Comparison of AI Speech-to-Text Systems and Their Application in Artillery Command and Fire Control Systems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG42__%2F26%3A00564679" target="_blank" >RIV/60162694:G42__/26:00564679 - isvavai.cz</a>

Result on the web

<a href="https://www.springer.com/series/7899" target="_blank" >https://www.springer.com/series/7899</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-032-04339-9_6" target="_blank" >10.1007/978-3-032-04339-9_6</a>

Result language

angličtina

Original language name

Comparison of AI Speech-to-Text Systems and Their Application in Artillery Command and Fire Control Systems

Original language description

This paper presents a comparative analysis of three leading AI speech-to-text (STT) systems: Descript.com, Google Vertex AI Studio (Chirp), and OpenAI Whisper. The objective of the study is to evaluate the accuracy, functionality, and potential applications of these technologies, with a particular focus on their integration into artillery command and fire control systems. The analysis outlines the evolution of speech recognition technologies, from traditional methods based on Hidden Markov Models (HMMs) to modern deep neural networks, including Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformer-based architectures. Practical testing was conducted on a dataset of English and Czech recordings with varying audio quality. The results indicate that Google Chirp achieves the highest accuracy in English transcriptions, while OpenAI Whisper demonstrates superior performance for the Czech language. Additionally, the paper explores the optimization of STT systems for combat environments, including the use of Ant Colony Optimization (ACO) algorithms to minimize errors and enhance the relevance of transcriptions. The study also highlights security risks associated with deploying cloud-based STT services in military applications and emphasizes the advantages of on-premise solutions to ensure data protection. Finally, the paper discusses strategies for modernizing defense capabilities through AI technologies. It advocates for increased investment in automated command and control systems, fire control, and situational awareness, emphasizing their crucial role in improving response times and the accuracy of artillery fire support.

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Communications in Computer and Information Science

ISBN

978-3-032-04338-2

ISSN

1865-0929

e-ISSN

1865-0937

Number of pages

14

Pages from-to

82-95

Publisher name

SPRINGER INTERNATIONAL PUBLISHING AG

Place of publication

Bilbao

Event location

Bilbao, SPAIN

Event date

Jun 12, 2025

Type of event by nationality

CST - Celostátní akce

UT code for WoS article

001696487100006