Version 1
: Received: 6 October 2023 / Approved: 11 October 2023 / Online: 11 October 2023 (06:53:39 CEST)
Version 2
: Received: 6 November 2023 / Approved: 6 November 2023 / Online: 7 November 2023 (02:34:57 CET)
How to cite:
Imam, M.; Gupta, G. Precision Location Keyword Detection Using Offline Speech Recognition Technique. Preprints2023, 2023100690. https://doi.org/10.20944/preprints202310.0690.v2
Imam, M.; Gupta, G. Precision Location Keyword Detection Using Offline Speech Recognition Technique . Preprints 2023, 2023100690. https://doi.org/10.20944/preprints202310.0690.v2
Imam, M.; Gupta, G. Precision Location Keyword Detection Using Offline Speech Recognition Technique. Preprints2023, 2023100690. https://doi.org/10.20944/preprints202310.0690.v2
APA Style
Imam, M., & Gupta, G. (2023). Precision Location Keyword Detection Using Offline Speech Recognition Technique<strong> </strong>. Preprints. https://doi.org/10.20944/preprints202310.0690.v2
Chicago/Turabian Style
Imam, M. and Gaurav Gupta. 2023 "Precision Location Keyword Detection Using Offline Speech Recognition Technique<strong> </strong>" Preprints. https://doi.org/10.20944/preprints202310.0690.v2
Abstract
This study introduces an original comprehensive system centered on identifying specific terms that indicate a user's position, particularly the discrete values representing latitude and longitude. This system not only detects these terms but also retrieves the corresponding numerical data for accurate and efficient determination of locations. The importance of this study can be applied various fields, notably aiding offline operations of military personnel, who often lack internet access. In such scenarios, precise awareness of location is vital for strategic manoeuvres, rescue operations, and navigating unfamiliar landscapes. The system allows these personnel by allowing them to extract exact location coordinates from spoken terms, thereby enhancing their awareness even in challenging surroundings. Apart from its military utility, the project holds broader significance. Teams responding to emergencies, personnel involved in disaster management, and exploratory missions can all gain from this technology during disruptions in communication infrastructure. Furthermore, travelers, adventurers, and outdoor enthusiasts can utilize this system to accurately determine their positions in remote areas without relying on online maps. We used offline speech recognition techniques to precisely transcribe spoken terms, achieving an accuracy of over 91.3% and a word error rate of 4.2%. For sound recognition, the OpenAI Whisper model was used, and a conversion process from SpeechRecognition to AudioSegmentation was implemented, followed by transforming the audio into .wav format, we have also developed the interface of the app to use it efficiently using Streamlit. This was done to ensure seamless compatibility with the Whisper model and uninterrupted audio input. By training the system to identify specific linguistic linked to location, it achieves robust detection and extraction of relevant terms. This approach eliminates the necessity for constant internet connectivity, rendering it exceptionally useful in remote, offline, and resource-limited situations.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Mohsin Imam
Commenter's Conflict of Interests: Author