Machine-Learning Audio Translation for Accurate Sports Commentary

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Solution Overview

Problem

Manual commentary for sports broadcasts is limited to a single language, making it inaccessible to a broader audience and costly, and lacks accessibility for individuals with hearing impairments, with existing translation technologies failing to accurately translate sports-specific terminology.

Innovation Solution

Utilizing machine-learning models, particularly generative and rephrasing models, to convert audio data from a first language to a second language in real-time, tailoring commentary to individual preferences, and providing accessible text and audio for diverse audiences, including those with hearing impairments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If machine-learning models are used to translate sports commentary into multiple languages, then audience accessibility is improved, but translation accuracy of sports-specific terminology may deteriorate

Engineering Contradiction:

Improvelanguage accessibilityVSAvoidtranslation accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent applies local quality by training machine-learning models specifically on sports commentary data and terminology. Instead of using general translation models, the system creates domain-specific models that understand sports context, player names, team names, and sports-specific vocabulary. This localized training approach ensures accurate translation of sports terminology while maintaining multi-language accessibility.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system incorporates feedback mechanisms where translated commentary can be reviewed and corrected, and this feedback is used to refine the machine-learning models. The system learns from corrections and improvements to enhance translation accuracy over time, particularly for sports-specific terms, while maintaining the ability to translate into multiple languages.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If manual commentary is provided in multiple languages, then audience accessibility is improved, but cost and logistical complexity increase

Engineering Contradiction:

Improvelanguage accessibilityVSAvoidlogistical complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent uses machine-learning models to create synthetic translated commentary that copies the style and structure of original human commentary. Instead of hiring multiple language commentators, the system generates AI-generated commentary in various languages that replicates the original commentary's quality and sports expertise, significantly reducing logistical complexity and costs.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system enables self-service translation where the machine-learning models automatically translate sports commentary in real-time without requiring human intervention for each language version. The system processes and translates commentary autonomously, reducing the need for complex coordination between multiple language teams and simplifying the overall logistical structure.

Inventive Principle:

Principle #25Self-service

3Adaptability or versatility

If real-time translation is implemented, then audience accessibility is improved, but processing time may worsen

Engineering Contradiction:

Improvelanguage accessibilityVSAvoidprocessing time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by pre-training machine-learning models on extensive sports commentary datasets before actual translation is needed. The models are pre-configured with sports terminology and context, so that during real-time broadcasting, they can process and translate commentary quickly without requiring time for initial learning or adaptation, thus minimizing processing delays.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system optimizes processing parameters such as model architecture, computational resources, and translation speed to achieve real-time performance. By adjusting these parameters and using efficient machine-learning algorithms, the system maintains fast processing speeds that enable real-time translation while preserving accuracy, thus reducing the time loss associated with translation operations.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250272517A1Systems and methods for using machine-learning to extract and process audio data

Publication Date: 2025.08.28 STATS LLC

AI summary

A method for extracting and processing audio data may include receiving one or more packets of multimedia content. The one or more packets of multimedia content may comprise audio data. The method may further include extracting the audio data from the one or more packets of multimedia content. The audio data may comprise verbal speech in a first language. The method may further include converting the audio data into first text data in the first language based on the verbal speech in the first language. The method may further include providing the first text data to a generative machine-learning model. The generative machine-learning model may have been trained to translate the first text data in the first language to a second language and generate second text data in the second language. The method may further include transmitting, to a user interface, the second text data in the second language.