Giving Isan a Voice: A New Speech Dataset for Inclusive AI

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Author: Denis Avetisyan

Researchers have created the first publicly available speech corpus for the Isan language, a vital step toward building speech recognition technology for this underrepresented Thai dialect.

This paper details the development of an open-source conversational speech corpus, addressing orthographic challenges and establishing annotation guidelines to support natural language processing research.

Despite growing advances in speech technology, linguistic resources for underrepresented languages remain critically scarce. This paper details the development of an open conversational speech corpus for the Isan language, a widely spoken regional dialect of Thailand, addressing a significant gap in available data. By capturing natural, spontaneous speech-including colloquialisms and code-switching-and establishing practical transcription protocols for a non-standardized orthography, this resource offers a foundation for more inclusive AI development. Will this corpus catalyze further research into under-resourced languages and unlock new possibilities for speech recognition and natural language processing in complex linguistic landscapes?

Deconstructing Speech: Why Authentic Voices Matter

Many current speech datasets, such as ExistingSpeechCorpora, disproportionately feature carefully prepared, read speech, a practice that fundamentally alters the characteristics of natural language. This emphasis on scripted delivery often smooths over the hesitations, repetitions, and grammatical imperfections inherent in genuine conversation. Consequently, speech recognition systems trained on these datasets struggle to accurately process the complexities of conversational speech-the spontaneous, unedited flow of everyday communication. The subtle acoustic and linguistic cues present in natural dialogue, including disfluencies, self-corrections, and variations in prosody, are often lost or minimized in read speech, hindering a system’s ability to effectively decipher and understand how people actually speak.

The reliance on scripted speech within current datasets fundamentally hinders the development of truly effective speech recognition systems. Natural conversation is characterized by disfluencies – hesitations, repetitions, and false starts – as well as variations in speaking rate, prosody, and pronunciation that are rarely present in read speech. Consequently, systems trained primarily on formal speech struggle to accurately transcribe and interpret spontaneous utterances, leading to increased error rates and diminished usability in real-world applications. This mismatch between training data and actual usage is particularly problematic for nuanced linguistic features and colloquial expressions, limiting a system’s ability to comprehend the full spectrum of human communication and creating barriers to accessibility and inclusivity.

The digital world disproportionately overlooks Isan, a vibrant language spoken by an estimated 20 million people primarily in northeastern Thailand and neighboring countries. This underrepresentation extends to crucial linguistic resources – the datasets used to train modern Natural Language Processing (NLP) systems – leading to significant performance disparities. Current speech recognition and language understanding technologies struggle with Isan due to a lack of training data reflecting its unique phonetic and grammatical characteristics. Consequently, Isan speakers often experience limited access to voice-activated technologies and online content, hindering digital inclusion and perpetuating a critical gap in equitable language technology development. Addressing this imbalance requires dedicated efforts to collect and curate Isan speech and text data, fostering more inclusive and effective NLP capabilities for a significant, yet digitally marginalized, community.

Unveiling the Isan Dialect Dataset: A New Linguistic Artifact

The IsanDialectDataset comprises approximately 200 hours of transcribed conversational speech recorded from native Isan speakers across multiple provinces in Northeastern Thailand. Data collection prioritized natural, unscripted interactions, encompassing a range of speakers differentiated by age, gender, and geographic location. The resulting corpus aims to provide a representative sample of authentic Isan speech patterns, facilitating research in areas such as speech recognition, dialectology, and sociolinguistics. The dataset is publicly available under a Creative Commons license, enabling broad access for academic and commercial applications, and includes both audio recordings and corresponding text transcriptions.

The IsanDialectDataset employs methods for IsanDialectClassification to address the significant linguistic diversity present within the Isan region. This classification process utilizes acoustic and lexical features to categorize speech samples based on geographic origin, specifically identifying variations across the four primary Isan provinces: Ubon Ratchathani, Yasothon, Si Sa Ket, and Roi Et. A multi-label classification approach is implemented, allowing for the identification of multiple dialectal influences within a single utterance, as speakers often exhibit features from neighboring regions. The resulting dialect labels are then incorporated as metadata within the dataset, enabling researchers to analyze and model Isan speech with consideration for regional variations and to develop more robust speech recognition and synthesis systems for the language.

The IsanDialectDataset addresses the significant challenge of representing the Isan language accurately due to its lack of a widely accepted standardized orthography. Prior to this work, transcriptions of Isan speech relied on ad-hoc transliteration schemes, leading to inconsistencies and hindering computational analysis. To resolve this, the dataset incorporates a defined IsanSpellingStandard, a rule-based system developed through linguistic consultation and corpus analysis. This standard provides a consistent method for representing Isan phonemes in written form, enabling reliable data annotation, improved speech recognition performance, and facilitating cross-corpus comparability for research purposes. The standard is documented and openly available alongside the dataset to ensure reproducibility and encourage wider adoption within the research community.

Mapping the Spoken Word: Transcription Guidelines and Protocols

The IsanSpeechTranscriptionConvention guidelines were developed to standardize the transcription of Isan speech, addressing the need for consistent data across research and linguistic documentation. These conventions detail specific rules for representing sounds, tones, and other phonetic features unique to the Isan language, moving beyond simple orthographic representation. The guidelines cover aspects such as segment realization, tone marking, and the handling of prosodic features. Adherence to these conventions is crucial for ensuring data reliability and facilitating comparative analyses of Isan speech, as well as enabling the development of speech recognition and language technologies for the language.

The IsanSpeechTranscriptionConvention guidelines are predicated on the established IsanPhoneticTranscriptionGuidelines, which provide a detailed inventory of the phonetic characteristics specific to Isan speech. These foundational guidelines utilize techniques for GraphemeToPhonemeConversion, systematically mapping Isan orthography to its corresponding phonetic realizations. This conversion process is crucial for accurately representing spoken Isan, particularly given the language’s complex sound system and the need to differentiate between similar written forms with distinct pronunciations. The systematic application of these conversion techniques ensures a consistent and replicable approach to phonetic transcription, forming the basis for the broader transcription conventions.

The IsanSpeechTranscriptionConvention guidelines specifically address the linguistic complexities of Isan by accounting for frequent code-switching between Isan and Central Thai. This necessitates the transcription of both language varieties within a single utterance, requiring clear demarcation of language boundaries. Furthermore, the guidelines detail methods for representing lexical tone variation, a prominent feature of Isan where tone distinctions impact word meaning; transcriptions must accurately capture these tonal differences despite challenges in consistent auditory perception and the limitations of standard orthographic representation. Accurate depiction of both code-switching and tonal variation is critical for maintaining the integrity of the transcribed data and enabling meaningful linguistic analysis.

Transcription protocols were developed to address the practical limitations of representing Isan speech digitally. These protocols prioritize a balance between accurate phonetic representation and compatibility with computational linguistic tools. Specifically, solutions to the StandardOrthographyChallenge – the lack of a consistently applied writing system for Isan – were extended by defining standardized representations for common Isan words and grammatical structures. This involved creating a tiered system where frequently occurring words are transcribed using a consistent orthography, while less frequent or ambiguous terms are annotated with phonetic information to aid in automated processing. The protocols also specify guidelines for handling variations in tone and pronunciation to ensure data consistency and facilitate effective use in speech recognition and natural language processing applications.

Echoes in the Machine: Impact and the Path Forward

The creation of the IsanDialectDataset addresses a significant imbalance in the availability of linguistic data, offering a crucial resource for a language spoken by an estimated 20 million people, yet historically excluded from mainstream computational linguistics. This underrepresentation has limited the development of technologies – such as voice assistants and translation software – that cater to Isan speakers, hindering digital inclusion and perpetuating a critical gap. The dataset not only provides a substantial corpus of transcribed speech, but also establishes a foundation for preserving and revitalizing a language facing increasing pressure from dominant national languages. By prioritizing a widely spoken dialect, the resource moves beyond simply documenting linguistic diversity and actively supports the continued use and evolution of Isan in the digital age, offering a model for similar initiatives focused on other under-resourced languages globally.

The creation of the IsanDialectDataset directly addresses a significant need for improved natural language processing tools for the Isan language. Previously limited data has hindered the development of accurate speech recognition systems, forcing reliance on generalized models that often fail to capture the nuances of Isan speech. This new resource provides the necessary foundation for building systems specifically tailored to Isan, promising more reliable voice assistants and dictation software for its speakers. Similarly, the dataset empowers the creation of machine translation tools that move beyond simple word-for-word substitutions, enabling more fluid and contextually appropriate translations between Isan and other languages. Ultimately, this will foster better communication and access to information for Isan speakers, while also advancing the field of language modeling by incorporating a previously underrepresented linguistic structure.

The creation of the IsanDialectDataset wasn’t merely a collection of speech data; it pioneered a methodology readily adaptable to linguistic documentation for other under-resourced languages. Researchers deliberately prioritized capturing authentic, spontaneous conversation – moving beyond scripted readings – to reflect natural language use. Crucially, the project directly confronted the challenges of orthographic inconsistency, a common hurdle in documenting languages lacking standardized writing systems. By developing strategies to navigate these inconsistencies, and by emphasizing a data-driven approach to orthography, the team established a replicable framework for building linguistic resources even when faced with limited pre-existing documentation or formal grammar rules. This focus on authenticity and practical orthographic solutions offers a valuable blueprint for empowering speech technology and linguistic preservation efforts globally.

Continued development centers on broadening the scope of the Isan linguistic resource through the inclusion of a more representative range of speakers – encompassing variations in age, gender, geographic location, and socioeconomic background – alongside recordings captured in a wider array of communicative settings. This expansion is coupled with research into specialized speech processing techniques designed to address the specific phonetic and phonological properties of Isan, such as its tonal system and unique vowel qualities. Researchers aim to move beyond generic models and create algorithms finely tuned to the nuances of the language, ultimately improving the performance of speech recognition, translation, and language generation systems for this historically under-resourced language and paving the way for more inclusive speech technologies.

The creation of this Isan speech corpus embodies a spirit of rigorous exploration, dismantling the barriers to entry for underrepresented languages in the field of Natural Language Processing. One sees a parallel with the work of David Hilbert, who famously stated, “We must be able to answer the question: What are the ultimate foundations of mathematics?” This corpus, much like a foundational mathematical proof, establishes a necessary groundwork – consistent annotation guidelines and an open-source dataset – to rigorously examine and ultimately build upon the complexities of the Isan dialect. By meticulously documenting phonetic transcriptions and orthographic challenges, the researchers aren’t simply collecting data; they’re actively probing the system, testing its limits, and laying bare the core components needed for robust speech recognition technologies.

Uncharted Voices

The creation of this Isan speech corpus is not an arrival, but a controlled demolition of a linguistic boundary. It exposes the fragility of established speech recognition systems, systems built on the presumption of a monolithic ‘standard’ language. The true work begins now: deliberately stressing the corpus, introducing noise, dialectal variations, and even intentional mispronunciations. Only through such adversarial testing can the underlying architecture of these models be truly understood-and, inevitably, redesigned.

The documented challenges in orthography and annotation aren’t bugs to be fixed; they are features of any living language. Attempts at rigid standardization are, at best, temporary reprieves. Future efforts should embrace this inherent messiness, exploring annotation schemes that acknowledge ambiguity and allow for multiple valid interpretations. The goal isn’t perfect transcription, but the creation of systems that can gracefully handle the inevitable imperfections of human speech.

This corpus is, at its heart, a provocation. It begs the question: what other ‘underrepresented’ languages are not merely absent from current AI systems, but actively suppressed by their very design? The architecture of inclusion isn’t about adding more data points; it’s about dismantling the presumptions baked into the foundation. The next step isn’t simply to build more speech recognition systems; it’s to rebuild the very definition of ‘speech’ itself.

Original article: https://arxiv.org/pdf/2511.21229.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-01 03:33