Abstract: This application relates generally to modifying visual data based on audio commands and more specifically, to performing complex operations that modify visual data based on one or more audio commands. In some embodiments, a computer system may receive an audio input and identify an audio command based on the audio input. The audio command may be mapped to one or more operations capable of being performed by a multimedia editing application. The computer system may perform the one or more operations to edit to received multimedia data.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: obtaining a style feed including a plurality of original works by an author. An author-style model for the author is built based on the style feed by use of a selected neural network, and a publication is generated in the style of the author based on the author-style model.

Abstract: A computer-implemented method may include obtaining, by processor, a first message composed in a first language and obtaining, by the processor, a translated first message. The translated first message may include a translation of the first message from the first language to a second language. The method may further include determining, by the processor, that the translated first message includes a translation-generated additional meaning, and notifying, by the processor, the first user of the determination.

Abstract: The gain of an amplifier in a receiver operating in a cellular communication system is controlled by determining one or more gain variability metrics, which are then used to produce first and second threshold values. A frequency difference between a current carrier frequency and a target carrier frequency is ascertained and then compared to the threshold values. Target gain setting production is based on comparison results: If the frequency difference is larger than the first threshold, a first automatic gain control algorithm is performed; if the frequency difference is smaller than the first threshold and larger than the second threshold, a second automatic gain control algorithm is performed, wherein the second automatic gain control algorithm uses a current gain setting as a starting point; and if the frequency difference is smaller than both the first and second thresholds, the current gain setting is used as the target gain setting.

Abstract: A computer-implement voice command authentication method is provided. The method includes obtaining a sound signal stream; calculating a Signal-to-Noise Ratio (SNR) value of the sound signal stream; converting the sound signal stream into a Mel-Frequency Cepstral Coefficients (MFCC) stream; calculating a Dynamic Time Warping (DTW) distance corresponding to the MFCC stream according to the MFCC stream and one of a plurality of sample streams generated by the Gaussian Mixture Model with Universal Background Model (GMM-UBM); calculating, according to the MFCC stream and the sample streams, a Log-likelihood ratio value corresponding to the MFCC stream as a GMM-UBM score; determining whether the sound signal stream passes a voice command authentication according to the GMM-UBM score, the DTW distance and the SNR value; in response to determining that the sound signal stream passes the voice command authentication, determining that the sound signal stream is a voice stream spoken from a legal user.

Abstract: A method includes receiving a voice input at an electronic device. An ambiguity of the voice input is determined. The ambiguity is resolved based on contextual data. The contextual data includes at least one of: an image, a non-voice input comprising a gesture, a pointer of a pointing device, a touch, or a combination thereof.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for controlling a page. A specific embodiment of the method comprises: receiving voice information from a terminal and element information of at least one element in a displayed page; performing voice recognition on the voice information to acquire a voice recognition result, in response to determining the voice information being used for controlling the displayed page; matching the voice recognition result with the element content information of the at least one element; and generating page control information in response to determining successfully matching the voice recognition result with the element content information of the at least one element, and sending the page control information to the terminal to allow the terminal to control the displayed page based on the page control information.

Abstract: Voice control in a multi-talker and multimedia environment is disclosed. In one aspect, there is provided a method comprising: receiving a microphone signal for each zone in a plurality of zones of an acoustic environment; generating a processed microphone signal for each zone in the plurality of zones of the acoustic environment, the generating including removing echo caused by audio transducers in the acoustic environment from each of the microphone signals, and removing interference from each of the microphone signals; and performing speech recognition on the processed microphone signals.

Abstract: A masking system prevents a human agent from receiving sensitive personal information (SPI) provided by a caller during caller-agent communication. The masking system includes components for detecting the SPI, including automated speech recognition and natural language processing systems. When the caller communicates with the agent, e.g., via a phone call, the masking system processes the incoming caller audio. When the masking system detects SPI in the caller audio stream or when the masking system determines a high likelihood that incoming caller audio will include SPI, the caller audio is masked such that it cannot be heard by the agent. The masking system collects the SPI from the caller audio and sends it to the organization associated with the agent for processing the caller's request or transaction without giving the agent access to caller SPI.

Abstract: A computer-implemented method for training a neural network that is configured to generate a score distribution over a set of multiple output positions. The neural network is configured to process a network input to generate a respective score distribution for each of a plurality of output positions including a respective score for each token in a predetermined set of tokens that includes n-grams of multiple different sizes. Example methods described herein provide trained neural networks which produce results with improved accuracy compared to the state of the art, e.g. translations that are more accurate compared to the state of the art, or more accurate speech recognition compared to the state of the art.

Abstract: Methods, systems, and computer program products are provided for language entity identification. In one embodiment, a computer-implemented method is disclosed. In the method, respective pinyin codes may be determined for respective Chinese characters comprised in a string that is to be processed. Then, respective pinyin features may be generated from the respective pinyin codes. Next, a candidate language entity may be identified from the string based on the respective pinyin features and a mapping describing an association between pinyin features and language entity. In other embodiments, a computer-implemented system and a computer program product for security management are disclosed.

Abstract: The present disclosure discloses method and a user intent identification system for identifying user intent from user statements. The user intent identification system receives input statement provided by a user from a Natural Language Understanding (NLU) engine. The input statement is processed to remove one or more irrelevant content. A plurality of features for each word in the processed input statement is extracted. The plurality of features comprises Parts of Speech (POS) label, dependency parse tree and word embeddings. The user intent determination system predicts class for each word in the processed input statement from a plurality of predefined classes using a neural network model. The neural network model predicts class for each word based on input vector generated for the each word based on the plurality of features. Thereafter, the user intent is identified based on class predicted for each word in processed input statement.

Abstract: Techniques related to keyphrase detection for applications such as wake on voice are disclosed herein. Such techniques may have high accuracy by using scores of phone positions in triphones to select which triphones to use with a rejection model, using context-related phones for the rejection model, adding silence before keyphrase sounds for a keyphrase model, or any combination of these.

Abstract: The present disclosure relates to an image processing device, an operation method of the image processing device, and a computer-readable recording medium. The image processing device according to an embodiment in the present disclosure may comprise: a voice-obtaining unit for obtaining the voice of a user and generating a first voice signal; a communication interface unit for receiving a second voice signal of the user from an external device; and a processor which, after the first voice signal is received from the voice-obtaining unit, performs a first pre-processing operation employing voice amplification of the received first voice signal, and, after the second voice signal is received via the communication interface unit, performs a second pre-processing operation employing noise amplification of the second voice signal.

Abstract: The invention discloses a training method and a speech recognition method for a mixed frequency acoustic recognition model, which belongs to the technical field of speech recognition.

Abstract: A computer-implemented method comprises: receiving a first address and a second address, the first address including n morphemes and the second address including m morphemes, wherein a morpheme is a smallest semantic unit in the address, and wherein n and m are both natural numbers; determining first correlation values between the n morphemes and the m morphemes; obtaining, based on the first correlation values and a preset algorithm, a second correlation value between the first address and the second address; and analyzing a correlation between the first address and the second address based on the second correlation value.

Abstract: Agents are coached to improve their performance by participation in a speech coaching campaign. In one embodiment, an administrator identifies top and bottom performing agents, and retrieves their voice call recordings that are processed by a speech analytics system to produce word clouds corresponding to desirable and undesirable phrases. After reviewing and potentially editing the word clouds, a set of desirable and undesirable operational phrases are created, which the agent should use, or not use, during a call. A speech analytics system is configured to detect the presence of these operational phrases for an agent when the agent is on a call. The agent may review information depicting how well they are utilizing the desirable phrases and avoiding the undesirable phrases, and points may be allocated reflecting the agent's usage. The points may be processed by a gamification system to incentivize the agent to improve their performance.

Abstract: A system for artificial intelligent dispute resolution is disclosed. The system may receive a dispute initiation request from a voice input channel. The system may determine user authentication state in response to the dispute initiation request. The system may receive a natural language problem statement from the voice input channel. The system may determine a user intent in response to the natural language problem statement. The system may compare the user intent with a business rules set and determine a dispositioned outcome based on the business rules set and the user intent.

Abstract: Techniques are provided for dynamic adaptation of language understanding systems to acoustic environments. A methodology implementing the techniques according to an embodiment includes generating a trigger in response to recognition of a wake-on-voice key-phrase in or prior to an audio stream. The trigger serves to switch processing modes from an adaptation mode to a query recognition mode. The method further includes performing automatic speech recognition on the audio stream during the query recognition mode, to recognize an in-domain query. The method further includes applying both a static language understanding classifier and a dynamic language understanding classifier to the recognized in-domain query. The static language understanding classifier employs a static semantic model and the dynamic language understanding classifier employs a dynamic semantic model.

Abstract: A method and system may select an interaction involving an agent, sending the selected interaction and a computerized form to the agent and an evaluator, simultaneously or concurrently, displaying to the evaluator and agent screens defined by the form, each screen including an evaluation question, accepting from the agent, for each evaluation question, an agent answer having associated with the agent answer a rating, accepting from the evaluator a submission indicating that the evaluator has completed the computerized evaluation form, accepting from the agent a submission indicating that the agent has completed the computerized evaluation form, summing an agent rating from the ratings associated with the agent answers provided by the agent, summing an evaluator rating from the ratings associated with evaluator answers provided by the evaluator, and calculating a variance from the agent rating and evaluator rating.