Abstract: A control method for a human-computer interaction device, a human-computer interaction device, and a human-computer interaction system are described. The control method includes: capturing first voice information of a first object; identifying a second object related to the first voice information; acquiring first information related to the second object; and presenting the first information.

Abstract: A system configured to vary a speech speed of speech represented in input audio data without changing a pitch of the speech. The system may vary the speech speed based on a number of different inputs, including non-audio data, data associated with a command, or data associated with the voice message itself. The non-audio data may correspond to information about an account, device or user, such as user preferences, calendar entries, location information, etc. The system may analyze audio data associated with the command to determine command speech speed, identity of person listening, etc. The system may analyze the input audio data to determine a message speech speed, background noise level, identity of the person speaking, etc. Using all of these inputs, the system may dynamically determine a target speech speed and may generate output audio data having the target speech speed.

Abstract: A device implementing an automatic speech recognition triggering system includes at least one processor configured to receive first and second audio signals respectively corresponding to first and second microphones of a device. The at least one processor is further configured to generate, based on at least one of the first or second audio signals, a third audio signal corresponding to a voice beam directed to an expected position of a mouth of a user. The at least one processor is further configured to determine whether wind noise is present in at least one of the first, second, or third audio signals. The at least one processor is further configured to, based on determining whether wind noise is present, an audio signal from among the second or third audio signals, for a determination of whether at least one of the first or second audio signals corresponds to the user.

Abstract: The systems and methods of seamlessly connecting an internet of things (“IoT”) device to one or more intelligent voice assistants, comprising: configuring a manager module to manage an IoT device connected to a network; receiving a speech command for the IoT device at the manager module through a mobile application, a smart speaker, a web interface or any other user interface; connecting to a central Speak-to-IoT cloud service; receiving a map to connect to a customer specific Speak-to-IoT cloud service based on the customer, IoT device type and manager module; authenticating with the customer specific Speak-to-IoT cloud service; communicating and executing the speech command on the IoT device. The systems and methods further comprising adding or replacing one or more IoT device with another device type or manager module of another type.

Abstract: A speech synthesizer using artificial intelligence includes a memory configured to store a first ratio of a word classified into a minor class among a plurality of classes and a synthesized speech model, and a processor configured to determine a class classification probability set of the word using the word, the first ratio and the synthesized speech model. The first ratio indicates a ratio in which the word is classified into the minor class within a plurality of characters, the plurality of classes includes a first class corresponding to first reading break, a second class corresponding to second reading break greater than the first break and a third class corresponding to third reading break greater than the second break, and the minor class has a smallest count among the first to third classes.

Abstract: Disclosed is a system and method for detecting and addressing bias in training data prior to building language models based on the training data. Accordingly system and method, detect bias in training data for Intelligent Virtual Assistant (IVA) understanding and highlight any found. Suggestions for reducing or eliminating them may be provided This detection may be done for each model within the Natural Language Understanding (NLU) component. For example, the language model, as well as any sentiment or other metadata models used by the NLU, can introduce understanding bias. For each model deployed, training data is automatically analyzed for bias and corrections suggested.

Abstract: Structured conversation enhancement can include determining an anticipated ebb point of a current conversation. The determination can be made in response to a predetermined triggering event indicating a start of the current conversation. Structured conversation enhancement also can include monitoring the current conversation using pattern recognition. A probable change in the anticipated ebb point can be determined in response to recognizing a predetermined word pattern indicating a change in the conversation. A response action can be initiated in response to the probable change in the anticipated ebb point.

Abstract: A method of and system of for compressing and decompressing a localized software resource is disclosed. The method may include receiving a software resource, the software resource being in a first language, receiving a localized software resource for compression, where the software resource in the first language is a counterpart of the localized software resource in the second language. Upon receiving the software resources creating a first local dictionary for the localized software resource based at least in part on one or more first language words in the software resource and on data from a global dictionary, and compressing the localized software resource based on the local dictionary.

Abstract: A system and method for masking an identity of a speaker of natural language speech, such as speech clips to be labeled by humans in a system generating voice transcriptions for training an automatic speech recognition model. The natural language speech is morphed prior to being presented to the human for labeling. In one embodiment, morphing comprises pitch shifting the speech randomly either up or down, then frequency shifting the speech, then pitch shifting the speech in a direction opposite the first pitch shift.

Abstract: A system can be configured to perform tasks such as converting recorded speech to a sequence of phonemes that represent the speech, converting an input sequence of graphemes into a target sequence of phonemes, translating an input sequence of words in one language into a corresponding sequence of words in another language, or predicting a target sequence of words that follow an input sequence of words in a language (e.g., a language model). In a speech recognizer, the RNN system may be used to convert speech to a target sequence of phonemes in real-time so that a transcription of the speech can be generated and presented to a user, even before the user has completed uttering the entire speech input.

Abstract: An information processing apparatus including an output control unit that controls display of a user interface related to a recognition application. The output control unit causes a visual effect to be output to an input field to which a recognition result is input, the visual effect indicating a state related to recognition. Also provided is an information processing method including controlling, by a processor, display of a user interface related to a recognition application. Controlling of the display further includes causing a visual effect to be output to an input field to which a recognition result is input, the visual effect indicating a state related to recognition.

Abstract: The present invention relates to a real-time voice recognition apparatus equipped with an application-specific integrated circuits (ASIC) chip and a smartphone, capable, by using one smartphone and one ASIC chip and without using a cloud computer, of assuring personal privacy, and, due to a short delay time, enabling real-time conversion of voice input signals into text for output. When one DRAM chip is optionally added to the real-time voice recognition apparatus, the number of neural network layers is increased thereby significantly improving accuracy of conversion of voice input signals into text.

Abstract: Disclosed is a method and an apparatus for recognizing speech, and the method comprises: separating an input audio signal into at least two separated signals; generating a denoised signal at a current frame; performing a preliminary recognition on each interesting signal at the current frame; and performing a recognition decision according to a recognition score of each interesting signal at the current frame. The method and apparatus of the present disclosure deeply integrate an array signal processing and a speech recognition and use multiway recognitions such that a good recognition rate may be obtained even in a case of a low signal-to-noise ratio.

Abstract: The problem relates to making a user grasp notification contents more effectively. There is provided an information processing device including a control unit that controls information notification to a user based on notification contents, in which the control unit determines an output position of a subject in the notification contents on the basis of a calculated attention acquisition difficulty level related to the user. In addition, there is provided an information processing method including controlling, by a processor, information notification to a user based on notification contents, in which the controlling further includes determining an output position of a subject in the notification contents on the basis of a calculated attention acquisition difficulty level related to the user.

Abstract: In one embodiment, a method includes receiving a request to identify a similarity in public sentiments for each pair from a plurality of entities from a second computing device, where the request includes names of the plurality of entities, accessing a table of word vector relationships, where the table of word vector relationships includes a plurality of unique n-grams and their corresponding word vectors, and where each of the word vectors represents a semantic context of a corresponding n-gram as a point in a d-dimensional embedding space, looking up word vectors corresponding to each of the names using the table, calculating, for each of the word vectors, a similarity metric to each of the word vectors, and sending a response message to the second computing device, where the response message includes calculated similarity metrics corresponding to all the pairs of the word vectors.

Abstract: Features are disclosed for processing a user utterance with respect to multiple subject matters or domains, and for selecting a likely result from a particular domain with which to respond to the utterance or otherwise take action. A user utterance may be transcribed by an automatic speech recognition (“ASR”) module, and the results may be provided to a multi-domain natural language understanding (“NLU”) engine. The multi-domain NLU engine may process the transcription(s) in multiple individual domains rather than in a single domain. In some cases, the transcription(s) may be processed in multiple individual domains in parallel or substantially simultaneously. In addition, hints may be generated based on previous user interactions and other data. The ASR module, multi-domain NLU engine, and other components of a spoken language processing system may use the hints to more efficiently process input or more accurately generate output.

Abstract: Systems and methods for media playback via a media playback system include (i) capturing a voice input comprising a request for media content, (ii) receiving information derived at least from the request for media content, (iii) requesting and receiving information from at least one remote computing device associated with a first media content service and at least one remote computing device associated with a second media content service, wherein (a) the information identifies first media content available via the first media content service for playback and identifies second media content available via the second media content service for playback, and (b) the first and second media content are related to the requested media content, and (iv) after receiving at least one of the first information and the second information, (a) selecting the first media content instead of the second media content, and (b) playing back the first media content.

Abstract: Systems and methods for utilizing a cognitive device are disclosed. A method includes: receiving, by a computer device, a query from a cognitive device; processing, by the computer device, the query to generate a processed query; transmitting, by the computer device, the processed query to a mobile device; receiving, by the computer device, an action query result from the mobile device based on the mobile device receiving the processed query and performing an action query; transmitting, by the computer device, the action query result to the cognitive device based on receiving the action query result.

Abstract: A media generating and editing system that generates audio playback in alignment with text that has been automatically transcribed from the audio. A transcript data file that includes a plurality of text words transcribed from audio words included in the audio data is stored. Timing data is paired with the text words indicating locations in the audio data of the corresponding audio words from which the text words are transcribed. The audio data is provided for playback at a user device. The text words are displayed on a display screen at a user device and a visual marker is displayed on the display screen to indicate the text words on the display screen in time alignment with the audio playback of the corresponding audio words at the user device. The text words in the transcript data file are amended in response to inputs from the user device.

Abstract: A device includes a screen and one or more processors configured to provide, at the screen, a graphical user interface (GUI) configured to display data associated with multiple devices on the screen. The GUI is also configured to illustrate a label and at least one control input for each device of the multiple devices. The GUI is also configured to provide feedback to a user. The feedback indicates that a verbal command is not recognized with an action to be performed. The GUI is also configured to provide instructions for the user on how to teach the one or more processors which action is to be performed in response to receiving the verbal command.