at least one length of down-processed/mixed signal in a current frame of the stereo sound signal, (b) reconstruct a down-processed/mixed signal and also other signals related to the other stereo mode in the current frame, (c) adapt data structures and/or memories for coding the stereo sound signal in the current frame using the other stereo mode, and/or (d) alter a TD stereo channel down-mixing to maintain a correct phase of left and right channels of the stereo sound signal. Corresponding stereo sound signal decoding method and device are described.

Abstract: In one aspect, an example method includes (i) obtaining media, wherein the obtained media includes (a) audio representing speech and (b) video; (ii) using at least the audio representing speech as a basis to generate speech text; (iii) using at least the audio representing speech to determine starting and ending time points of the speech; and (iv) using at least the generated speech text and the determined starting and ending time points of the speech to (a) generate closed-captioning or subtitle data that includes closed-captioning or subtitle text based on the generated speech text and (b) associating the generated closed-captioning or subtitle data with the obtained media, such that the closed-captioning or subtitle text is time-aligned with the video based on the determined starting and ending time points of the speech.

Abstract: An operation support apparatus includes: an acquisition unit that acquires an operation status of transportation means; a generation unit that performs processing of recognizing voices of a first staff who gives an instruction to operate the transportation means and a second staff who is instructed by the first staff, and generates character information obtained by converting the recognized voice into characters; a detection unit that performs syntax analysis on the character information and detects wrong recognition by the first or second staff; and a display control unit that displays the character information and the detection result of the wrong recognition by the detection unit on a display device visually recognizable by the first staff, thereby reducing occurrence of an accident due to the wrong recognition by the staff related to operation of the transportation means at a site where the transportation means is operated.

Abstract: A method, performed by an electronic device, of providing a conversational service includes: receiving an utterance input; identifying a temporal expression representing a time in a text obtained from the utterance input; determining a time point related to the utterance input based on the temporal expression; selecting a database corresponding to the determined time point from among a plurality of databases storing information about a conversation history of a user using the conversational service; interpreting the text based on information about the conversation history of the user, the conversation history information being acquired from the selected database; generating a response message to the utterance input based on a result of the interpreting; and outputting the generated response message.

Abstract: Implementations set forth herein relate to phasing-out of vehicle computing device versions while ensuring useful responsiveness of any vehicle computing device versions that are still in operation. Certain features of updated computing devices may not be available to prior versions of computing devices because of hardware limitations. The implementations set forth herein eliminate crashes and wasteful data transmissions caused by prior versions of computing devices that have not been, or cannot be, upgraded. A server device can be responsive to a particular intent request provided to a vehicle computing device, despite the intent request being associated with an action that a particular version of the vehicle computing device cannot execute. In response, the server device can elect to provide speech to text data, and/or natural language understanding data, in furtherance of allowing the vehicle computing device to continue leveraging resources at the server device.

Abstract: An electronic apparatus and a controlling method thereof are provided. The electronic apparatus includes a memory storing an artificial intelligence model, a display configured to display a keypad screen, and a processor configured to, based on a swipe interaction contacting a first key and connecting a plurality of keys being on the keypad screen, remove from the input window, a first word that is pre-input to the input window, obtain a word candidate list corresponding to the swipe interaction by inputting information regarding a trajectory of the swipe interaction to an artificial intelligence model, and control the display to display, in the input window, a second word having a highest score in the word candidate list.

Abstract: A communication methods, including: receiving first information by a first control panel of a plurality of control panels; determining one or more target control panels from the plurality of control panels (S101), where the plurality of control panels are disposed in different rooms, or at least partially at different locations in a same room; for each of the one or more target control panels, detecting whether a room where the target control panel is located is occupied (S102); in response to determining that there is an occupant, sending the first information to the target control panel by the first control panel (S103), and outputting the first information from the target control panel (S104); and in response to determining that there is no occupant, generating first prompt information by the first control panel (S105).

Abstract: The application discloses an event extraction method, apparatus, device and storage medium, the method including acquiring a text to be processed, inputting the text to be processed into an event detection model to determine an event type involved in the text to be processed, constructing a first query corpus based on the event type, inputting the text to be processed and the first query corpus into a reading comprehension model to determine an event body corresponding to the event type and event elements corresponding to the event body, obtaining an event extraction result of the text to be processed based on the event body and the event elements. This method accurately determines the event type and the event elements in the text to be processed, and is adapted to event extraction in a document level text, improving the overall performance of event extraction and promoting the efficiency of event extraction.

Abstract: An apparatus for generating a representation of a bandwidth-extended signal on the basis of an input signal representation includes a phase vocoder configured to obtain values of a spectral domain representation of a first patch of the bandwidth-extended signal on the basis of the input signal representation. The apparatus also includes a value copier configured to copy a set of values of the spectral domain representation of the first patch, which values are provided by the phase vocoder, to obtain a set of values of a spectral domain representation of a second patch, wherein the second patch is associated with higher frequencies than the first patch. The apparatus is configured to obtain the representation of the bandwidth-extended signal using the values of the spectral domain representation of the first patch and the values of the spectral domain representation of the second patch.

Abstract: Provided are a speech recognition device and method. The speech recognition device includes a microphone and a processor configured to receive a voice signal through the microphone, generate voice characteristic data by analyzing the voice signal by using a data recognition model based on a neural network, determine whether the voice signal is voice uttered from a user or voice output from an external device based on the voice characteristic data, and when the voice signal is determined as the voice uttered from the user, determine the voice signal as a voice command of the user and perform an operation corresponding to the voice command. The present disclosure also relates to an artificial intelligence (AI) system utilizing a machine learning algorithm such as deep learning, and applications thereof.

Abstract: A messaging system (400) comprises a user interface (404) coupled with a processor (402) and a memory (406), to display text messages; a translation selection module (414) coupled with the processor (402) and the user interface (404) to: associate a translation input selection button (203) with each of the text messages; send, upon selection translation input selection button (203), the associated text message data with language code to a messaging system server (104) for translation; receive the translated text message data from the messaging system server (104); a display module (416) coupled with the processor (402) to display the translated text message retrieve from the translated text message data with the associated translation input selection button (203) in place of the text message on the user interface (404, 201).

Abstract: Disclosed is an electronic apparatus. The electronic apparatus obtains a first character string comprising a previously defined character from first user utterance; recognizes a second character string, which is edited from the first character string based on a first edition command, as an input character, based on the first user utterance comprising the first edition command following the first character string; and performs edition with regard to the second character string based on second edition command, based on second user utterance comprising the second edition command without the first edition command.