Mixat: A Dataset of Bilingual Emirati-English Speech for Studying Code-Switching

Core Concepts

This paper introduces Mixat, a dataset of 15 hours of Emirati speech code-mixed with English, to address the lack of resources for studying code-switching in dialectal Arabic.

Abstract

The Mixat dataset was constructed from two public podcasts featuring native Emirati speakers, one in the form of conversations and the other as structured monologues. The dataset consists of 5,316 utterances, with 1,947 (36%) containing code-switching between Emirati Arabic and English.
The authors provide statistics on the dataset, including the distribution of monolingual and code-switched segments, as well as the average code mixing index (CMI). They also evaluate the performance of pre-trained Arabic and multilingual automatic speech recognition (ASR) systems on the dataset, demonstrating the challenges of recognizing code-switching in low-resource dialectal Arabic.
The Mixat dataset will be made publicly available to support research on code-switching in speech, particularly in the context of Emirati Arabic. The authors highlight the importance of such resources for understanding the linguistic culture of the Emirati population, where code-switching is a prevalent aspect of daily communication.

Stats

The Mixat dataset consists of approximately 15 hours of audio content.
36% of the 5,316 utterances in the dataset contain code-switching between Emirati Arabic and English.
The average code mixing index (CMI) of the code-switched utterances is 0.11.

Quotes

"Code-switching (CS), or code-mixing, refer to the linguistic behavior of alternating between languages within a conversation or an utterance, which is common in multi-cultural, multi-lingual communities."
"In the United Arab Emirates (UAE), where Arabic is the primary local language and English is a widely spoken second language, code-switching and code-mixing have become observable and significant aspects of daily communication."

Key Insights Distilled From

Mixat: A Data Set of Bilingual Emirati-English Speech

by Maryam Al Al... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02578.pdf

Deeper Inquiries

How can the Mixat dataset be used to develop more robust code-switching aware speech recognition models for dialectal Arabic

The Mixat dataset can be instrumental in developing more robust code-switching aware speech recognition models for dialectal Arabic by providing a rich source of annotated data that captures the nuances of Emirati-English code-switching. Researchers can utilize this dataset to train and fine-tune ASR models specifically tailored to recognize and transcribe the unique speech patterns present in the Emirati dialect. By leveraging the Mixat dataset, developers can enhance the performance of ASR systems in accurately identifying code-switching points, distinguishing between Arabic and English segments, and improving overall transcription accuracy in bilingual contexts.
Furthermore, the Mixat dataset offers a diverse range of conversational contexts and linguistic variations, allowing researchers to analyze the intricacies of code-switching behavior among bilingual Emirati speakers. By studying the patterns and frequencies of code-switching in different settings within the dataset, researchers can gain insights into the underlying linguistic mechanisms and sociolinguistic factors that influence code-mixing in the Emirati community. This understanding can inform the design of more sophisticated ASR models that are attuned to the specific language practices of Emirati speakers, leading to more effective speech recognition systems for dialectal Arabic.

What are the sociolinguistic factors that contribute to the prevalence of code-switching among the younger Emirati population, and how can this dataset be used to study these phenomena

The prevalence of code-switching among the younger Emirati population can be attributed to several sociolinguistic factors that shape language use in the UAE. One key factor is the country's multicultural environment, characterized by a diverse expatriate population and the widespread use of English as a second language. The educational system in the UAE also plays a significant role in promoting bilingualism, leading to the seamless integration of Arabic and English in daily communication, especially among the youth.
The Mixat dataset serves as a valuable resource for studying these sociolinguistic phenomena by providing real-world examples of Emirati-English code-switching in natural conversational contexts. Researchers can analyze the dataset to explore the motivations behind code-switching, the linguistic strategies employed by speakers, and the social dynamics that influence language choice. By examining the patterns of code-switching in the dataset, researchers can uncover insights into how language identity, social relationships, and cultural influences intersect to shape language practices among young Emiratis.

What insights can be gained from the Mixat dataset about the linguistic and cultural dynamics of the Emirati community, and how might these insights inform language education and policy in the UAE

The Mixat dataset offers valuable insights into the linguistic and cultural dynamics of the Emirati community, shedding light on the intricate interplay between Arabic and English in everyday communication. By analyzing the patterns of code-switching and code-mixing present in the dataset, researchers can gain a deeper understanding of how language is used as a tool for identity expression, social bonding, and cultural negotiation among Emirati speakers.
These insights can inform language education and policy in the UAE by highlighting the importance of recognizing and valuing the bilingual practices of Emirati youth. Educators can leverage the findings from the Mixat dataset to design language curricula that reflect the linguistic diversity of the Emirati population, incorporating code-switching awareness and proficiency in both Arabic and English. Additionally, policymakers can use the insights from the dataset to advocate for the preservation and promotion of the Emirati dialect within educational and societal contexts, recognizing it as a vital component of the country's linguistic heritage and cultural identity.