20240021204. SYSTEM AND METHOD FOR TRANSCRIPTION WORKFLOW simplified abstract (VIQ Solutions Inc.)

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SYSTEM AND METHOD FOR TRANSCRIPTION WORKFLOW

Organization Name

VIQ Solutions Inc.

Inventor(s)

Thomas Deplonty of Mississauga (CA)

Gilles-André Morin of Mississauga (CA)

SYSTEM AND METHOD FOR TRANSCRIPTION WORKFLOW - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240021204 titled 'SYSTEM AND METHOD FOR TRANSCRIPTION WORKFLOW

Simplified Explanation

The abstract describes a system, methods, and computer-readable storage media for assigning different speech-to-text engines based on previous transcription scores. The system can train a model using a first digital audio recording by randomly assigning speech-to-text engines to transcribe it and scoring the resulting transcriptions and the engines. A model is generated to select a speech-to-text engine from within the available engines. When a second digital audio recording is received, the model is executed to assign at least one selected speech-to-text engine to transcribe it.

  • The system trains a model using a first digital audio recording and scores the performance of different speech-to-text engines.
  • A model is generated to select the most suitable speech-to-text engine for transcription.
  • When a second digital audio recording is received, the model is used to assign the best speech-to-text engine for transcription.

Potential applications of this technology:

  • Transcription services: This technology can be used in transcription services to automatically assign the most accurate speech-to-text engine for transcribing audio recordings.
  • Voice assistants: Voice assistants can benefit from this technology by using the most suitable speech-to-text engine for accurately transcribing user commands.
  • Call center operations: Call centers can utilize this technology to improve the accuracy of transcriptions for customer interactions.

Problems solved by this technology:

  • Inaccurate transcriptions: By assigning the most suitable speech-to-text engine based on previous performance, this technology helps solve the problem of inaccurate transcriptions.
  • Time-consuming manual selection: Instead of manually selecting a speech-to-text engine for each audio recording, this technology automates the process, saving time and effort.

Benefits of this technology:

  • Improved transcription accuracy: By selecting the best speech-to-text engine, this technology improves the accuracy of transcriptions, leading to better understanding and analysis of audio recordings.
  • Efficiency and automation: The automated selection of speech-to-text engines streamlines the transcription process, making it more efficient and reducing the need for manual intervention.
  • Cost savings: With improved accuracy and efficiency, organizations can save costs associated with manual transcription or the use of less accurate speech-to-text engines.


Original Abstract Submitted

systems, methods, and computer-readable storage media for making assignments to different speech-to-text engines based on previous transcription scores. an exemplary system can train a model by receiving a first digital audio recording, randomly assigning speech-to-text engines to transcribe the first digital audio recording, and scoring the resulting transcriptions and scoring the engines based on their performances. the system can then generate a model for selecting a speech-to-text engine from within the speech-to-text engines. when a second digital audio recording is received, the system can assign, by executing the model, at least one selected speech-to-text engine from the speech-to-text engines to transcribe the second digital audio recording.

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