Microsoft Technology Licensing, LLC

Heiko Rahmel

Xuedong D Huang

Li-Juan Qin

Wei Liu

Wei Xiong

Shuangyu Chang

Khuram Shahid

Nicholas Kibre

Padma Varadharajan

Edward C Lin

Piyush Behre

Che Zhao

Rachel Imogen Morton

Nicholas J Gedge

Solutions for custom display post processing (DPP) in speech recognition (SR) use a customized multi-stage DPP pipeline that transforms a stream of SR tokens from lexical form to display form. A first transformation stage of the DPP pipeline receives the stream of tokens, in turn, by an upstream filter, a base model stage, and a downstream filter, and transforms a first aspect of the stream of tokens (e.g., disfluency, inverse text normalization (ITN), capitalization, etc.) from lexical form into display form. The upstream filter and/or the downstream filter alter the stream of tokens to change the default behavior of the DPP pipeline into custom behavior. Additional transformation stages of the DPP pipeline perform further transforms, allowing for outputting final text in a display format that is customized for a specific user. This permits each user to efficiently leverage a common baseline DPP pipeline to produce a custom output.

Custom display post processing in speech recognition

Systems, methods, and computer-readable storage devices are disclosed for generating smart notes for a meeting based on participant actions and machine learning. One method including: receiving meeting data from a plurality of participant devices participating in an online meeting; continuously generating text data based on the received audio data from each participant device of the plurality of participant devices; iteratively performing the following steps until receiving meeting data for the meeting has ended, the steps including: receiving an indication that a predefined action has occurred on the first participating device; generating a participant segment of the meeting data for at least the first participant device from a first predetermined time before when the predefined action occurred to when the predefined action occurred; determining whether the receiving meeting data of the meeting has ended; and generating a summary of the meeting.

Systems, methods, and computer-readable storage device for generating notes for a meeting based on participant actions and machine learning

A runtime framework and authoring tool are provided for enabling linguistic experts to author text normalization maps and grammar libraries without requiring high level of technical or programming skills. Authors define or select terminals, map the terminals, and define rules for the mapping. The tool enables an author to validate their work, by executing the map in the same way the recognition engine does, causing consistency in results from authoring to user operations. The runtime is used by the speech engines and by the tools to provide consistent normalization for supported scenarios.

Tool and framework for creating consistent normalization maps and grammars

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Bellevue, WA, United States of America

Heiko Rahmel