Abstract: A method for generating an acoustic model is disclosed. The method can generate the acoustic model with high accuracy through learning data including various dialects by training the acoustic model using text data, to which regional information is tagged, and changing a parameter of the acoustic model based on the tagged regional information. The acoustic model can be associated with an artificial intelligence module, an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services, and the like.

Abstract: A text extracting system includes at least one processor configured to obtain a plurality of texts, specify at least one characteristic expression included in the plurality of texts, and extract, based on the at least one characteristic expression, at least one text to be entered into a question sentence generator from the plurality of texts, where the question sentence generator generating a question sentence from an input sentence.

Abstract: A conference minutes generation method is provided, which relates to the technical field of natural language processing. The conference minutes generation method comprises: acquiring a text conference record; dividing the text conference record into a plurality of conference paragraphs, generating a conference paragraph summary for each conference paragraph, and generating a conference record summary based on the conference paragraph summary of each conference paragraph; extracting conference instructions based on the text conference record; and generating the conference minutes based on the conference record summary and the conference instructions.

Abstract: A system and method performs wakeword detection using a feedforward neural network model. A first output of the model indicates when the wakeword appears on a right side of a first window of input audio data. A second output of the model indicates when the wakeword appears in the center of a second window of input audio data. A third output of the model indicates when the wakeword appears on a left side of a third window of input audio data. Using these outputs, the system and method determine a beginpoint and endpoint of the wakeword.

Abstract: An artificial intelligence (AI) system is provided. The AI system simulates functions of human brain such as recognition and judgment by utilizing a machine learning algorithm such as deep learning, etc. and an application of the AI system. A method, performed by an electronic device, of outputting a response to a speech input by using an application, includes receiving the speech input, obtaining text corresponding to the speech input by performing speech recognition on the speech input, obtaining metadata for the speech input based on the obtained text, selecting at least one application from among a plurality of applications for outputting the response to the speech input based on the metadata, and outputting the response to the speech input by using the selected at least one application.

Abstract: Methods, systems, and related products for voice-enabled computer systems are described. A machine-learning model is trained to produce pronunciation output based on text input. The trained machine-learning model is used to produce pronunciation data for text input even where the text input includes numbers, punctuation, emoji, or other non-letter characters. The machine-learning model is further trained based on real-world data from users to improve pronunciation output.

Abstract: The disclosure includes using dilation of speech content from an interlaced audio input for speech recognition. A learning model is initiated to determine dilation parameters for each of a plurality of audible sounds of speech content from a plurality of speakers received at a computer as an audio input. As part of the learning model, a change of each of a plurality of independent sounds is determined in response to an audio stimulus, the independent sounds being derived from the audio input. The disclosure applies the dilation parameters, respectively, based on the change of each of the independent sounds. A voice print is constructed for each of the speakers based on the independent sounds and the dilation parameters, respectively. Speech content is attributed to each of the plurality of speakers based at least in part on the voice print, respectively, and the independent sounds.

Abstract: Disclosed are a sound processing apparatus and a sound processing method. The sound processing method includes extracting a desired voice enhanced signal by a sound source separation and a sound extraction. By using a multi-channel blind source separation method based on independent vector analysis, the desired voice enhanced signal is extracted from a channel having the smallest sum of off-diagonal values of a separation adaptive filter when the power of the desired voice signal is larger than that of other voice signals. According to the present disclosure, a user may build a robust artificial intelligence (AI) speech recognition system by using sound source separation and voice extraction using eMBB, URLLC, and mMTC techniques of 5G mobile communication.

Abstract: Implementations can reduce the time required to obtain responses from an automated assistant by, for example, obviating the need to provide an explicit invocation to the automated assistant, such as by saying a hot-word/phrase or performing a specific user input, prior to speaking a command or query. In addition, the automated assistant can optionally receive, understand, and/or respond to the command or query without communicating with a server, thereby further reducing the time in which a response can be provided. Implementations only selectively initiate on-device speech recognition responsive to determining one or more condition(s) are satisfied. Further, in some implementations, on-device NLU, on-device fulfillment, and/or resulting execution occur only responsive to determining, based on recognized text form the on-device speech recognition, that such further processing should occur.

Abstract: Disclosed is a speech processing method. The speech processing method controls activation timing of a microphone based on a response pattern of the microphone from a user in order to implement a natural conversation. The speech processing device and the NLP system of the present disclosure may be associated with an artificial intelligence module, a drone (or unmanned aerial vehicle (UAV)), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to 5G service, etc.

Abstract: Techniques are described related to enabling automated assistants to enter into a “conference mode” in which they can “participate” in meetings between multiple human participants and perform various functions described herein. In various implementations, an automated assistant implemented at least in part on conference computing device(s) may be set to a conference mode in which the automated assistant performs speech-to-text processing on multiple distinct spoken utterances, provided by multiple meeting participants, without requiring explicit invocation prior to each utterance. The automated assistant may perform semantic processing on first text generated from the speech-to-text processing of one or more of the spoken utterances, and generate, based on the semantic processing, data that is pertinent to the first text. The data may be output to the participants at conference computing device(s).

Abstract: A brain computer interface (BCI) system predicts text based on input and output signals obtained in relation to an individual that are informative for determining an individual's neurobiological activity. The BCI system applies a first predictive model to the input signal and a second predictive model to the output signal. The first predictive model predicts the forward propagation of the input signal through the individual's head whereas the second predictive model predicts the backward propagation of the output signal through the individual's head. Each of the first predictive model and second predictive model predicts characteristics of their respective signal at a common plane such as the cortical surface of the individual's brain. The BCI system predicts text by applying a third predictive model to the predicted signal characteristics at the common plane outputted by the first predictive model and the second predictive model.

Abstract: A document identification device that improves class identification precision of multi-stream documents is provided. The document identification device includes: a primary stream expression generation unit that generates a primary stream expression, which is a fixed-length vector of a word sequence corresponding to each speaker's speech recorded in a setting including a plurality of speakers, for each speaker; a primary multi-stream expression generation unit that generates a primary multi-stream expression obtained by integrating the primary stream expression; a secondary stream expression generation unit that generates a secondary stream expression, which is a fixed-length vector generated based on the word sequence of each speaker and the primary multi-stream expression, for each speaker; and a secondary multi-stream expression generation unit that generates a secondary multi-stream expression obtained by integrating the secondary stream expression.

Abstract: An AI device is provided. The AI device includes a content output interface to output video data contained in content and voice data contained in the content, and a processor to control the content output interface to acquire a voice recognition result by providing, to a voice recognition model, content extraction information including at least one of video information acquired from the video data in the content or tag information of the content and the voice data, and control the content output interface to output the voice recognition result.

Abstract: Techniques for handling errors during processing of natural language inputs are described. A system may process a natural language input to generate an ASR hypothesis or NLU hypothesis. The system may use more than one data searching technique (e.g., deep neural network searching, convolutional neural network searching, etc.) to generate an alternate ASR hypothesis or NLU hypothesis, depending on the type of hypothesis input for alternate hypothesis processing.

Abstract: The present disclosure describes a method, apparatus, and storage medium for performing speech recognition. The method includes acquiring, by an apparatus, first to-be-processed speech information. The apparatus includes a memory storing instructions and a processor in communication with the memory. The method includes acquiring, by the apparatus, a first pause duration according to the first to-be-processed speech information; and in response to the first pause duration being greater than or equal to a first threshold, performing, by the apparatus, speech recognition on the first to-be-processed speech information to obtain a first result of sentence segmentation of speech, the first result of sentence segmentation of speech being text information, the first threshold being determined according to speech information corresponding to a previous moment.

Abstract: Devices, systems, and methods are provided for locally grouping voice-enabled device state communications. A device may determine first state information associated with the first device and send the first state information to a second device. The device may receive second state information associated with a second device and third state information associated with a third device. The device may receive an audible command, and may determine, based on the audible command, an indicator to send state data. The device may send the first state information, the second state information, the third state information, and data associated with the audible command. The device may receive fourth state information associated with the audible command.

Abstract: Implementations of the subject matter described herein relate to conversational data analysis. After a data analysis request is received from a user, heuristic information may be determined based on the data analysis request. The heuristic information mentioned here is not a result for the data analysis request but information which may be used for leading the conversation to proceed. Based on such heuristic information, the user may provide supplementary information associated with the data analysis request, for example, clarify meaning of the data analysis request, submit a relevant further analysis request, and so on. A really desired and meaningful data analysis result can be provided to the user according to the supplementary information provided by the user. Thus, data analysis will become more accurate and effective. While obtaining really helpful information, the user also gains good user experience.

Abstract: Techniques are described herein for enabling the use of “dynamic” or “context-specific” hot words for an automated assistant. In various implementations, an automated assistant may be operated at least in part on a computing device. Audio data captured by a microphone may be monitored for default hot word(s). Detection of one or more of the default hot words may trigger transition of the automated assistant from a limited hot word listening state into a speech recognition state. Transition of the computing device into a given state may be detected, and in response, the audio data captured by the microphone may be monitored for context-specific hot word(s), in addition to or instead of the default hot word(s). Detection of the context-specific hot word(s) may trigger the automated assistant to perform a responsive action associated with the given state, without requiring detection of default hot word(s).

Abstract: The embodiments of the present application provide a method for updating a speech recognition model a storage medium and an electronic device. The method includes: detecting whether the speech recognition algorithm is updated; and updating the speech recognition model when the speech recognition algorithm has been updated. Wherein, the voice information is recognized by the electronic device based on the speech recognition algorithm and the speech recognition model. In the method for updating a speech recognition model, when the electronic device detects that the speech recognition algorithm has been updated, the electronic device can update the speech recognition model.