Abstract: A technique is described herein for transforming audio content into images. The technique may include: receiving the audio content from a source; converting the audio content into a temporal stream of audio features; and converting the stream of audio features into one or more images using one or more machine-trained models. The technique generates the image(s) based on recognition of: semantic information that conveys one or more semantic topics associated with the audio content; and sentiment information that conveys one or more sentiments associated with the audio content. The technique then generates an output presentation that includes the image(s), which it provides to one or more display devices for display thereat. The output presentation serves as a summary of salient semantic and sentiment-related characteristics of the audio content.

Abstract: Disclosed in some examples are methods, systems, devices, and machine-readable mediums for determining states of content characteristics of electronic messages. In some embodiments, the probability of the states of the content characteristics of electronic messages are determined. Some embodiments determine a scores for states of content characteristics. Some embodiments determine a score for electronic messages for content characteristic diversity and inclusion based on a probability of a gender-bias state, a probability of a gender-neutral state, and a probability of not applicable to gender-bias state or gender-neutral state. In some embodiments the probabilities are determined based on a natural language model that is trained with data structures that relate training phrases to states of content characteristics.

Abstract: Methods and systems for providing a correct voice command. One system includes a communication device that includes an electronic processor configured to receive a first voice command via a microphone and analyze the first voice command using a first type of voice recognition. The electronic processor determines that an action to be performed in accordance with the first voice command is unrecognizable based on the analysis using the first type of voice recognition. The electronic processor transmits the first voice command to a remote electronic computing device accompanying a request requesting that the first voice command be analyzed using a second type of voice recognition different from the first type of voice recognition. The electronic processor receives, from the remote electronic computing device, a second voice command corresponding to the action and different from the first voice command, and outputs, with a speaker, the second voice command.

Abstract: A method of generating text from speech using video-speech matching from a primary user is disclosed herein. The method requires first receiving a video and audio input. The video and audio inputs are then segmented into a plurality of video and audio features, respectively. The plurality of video and audio features are then matched according to their similarities. A primary speaker is then determined from one of the matched video and audio features. The primary speaker's matched video and audio features are then used to generate a text representative of the primary speaker's speech.

Abstract: An information processing apparatus includes a processor that executes an information processing program to operate as a text obtaining unit that obtains a text indicating an issue occurred in an electronic apparatus, a keyword extracting unit that tokenizes the text, and filters words obtained by tokenizing the text to extract a keyword, a maintenance-information obtaining unit that obtains a maintenance-information-item or a plurality of maintenance-information-items in association with the keyword from a database that stores a plurality of maintenance-information-items, each of the plurality of maintenance-information-item being information about a solution to an issue occurred in the electronic apparatus, and a maintenance-information providing unit that provides the obtained maintenance-information-item or the plurality of obtained maintenance-information-items to a user.

Abstract: Systems, devices, and methods are described for reducing degradation of a voice recognition input. An always listening device may always be listening for voice commands via a microphone and may experience interference from unwanted audio such as from the output audio of television speakers. The always listening device may receive data associated with the output audio over a first communications channel. The always listening device may also receive, on a second communications channel, timing information associated with data. The always listening device may adjust admission of the audio received by the microphone to enable it to arrive at approximately the same time as the data received via the first communications channel. The unwanted output audio included in the audio received via the microphone may then be determined and may be removed so that a voice command in the audio received by the microphone may be processed.

Abstract: A response sentence generation apparatus includes a conversion device for converting an input voice of a user into text information, an extraction device for extracting prosodic information from the input voice, a specifying device for specifying an emotion occurrence word indicating an occurrence of an emotion of the user based on the text information and the prosodic information, and a generation device for selecting a character string including the specified emotion occurrence word from the text information and generating a response sentence by performing predetermined processing on the selected character string.

Abstract: Methods, apparatuses, and computer program products are described herein that are configured to express a time in an output text. In some example embodiments, a method is provided that comprises identifying a time period to be described linguistically in an output text. The method of this embodiment may also include identifying a communicative context for the output text. The method of this embodiment may also include determining one or more temporal reference frames that are applicable to the time period and a domain defined by the communicative context. The method of this embodiment may also include generating a phrase specification that linguistically describes the time period based on the descriptor that is defined by a temporal reference frame of the one or more temporal reference frames. In some examples, the descriptor specifies a time window that is inclusive of at least a portion of the time period to be described linguistically.

Abstract: An audio signal encoding method is provided comprising: receiving first and second audio signal frames; processing a second portion of the first audio signal frame and a first portion of the second audio signal frame using an orthogonal transformation to determine in part a first intermediate encoding result; and processing the first intermediate encoding result using an orthogonal transformation to determine a set of spectral coefficients that corresponds to at least a portion of the first audio signal frame.

Abstract: Candidate identification and matching for professional positions, and associated systems and methods are disclosed herein. A representative method includes obtaining first key phrase groups based on textual input, converting the first key phrase groups into vectors defined in accordance with a collection of key phrases, generating a set of topics based on the vectors, generating second key phrase groups based on an association between individual topics of the set of topics and the collection of key phrases, and identifying documentation associated with one or more candidates for a professional position based on the second key phrase groups.

Abstract: An approach is provided that receives, from a user, an amalgamation at a digital assistant. The amalgamation includes one or more words spoken by the user that are captured by a digital microphone and a set of digital images corresponding to one or more gestures that are performed by the user with the digital images captured by a digital camera. The system then determines an action that is responsive to the amalgamation and then performs the determined action.

Abstract: An audio encoder for encoding an audio signal having a lower frequency band and an upper frequency band includes: a detector for detecting a peak spectral region in the upper frequency band of the audio signal; a shaper for shaping the lower frequency band using shaping information for the lower band and for shaping the upper frequency band using at least a portion of the shaping information for the lower band, wherein the shaper is configured to additionally attenuate spectral values in the detected peak spectral region in the upper frequency band; and a quantizer and coder stage for quantizing a shaped lower frequency band and a shaped upper frequency band and for entropy coding quantized spectral values from the shaped lower frequency band and the shaped upper frequency band.

Abstract: A method of encoding an audio signal is provided comprising: applying multiple different time-frequency transformations to an audio signal frame; computing measures of coding efficiency across multiple frequency bands for multiple time-frequency resolutions; selecting a combination of time-frequency resolutions to represent the frame at each of the multiple frequency bands based at least in part upon the computed measures of coding efficiency; determining a window size and a corresponding transform size; determining a modification transformation; windowing the frame using the determined window size; transforming the windowed frame using the determined transform size; modifying a time-frequency resolution within a frequency band of the transform of the windowed frame using the determined modification transformation.

Abstract: In one implementation, a content processing system includes a computing platform having a hardware processor and a system memory storing a content classification software code, a natural language processor, and a computer vision analyzer. The hardware processor executes the content classification software code to receive content inputs from multiple content sources, and, for each content input, to parse the content input for metadata describing the content input, obtain a description of language-based content included in the content input from the natural language processor, and obtain a description of visual content included in the content input from the computer vision analyzer.

Abstract: The invention discloses systems and methods for enhancing the sound of vocal utterances of interest in an acoustically cluttered environment. The system generates canceling signals (sound suppression signals) for an ambient audio environment and identifies and characterizes desired vocal signals and hence a vocal stream or multiple streams of interest. Each canceling signal, or collectively, the noise canceling stream, is processed so that signals associated with the desired audio stream or streams are dynamically removed from the canceling stream. This modified noise canceling stream is combined (electronically or acoustically) with the ambient to effectuate a destructive interference of all ambient sound except for the removed audio streams, thus “enhancing” the vocal streams with respect to the unwanted ambient sound. Cepstral analysis may be used to identify a fundamental frequency associated with a voiced human utterance.

Abstract: A system and method for providing a voice assistant including receiving, at a first device, a first audio input from a user requesting a first action; performing automatic speech recognition on the first audio input; obtaining a context of user; performing natural language understanding based on the speech recognition of the first audio input; and taking the first action based on the context of the user and the natural language understanding.

Abstract: A sound collection apparatus includes: a sound collection unit including a microphone configured to collect sound; a noise determination unit configured to determine noise in dictation based on voice collected by the sound collection unit; and a presentation unit configured to perform presentation based on a determination result by the noise determination unit. With this configuration, presentation is performed to indicate environmental noise at sound collection for dictation, which leads to improved efficiency of dictation work.

Abstract: A speech-enabled dialog system responds to a plurality of wake-up phrases. Based on which wake-up phrase is detected, the system's configuration is modified accordingly. Various configurable aspects of the system include selection and morphine of a text-to-speech voice; configuration of acoustic model, language model, vocabulary, and grammar; configuration of a graphic animation; configuration of virtual assistant personality parameters; invocation of a particular user profile; invocation of an authentication function; and configuration of an open sound. Configuration depends on a target market segment. Configuration also depends on the state of the dialog system, such as whether a previous utterance was an information query.

Abstract: A sound collection apparatus includes: a sound collection unit including a microphone configured to collect sound; and a recording unit configured to record a personal dictionary used in voice recognition processing at dictation of voice collected by the sound collection unit. The sound collection apparatus further includes: a control unit configured to control provision of the personal dictionary to an information processing apparatus configured to execute dictation of voice collected by the sound collection unit; and a communication unit configured to transmit voice collected by the sound collection unit to the information processing apparatus and transmit, under control of the control unit, the personal dictionary to the information processing apparatus. This configuration achieves improved voice recognition performance even when a computer other than an exclusively used personal computer is used.

Abstract: Approaches for multitask learning as question answering include an input layer for encoding a context and a question, a self-attention based transformer including an encoder and a decoder, a first bi-directional long-term short-term memory (biLSTM) for further encoding an output of the encoder, a long-term short-term memory (LSTM) for generating a context-adjusted hidden state from the output of the decoder and a hidden state, an attention network for generating first attention weights based on an output of the first biLSTM and an output of the LSTM, a vocabulary layer for generating a distribution over a vocabulary, a context layer for generating a distribution over the context, and a switch for generating a weighting between the distributions over the vocabulary and the context, generating a composite distribution based on the weighting, and selecting a word of an answer using the composite distribution.