Microsoft Technology Licensing, LLC

Tellme Networks, Inc.

Shuangyu Chang

Michael Levit

Benoit F Dumoulin

Ziad Al Bawab

Bruce Melvin Buntschuh

Anand U Desai

Amit Kumar Agarwal

Christopher Hakan Basoglu

Piyush Behre

Sarangarajan Parthasarathy

Nicholas Kibre

Padma Varadharajan

Zoltan Romocsa

Wei Liu

Khuram Shahid

Marcus Jon Jager

Sayan Dev Pathak

Issac Alphonso

Heiko Rahmel

Edward C Lin

Hemant Malhotra

Rishi Girish

Hernan Guelman

Xin Meng

Suyash Choudhury

Abhik Lahiri

Dhiresh Rawal

Barlas Oguz

Tao Li

Nathan E Wohlgemuth

Ananth Rampura Sheshagiri Rao

Cem Aksoylar

Che Zhao

Xuedong D Huang

Jinyu Li

Yifan Gong

Dong Li

Robert L Chambers

Hosam Adel Khalil

Geoffrey G Zweig

Andreas Stolcke

Ye Tian

Li Jiang

Pradip Kumar Fatehpuria

Mark T Hanson

Yashesh Gaur

Ranjitha Gurunath Kulkarni

Jian Xue

Jonathan E Hamaker

Rupeshkumar Rasiklal Mehta

Nick Kibre

Anastasios Anastasakos

Veljko Miljanic

Ankur Gupta

Shiun-Zu Kuo

Satarupa Guha

Amy Parag Shah

Ravikant Gupta

Andrew K Krumel

Sharman W Tan

Nitin Kamra

Thomas Reutter

Qi Miao

Yuancheng Tu

Aadyot Bhatnagar

Meryem Pinar Donmez Ediz

Ivo Jose Garcia Dos Santos

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 generate segments of spoken language utterances based on different sets of segmentation boundaries. The systems are also configured to generate one or more formatted segments by assigning a punctuation tags at segmentation boundaries and to generate one or more final sentences from the one or more segments.

Streaming punctuation for long-form dictation

Solutions for on-device streaming inverse text normalization (ITN) include: receiving a stream of tokens, each token representing an element of human speech; tagging, by a tagger that can work in a streaming manner (e.g., a neural network), the stream of tokens with one or more tags of a plurality of tags to produce a tagged stream of tokens, each tag of the plurality of tags representing a different normalization category of a plurality of normalization categories; based on at least a first tag representing a first normalization category, converting, by a first language converter of a plurality of category-specific natural language converters (e.g., weighted finite state transducers, WFSTs), at least one token of the tagged stream of tokens, from a first lexical language form, to a first natural language form; and based on at least the first natural language form, outputting a natural language representation of the stream of tokens.

On-device streaming inverse text normalization (ITN)

A computing system obtains features that have been extracted from an acoustic signal, where the acoustic signal comprises spoken words uttered by a user. The computing system performs automatic speech recognition (ASR) based upon the features and a language model (LM) generated based upon expanded pattern data. The expanded pattern data includes a name of an entity and a search term, where the entity belongs to a segment identified in a knowledge base. The search term has been included in queries for entities belonging to the segment. The computing system identifies a sequence of words corresponding to the features based upon results of the ASR. The computing system transmits computer-readable text to a search engine, where the text includes the sequence of words.

Scalable entities and patterns mining pipeline to improve automatic speech recognition

Embodiments are provided for securing data access to machine learning training data at a plurality of distributed computing devices. Electronic content including original data that corresponds to a preferred data security level is divided into a plurality of microsegments. The plurality of microsegments is restrictively distributed to a plurality of computing devices which apply transcription labels to the plurality of microsegments. The labeled microsegments are reconstructed into training data which is then used to train a machine learning model while facilitating an improvement in data security of the original data included with the training data from the reconstructed microsegments.

Microsegment secure speech transcription

A data processing system implements obtaining a first textual content, segmenting the first textual content into a plurality of first segments, and providing each segment of the plurality of first segments to a first natural language processing (NLP) model to obtain a set of first readability scores for the plurality of first segments. The first NLP model is configured to analyze a textual input and to output a readability score representing a measurement of readability of the textual input. The system further implements aggregating the set of first segment readability scores to determine a first readability score for the first textual content, and perform at least one of causing the first readability score to be presented to a user or performing one or more actions on the first textual content based on the readability score.

Automated artificial intelligence driven readability scoring techniques

Systems and methods are provided for acquiring training data and building an organizational-based language model based on the training data. In organizational data is generated via one or more applications associated with an organization, the collected organizational data is aggregated and filtered into training data that is used for training an organizational-based language model for speech processing based on the training data.

Organizational-based language model generation

A system includes acquisition of meeting data associated with a meeting, determination of a plurality of meeting participants based on the acquired meeting data, acquisition of e-mail data associated with each of the plurality of meeting participants, generation of a meeting language model based on the acquired e-mail data and the meeting data, and transcription of audio associated with the meeting based on the meeting language model.

Meeting-adapted language model for speech recognition

A system includes acquisition of a domain grammar, determination of an interpolated grammar based on the domain grammar and a base grammar, determination of a delta domain grammar based on an augmented first grammar and the interpolated grammar, determination of an out-of-vocabulary class based on the domain grammar and the base grammar, insertion of the out-of-vocabulary class into a composed transducer composed of the augmented first grammar and one or more other transducers to generate an updated composed transducer, composition of the delta domain grammar and the updated composed transducer, and application of the composition of the delta domain grammar and the updated composed transducer to an output of an acoustic model.

Online language model interpolation for automatic speech recognition

Provided is a system and method for acquiring training data and building an organizational-based language model based on the training data. In one example, the method may include collecting organizational data that is generated via one or more applications associated with an organization, aggregating the collected organizational data with previously collected organizational data to generate aggregated organizational training data, training an organizational-based language model for speech processing based on the aggregated organizational training data, and storing the trained organizational-based language model.

A method for eyes-off training of a dictation system includes translating an audio signal featuring speech audio of a speaker into an initial recognized text using a previously-trained general language model. The initial recognized text is provided to the speaker for error correction. The audio signal is re-translated into an updated recognized text using a specialized language model biased to recognize words included in the corrected text. The general language model is retrained in an 'eyes-off' manner, based on the audio signal and the updated recognized text.

Eyes-off training for automatic speech recognition

Disclosed in various examples are methods, systems, and machine-readable mediums for providing improved computer implemented speech recognition by detecting and correcting speech recognition errors during a speech session. The system recognizes repeated speech commands from a user in a speech session that are similar or identical to each other. To correct these repeated errors, the system creates a customized language model that is then utilized by the language modeler to produce a refined prediction of the meaning of the repeated speech commands. The custom language model may comprise clusters of similar past predictions of speech commands from the speech session of the user.

Correction of speech recognition on repetitive queries

The customization of language modeling components for speech recognition is provided. A list of language modeling components may be made available by a computing device. A hint may then be sent to a recognition service provider for combining the multiple language modeling components from the list. The hint may be based on a number of different domains. A customized combination of the language modeling components based on the hint may then be received from the recognition service provider.

Flexible schema for language model customization

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Fremont, CA, United States of America

Shuangyu Chang