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Updated: Jun 2, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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A comprehensive voice dataset for Hindko digit recognition.

Tanveer Ahmed1, Maqbool Khan1,2, Khalil Khan3

  • 1Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Haripur, Pakistan.

Data in Brief
|January 15, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new voice recognition dataset for the Hindko language, featuring 17,597 voice samples. This resource aims to improve digital inclusion and language preservation for Hindko speakers.

Keywords:
Artificial intelligenceMachine learningNatural language processingSignal processingVoice recognition

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Area of Science:

  • Computational Linguistics
  • Speech Technology
  • Language Preservation

Background:

  • Hindko is a significant language in Pakistan, spoken by approximately 8 million people, primarily in Khyber Pakhtunkhwa.
  • The Hazara region is the cultural center for Hindko speakers, with high prevalence in districts like Haripur, Abbotabad, and Mansehra.
  • Despite its widespread use across various domains, Hindko currently lacks a dedicated voice recognition system, hindering digital accessibility and preservation efforts.

Purpose of the Study:

  • To address the absence of a voice recognition system for the Hindko language.
  • To present a publicly accessible Hindko voice recognition dataset for academic and research use.
  • To facilitate advancements in natural language processing and digital inclusion for Hindko speakers.

Main Methods:

  • Compilation of a comprehensive voice recognition dataset.
  • Collection of 17,597 voice samples covering 20 Hindko digits (1-20).
  • Sourcing voice samples from students, staff, and faculty at Pak-Austria Fachhochschule Institute of Applied Science and Technology.

Main Results:

  • A novel Hindko voice recognition dataset with 17,597 voice samples has been created.
  • The dataset includes recordings of Hindko digits from 1 to 20.
  • The dataset is made publicly available for research and academic purposes.

Conclusions:

  • The developed Hindko voice recognition dataset is a crucial resource for advancing speech technology for the language.
  • This initiative supports the preservation of Hindko and promotes digital inclusion for its speakers.
  • Further research utilizing this dataset can lead to enhanced accessibility and broader applications of voice technology for Hindko.