DOTA-ME-CS: Daily Oriented Text Audio-Mandarin English-Code Switching Dataset

TL;DR

This paper introduces DOTA-ME-CS, a comprehensive daily-oriented Mandarin-English code-switching speech dataset with AI-enhanced diversity, aiming to advance bilingual speech recognition research.

Contribution

The paper presents a new large-scale, diverse code-switching speech dataset with AI-augmented data, filling a gap in resources for bilingual ASR research.

Findings

Dataset contains 18.54 hours of audio from 34 participants.

AI techniques increase dataset diversity and complexity.

Dataset and code will be publicly available.

Abstract

Code-switching, the alternation between two or more languages within communication, poses great challenges for Automatic Speech Recognition (ASR) systems. Existing models and datasets are limited in their ability to effectively handle these challenges. To address this gap and foster progress in code-switching ASR research, we introduce the DOTA-ME-CS: Daily oriented text audio Mandarin-English code-switching dataset, which consists of 18.54 hours of audio data, including 9,300 recordings from 34 participants. To enhance the dataset's diversity, we apply artificial intelligence (AI) techniques such as AI timbre synthesis, speed variation, and noise addition, thereby increasing the complexity and scalability of the task. The dataset is carefully curated to ensure both diversity and quality, providing a robust resource for researchers addressing the intricacies of bilingual speech recognition with detailed data analysis. We further demonstrate the dataset's potential in future research. The DOTA-ME-CS dataset, along with accompanying code, will be made publicly available.