A voice output is produced by a speech dialogue device between the acquisitions of two pieces of information. Each piece of information is acquired by acquiring natural verbal voice input data and extracting the respective piece of information from the voice input data using a speech recognition algorithm. When a repetition condition has been satisfied, a natural speech summary output is generated by the speech dialogue device and output as a voice output which includes a natural voice reproduction of at least one previously acquired piece of information or a part of this piece of information or a piece of information derived from this piece of information.

Contextual note taking is described. A note taking assistant can receive an indication of a specific presentation session. This indication can be used by the note taking assistant to access information or content related to the session. The note taking assistant can receive specific presentation session content, which includes identifiable context images. Identifiable context images are meant to define an individual page, an individual slide, or other atomic unit in the presentation. The note taking assistant operates by receiving a navigation message, changing the current assistant context image to a current presenter context image based on the navigation message; receiving a speech-to-text message comprising a unit of text; displaying the current presenter context image, and displaying the unit of text associated with the current presenter context image; and storing the unit of text associated with the current presenter context image.

Techniques performed by a data processing system for processing voice content received from a user herein include receiving a first audio input from the user comprising a mixed-mode dictation, analyzing, using one or more machine learning (ML) models, the first audio input to obtain a first interpretation of the mixed-mode dictation, presenting the first interpretation to the user in an application on the data processing system, receiving a second audio input from the user comprising a corrective command, analyzing the second audio input to obtain a second interpretation of the restatement of the mixed-mode dictation presenting the second interpretation to the user, receiving an indication from the user that the second interpretation is a correct interpretation of the mixed-mode dictation, and modifying the operating parameters of the one or more machine learning models to interpret the subsequent instances of the mixed-mode dictation based on the second interpretation.

An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to select between forwarding a request to an agent device and transmitting an automatically generated reply to the request. The processor receives feedback for a decision made by the algorithm, indicating whether the automatically generated reply includes the information sought by the request. If the algorithm decided to forward the request to the agent device, a reward is assigned to feedback that indicates that the reply does not include the information, while a punishment is assigned to feedback that indicates that the reply includes the information. If the algorithm decided to transmit the reply, a reward is assigned to feedback that indicates that the reply includes the information, and a punishment is assigned to feedback that indicates that the reply does not include the information. The processor updates the algorithm using the reward/punishment.

Methods and systems for manual and programmatic remediation of websites. JavaScript code is accessed by a user device and optionally calls TTS, ASR, and RADAE modules from a remote server to thereby facilitate website navigation by people with diverse abilities.

The invention relates to a computer-implemented method for converting speech to text. The method comprises: receipt (102) of a speech signal (206), which contains general language terms and technical language terms; input (104) of the received speech signal into a speech-to-text conversion system (226), which only supports the conversion of speech signals into a target vocabulary (234) which does not contain the technical language terms; receipt (106) of a text (208), which was generated by the speech-to-text conversion system from the speech signal; generation (108) of a corrected text (210) by automatically replacing terms and expressions from the target vocabulary in the received text with technical language terms according to an assignment table (238), which assigns at least one term or one expression from the target vocabulary, incorrectly recognized by the speech-to-text conversion system, to each of a plurality of technical language terms; and output (110) of the corrected text to the user or to software and/or a hardware component for executing a function.

Systems, devices, and methods transcribe words recorded in audio data. A computer-generated transcript is provided. The transcript comprises records for each word in the computer-generated transcript. At least one confirmation input is received for each record. The at least one confirmation input modifies a selected record and automatically identifies a next record for receiving a next confirmation input. A sequence of confirmation inputs may rapidly modify and validate each record in a sequence of records in the computer-generated transcript. A validated transcript is generated from the modified records and is provided from an evidence management system.

Methods and systems for manual and programmatic remediation of websites. JavaScript code is accessed by a user device and optionally calls TTS, ASR, and RADAE modules from a remote server to thereby facilitate website navigation by people with diverse abilities.

A summary generating device includes a featural script extracting unit, a segment candidate generating unit, and a structuring estimating unit. The featural script extracting unit extracts featural script information of the words included in text information. Based on the extracted feature script information, the segment candidate generating unit generates candidates of segments that represent the constitutional units for the display purpose. Based on the generated candidates of segments and based on an estimation model for structuring, the structuring estimating unit estimates structure information containing information ranging from information of a comprehensive structure level to information of a local structure level.

Utilizing a voice capturing device (e.g., smart phone, tablet, smart speaker) to capture voice commands and send the voice commands to a cloud based voice recognition/processing engine to convert the commands to text commands. Processing the text commands at an access point for a WiFi network. The voice commands may include search queries about particular wireless devices that are associated with the WiFi network. The access point may search the configuration and connectivity data for the WiFi network to determine what access point the wireless device is connected to and a location for the access point. The result of the search may be announced to the user via the voice capturing device. The voice activated search may be to find wireless devices that have misplaced or for inventory management. The voice activated commands may also include voice WiFi network configuration commands.