Ihquin tlahtouah in Tetelahtzincocah: An annotated, multi-purpose audio and text corpus of Western Sierra P uebla N ahuatl (2025)

Abstract: The development of digital linguistic resources is essential for enhancing the inclusion of indigenous and marginalized languages in the digital domain. Indigenous languages of Mexico, despite representing vast typological diversity and millions of speakers, have largely been overlooked in NLP until recently. In this paper, we present a corpus of audio and annotated transcriptions of Western Sierra Puebla Nahuatl, an endangered variety of Nahuatl spoken in Puebla, Mexico. The data made available in this corpus are useful for ASR, spelling normalization, and word-level language identification. We detail the corpus-creation process, and describe experiments to report benchmark results for each of these important NLP tasks. The corpus audio and text is made freely available.

## introduction data-driven approaches in nlp can offer a multitude of benefits to speakers and linguists, but the quality and availability of these technologies is largely influenced by the availability of linguistically-annotated data for training. to that end, we present a multi-purpose audio and text corpus for an endangered variety of nahuatl that can be used to facilitate research on language technology, particularly research focused on automatic speech recognition, spelling normalization, and the analysis of language contact phenomena. western sierra puebla nahuatl (náhuatl de la sierra oeste de puebla, also called zacatlán-ahuacatlán-tepetzintla nahuatl, iso-639: nhi), spoken by 17,100 speakers in puebla, mexico, is one of the 30 formally-recognized nahuatl varieties spoken today. it is considered endangered, with many communities seeing a loss or significant reduction in inter-generational transmission @xcite . figure 1 : a map of puebla's sierra norte, a mountainous region in the north of the state of puebla, home to numerous indigenous languages, including three nahuatl varieties. the three municipalities corresponding to the nahuatl variety of our corpus, nhi or western sierra nahuatl, is highlighted in yellow, with a green star in the approximate location of the town of tetelancingo. adapted from @xcite . ## related work even though nahuatl is perhaps the most researched and well-documented indigenous language of the americas, only recently has it begun receiving attention in the nlp community. the release of the axolotl corpus @xcite marked an important milestone in nahuatl nlp, making tens of thousands of parallel nahuatl-spanish sentences, from diverse sources and multiple nahuatl varieties, available to mt researchers in machine-readable format. since then, nahuatl has consistently been included in the americasnlp shared task of open mt @xcite . @xcite released a large corpus of transcribed and translated speech of the highland puebla variety, which has been used for developing asr @xcite and speech-to-text translation @xcite . a subset of these transcriptions, in addition to other highland puebla nahuatl texts, were annotated for morphosyntactic information in the form of a ud treebank @xcite . the nhi nahuatl variety has also been the focus of nlp resource and tool development, with the release of a morphological analyzer @xcite and a 10,000-token ud treebank @xcite . low-resource asr capabilities have improved greatly with the ability to fine-tuned large, pretrained models such as wav2vec @xcite and wav2vec 2.0 @xcite . the recent massively multilingual speech (mms) project offers a large multilingual speech model @xcite trained on data from over a thousand languages. text normalization is a valuable upstream task in nlp. automatic orthographic normalization can be useful in digital archive creation @xcite , for ocr post-processing @xcite , and syntactic parsing @xcite , among other tasks. furthermore, spell-correction systems may be useful for encouraging language learning and literacy for endangered languages @xcite . the most successful approaches to orthographic normalization typically leverage character-based machine translation techniques @xcite . text-to-text transformer (t5) @xcite model have recently been used successfully on the task of spelling normalization @xcite . since it is uncommon to have a large corpus of corrected spelling errors, particularly in the case of endangered languages, synthetic errors can be leveraged for fine-tuning such a transformer model @xcite . automatic language identification in languagecontact settings has been an area of interest for many nlp researchers over the last decade, notably with multiple iterations of the "language identification in code-switched data" shared task @xcite , which included code-switching datasets from a number of language pairs. these tasks, as well as a more recent shared task on guaraní-spanish code-switching analysis @xcite , are closely-aligned with the language identification task and data described in this paper for the nahuatl-spanish language pair. foot_2 a different but related task is that of subword-level language identification, which was explored for spanish and wixarika, an indigenous language of mexico, by @xcite . for word-level language identification, crf and lstm-crf were most common in the first two shared tasks. more recently, pre-trained multilingual language models such as mbert @xcite have been successfully leveraged for a variety of nlp tasks, including code-switched language identification @xcite . the language identification task in our corpus also labels adapted loanwords. ali et al. (2024) explore loanword detection for a low-resource language pair. álvarez-mellado and lignos (2022) released a corpus of spanish annotated for loanwords and investigated a number of sequence tagging models' performance on loanword identification. ## corpus creation the corpus originated as a set of fieldwork recordings initially collected as part of an nsf-funded project foot_3 , but were excluded from the project due to their their largely-conversational nature and frequent language mixing and code-switching with sents words type/token norm. 2,681 26k 0.19 orig. the resulting transcriptions make up 2,681 sentences consisting of 26,023 tokens. each sentence also has a corresponding normalized version, a spanish translation, and word-level language annotations on the normalized version. ## experiments and benchmarks in this section we describe experiments and results to provide a benchmark for each task. ## concluding remarks and future work for asr, we show that a mere 3 hours of transcribed audio drastically improves the performance of an off-the-shelf multilingual model. for the spelling normalization and language identification tasks, we find that fine-tuning pretrained transformer models on a relatively small (around 2,000 sentences) training set outperforms the prompted llm model, though we note room for potential improvement via further prompt engineering. we also find that a t5 transformer model, specifically trained for indigenous languages of the americas, does not provide any advantage for our two textbased tasks, and for spelling normalization, in fact performs notably worse. this finding suggests a need for an alternative approach to improving pretrained language models for indigenous languages. in the future, we plan to add to this corpus by automating a large part of the annotation process on additional recordings and/or nhi transcriptions.