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Automatic Speech Recognition for the Basa\`{a} Language: A Low-Resource Approach

Sophie Gertrude Ngo Mock, Charles Moudina Varmantchaonala, Paul Dayang, Jean Michel Nlong II, Christopher Gies

arXiv:2609.32408v1cs.CLcs.AI

TL;DR

Basaà lacks prior ASR despite a large speaker community and limited digital linguistic resources. This paper fine-tunes XLS-R using a Basaà–French corpus and character-level CTC, reporting a first quantitative baseline. The authors frame the system as a technical baseline and methodological blueprint, while noting constraints from limited data, dialect diversity, and orthographic inconsistency.

  • Problem

    Basaà has no known prior ASR system, despite being spoken by approximately 300 000 people and remaining severely under-resourced digitally.

    Source: §I · p. 1

  • Method

    The paper fine-tunes XLS-R on a Basaà–French Mozilla Common Voice corpus using character-level CTC, informed by analysis of Basaà’s linguistic features.

    Source: §I · p. 1, §V · p. 7

  • Results

    14.13% WER is described as competitive or superior to reported 30–88% WERs for comparably low-resource African-language systems, though direct comparison is difficult.

    Source: D. DISCUSSION · p. 5

  • Takeaways & Limitations

    The authors present the system as a technical baseline and methodological blueprint for future ASR research on Basaà and other underrepresented languages.

    Source: §V · p. 7

    The approximately 11 134-utterance training corpus constrains learning phonological contrasts; twelve dialects challenge generalization beyond the predominantly represented dialect, and orthographic conventions are inconsistent.

    Source: D. DISCUSSION · p. 6

Abstract

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The rapid advancement of Artificial Intelligence (AI) and Natural Language Processing (NLP) has revolutionized the way humans interact with machines. Among the most impactful developments is Automatic Speech Recognition (ASR), which enables computers to convert spoken language into text. Systems such as those built on deep neural networks, transformer architectures, and self-supervised learning have achieved near-human performance for well-resourced languages such as English and French. Yet, these advances have disproportionately benefited a small fraction of the world's languages.

I. INTRODUCTION

The paper addresses the lack of ASR for under-resourced Basaà, where scarce annotated data and linguistic complexity pose a research challenge. It introduces a first ASR pipeline and reports test-set results as a baseline for future work.

I. INTRODUCTION

  • Basaà had no known prior ASR system despite being spoken by approximately 300 000 people and remaining severely under-resourced digitally.The paper describes under-resourced languages as lacking adequate digital linguistic resources, including annotated corpora and computational tools.Source: §I · p. 1
  • The research asks how modern ASR can be applied to Basaà given scarce annotated data, limited orthographic standards, tonal contrasts, and a rich morphosyntactic system.Source: §I · p. 1
  • The pipeline fine-tunes XLS-R on a Basaà–French Mozilla Common Voice corpus, while linguistic analysis identifies features relevant to ASR design.The analysis highlights tone, vowel quantity, and the consonant inventory.Source: §I · p. 1
  • WER of 14.13% and CER of 3.51% on 1 671 test utterances establish the first quantitative baseline for Basaà ASR.The evaluation uses a test set of 1 671 utterances.Source: §I · p. 1
  • A prototype mobile application, Mahop, integrates the ASR model for real-world transcription and crowdsourced data collection.Source: §I · p. 1

II. ANALYSIS OF THE BASAÀ LANGUAGE

The section introduces Basaà’s linguistic and regional context, then describes phonological properties relevant to ASR design. Its vowel, consonant, and tonal distinctions pose specific recognition and vocabulary-design considerations.

II. ANALYSIS OF THE BASAÀ LANGUAGE

  • Basaà’s linguistic structure is relevant to acoustic modeling, tokenization strategies, and expected recognition difficulty.Source: §II · p. 2

A. GENERAL OVERVIEW OF BASAÀ

  • The study’s corpus primarily represents Mpo, specifically Basaà Babimbi spoken in Ngambè, among approximately twelve dialectal varieties.Basaà is spoken predominantly in Cameroon’s Centre and Littoral regions.Source: A. GENERAL OVERVIEW OF BASAÀ · p. 2

B. MORPHOSYNTACTIC ANALYSIS OF BASAÀ

1) Phonological System

  • Basaà’s short–long vowel contrast carries lexical meaning, so ASR must be sensitive to vowel duration.The passage gives tí (“to give”) and tíí (“to touch”) as an example.Source: 1) Phonological System · p. 2
  • Basaà has 30 consonants, including implosives and pre-nasalized stops that motivate a vocabulary explicitly encoding these distinctive sounds.Source: 1) Phonological System · pp. 2–3
  • Basaà’s lexical and grammatical tone contrasts add recognition complexity; spectral features alone may not distinguish tonal minimal pairs.The passage identifies high and low tones, with context-dependent rising, falling, or mid-level realizations.Source: 1) Phonological System · p. 3

2) Morphological Properties

Basaà’s agglutinative morphology creates many related forms and contributes to a large, open vocabulary. The section connects this structure to the case for character-level CTC modeling.

2) Morphological Properties

  • Basaà’s verbal themes combine a radical, extensions, and a final vowel, with extensions encoding a wide range of grammatical meanings.Extensions include applicative, passive, reflexive, causative, and other forms.Source: 2) Morphological Properties · p. 3
  • The productivity of verbal morphology creates a large, open vocabulary, making word-level language models less practical and strengthening the case for character-level CTC decoding.Source: 2) Morphological Properties · p. 3
  • Vowel raising in verbal radicals under extensions changes their acoustic realization and must be handled by the ASR acoustic model.The passage describes regular shifts such as /e/ and /o/ raising to /i/ and /u/.Source: 2) Morphological Properties · p. 3

3) Syntactic Properties

The paper links Basaà’s varied surface forms to character-level modeling, then outlines an end-to-end XLS-R pipeline from audio to transcription. Its architecture uses CTC to align audio representations with character sequences.

3) Syntactic Properties

  • Basaà’s varied pronouns and rich verbal morphology yield many surface forms for related meanings, which character-level CTC modeling is suited to address.Source: 3) Syntactic Properties · p. 3

A. OVERVIEW OF THE ASR PIPELINE

  • The system fine-tunes XLS-R in a four-stage end-to-end pipeline covering data preparation, model adaptation, CTC training, and inference with evaluation.Source: A. OVERVIEW OF THE ASR PIPELINE · p. 3

B. MODEL ARCHITECTURE AND COMPONENTS

  • Raw audio passes through a convolutional feature extractor and transformer encoder to a CTC head that predicts Basaà characters for decoding.CTC training learns alignment between acoustic frames and character sequences without frame-level annotations.Source: A. OVERVIEW OF THE ASR PIPELINE · p. 3

A. OVERVIEW OF THE ASR PIPELINE

  • XLS-R’s multilingual self-supervised pretraining includes several Bantu languages, while Basaà’s alphabet is introduced only during fine-tuning.Pretraining uses raw acoustic speech without an alphabet or transcriptions; fine-tuning maps learned representations to target-language orthographic units.Source: A. OVERVIEW OF THE ASR PIPELINE · pp. 3–4

1) Input: Raw Audio Signal

The system processes raw waveforms, which preserve spectro-temporal detail relevant to Basaà’s tonal contrasts, through XLS-R’s learned acoustic features and transformer representations. Full fine-tuning adapts the cross-lingual model to the Basaà corpus.

1) Input: Raw Audio Signal

3) Transformer Encoder: Cross-Lingual Representation

  • XLS-R-300M uses a 24-layer transformer pretrained contrastively on audio from 128 languages; full fine-tuning on Basaà yielded better results than freezing lower layers.Basaà may not have been in pretraining, but several Bantu languages were represented.Source: 3) Transformer Encoder: Cross-Lingual… · p. 4

4) CTC Head and Basaà Vocabulary

The model uses a character-level CTC vocabulary suited to Basaà orthography and transfers cross-lingual representations through fine-tuning. Its experiments use Mozilla Common Voice Basaà recordings and a held-out test set.

4) CTC Head and Basaà Vocabulary

  • The CTC tokenizer explicitly represents Basaà tonal diacritics and characters such as ñ, while CTC learns character-level alignments without explicit segmentation.The vocabulary is built from the training corpus, and greedy decoding collapses repeated characters and blank tokens.Source: 4) CTC Head and Basaà Vocabulary · p. 4

5) Architecture Specificity for Basaà: Comparison with French and English

  • Basaà’s tonal contrasts, small corpus, distinctive consonants, and morphological productivity motivate adaptation; XLS-R transfer avoids requiring a machine-readable lexicon or pronunciation dictionary.The model is fine-tuned to Basaà’s acoustic and phonological properties using cross-lingual representations pretrained on 128 languages.Source: 5) Architecture Specificity for Basaà:… · p. 5

A. DATASET

  • The Common Voice Basaà corpus contains 14 recorded hours from 52 speakers, with 13 validated hours; the final test set contains 1,671 audio-transcription pairs.The data were split into training, validation, and test subsets, and audio was resampled to 16 kHz and amplitude-normalized.Source: A. DATASET · p. 5

B. EXPERIMENTAL SETUP AND HYPERPARAMETERS

The fine-tuned system achieved 14.13% WER and 3.51% CER on the held-out test set. Errors include word-boundary and diacritic issues, while some utterances were transcribed perfectly.

B. EXPERIMENTAL SETUP AND HYPERPARAMETERS

  • Fine-tuning ran for 10 epochs with effective batch size 16, learning rate 3 × 10^-4, linear warmup over 500 steps, AdamW, and CTC loss.Experiments used NVIDIA Tesla T4/P100 GPUs and 25 GB RAM.Source: B. EXPERIMENTAL SETUP AND… · p. 5

C. RESULTS

  • 14.13% WER and 3.51% CER were achieved on the held-out test set of 1,671 utterances.The WER corresponds to approximately 86 correctly transcribed words out of every 100 on average.Source: C. RESULTS · p. 5
  • Errors include missing word boundaries, diacritic substitutions, and occasional consonant insertions; a significant proportion of test utterances had perfect transcriptions.Small character errors, such as missing tone marks, can make an entire word incorrect while leaving most characters intact.Source: C. RESULTS · p. 5

D. DISCUSSION

The reported 14.13% WER is presented as a strong result for a first low-resource Basaà ASR system, though comparisons across datasets are difficult. Remaining errors are largely localized to characters, with corpus size, dialect diversity, and orthographic inconsistency constraining performance.

D. DISCUSSION

  • 14.13% WER is reported as competitive or superior to the 30–88% WER range for comparable low-resource African-language systems, though dataset differences make direct comparison difficult.The system used approximately 14 hours of training audio.Source: D. DISCUSSION · p. 5
  • The approximately 11,134-utterance, 13-hour training corpus constrains learning of Basaà phonological contrasts, while twelve dialects and nonuniform orthography add generalization and label-consistency challenges.Tonal distinctions in particular require sufficient minimal-pair examples to be learned reliably.Source: D. DISCUSSION · p. 6
  • 3.51% CER versus 14.13% WER suggests errors are systematic and localized, often affecting diacritics and tonal markers rather than producing unintelligible outputs.A Basaà character n-gram language model or tonal correction module could potentially reduce these errors.Source: D. DISCUSSION · p. 6
  • Character-level CTC bypasses word-level modeling, providing a flexible framework for morphologically rich Basaà without a machine-readable lexicon.The discussion presents this as a linguistically principled design choice.Source: D. DISCUSSION · p. 6

E. FUTURE DIRECTIONS

The future-work agenda prioritizes expanding and diversifying the training corpus, alongside targeted improvements to tonal modeling and dialect robustness. It also proposes longer training, public deployment, and applying the methodology to other Cameroonian languages.

E. FUTURE DIRECTIONS

  • Expanding the corpus to 50+ hours with diverse speakers, dialects, and acoustic conditions is proposed as the most impactful improvement and would likely reduce WER substantially.The Mahop application’s data-collection mechanism is identified as a route for corpus growth.Source: E. FUTURE DIRECTIONS · pp. 6–7
  • A Basaà n-gram or neural language model trained on existing text could be integrated as a post-processor to correct tonally induced errors.Suggested text sources include Biblical translations and community web content.Source: E. FUTURE DIRECTIONS · p. 7
  • Dialect-specific submodels or dialect identification in the pipeline are proposed to improve robustness across Basaà’s twelve documented dialects.Source: E. FUTURE DIRECTIONS · p. 7
  • Additional training epochs with learning-rate scheduling and regularization techniques such as weight decay and dropout could further reduce WER.Source: E. FUTURE DIRECTIONS · p. 7
  • Hosting the Mahop API on a cloud GPU instance is proposed to make the ASR system publicly accessible and accelerate community-driven data collection.Source: E. FUTURE DIRECTIONS · p. 7
  • The methodology is proposed for extension to other low-resource Cameroonian languages, including Ewondo, Fulfulde, and Ghomala.The paper frames this as contributing to digital inclusion of Cameroon’s linguistic heritage.Source: E. FUTURE DIRECTIONS · p. 7

V. CONCLUSION

The paper presents the first Basaà ASR system, fine-tuning XLS-R-300M on a Basaà–French corpus with character-level CTC. It reports a first quantitative baseline and frames the work as a blueprint for future ASR research on underrepresented languages.

V. CONCLUSION

  • The system achieves 14.13% WER and 3.51% CER on 1,671 test utterances, establishing the first quantitative baseline for Basaà ASR.Source: §V · p. 7
  • Fine-tuned XLS-R-300M uses a character-level CTC objective on a Basaà–French Mozilla Common Voice corpus.Source: §V · p. 7
  • Remaining errors are predominantly localized to diacritics, tonal markers, and word boundaries, while many utterances receive perfect transcriptions.Source: §V · p. 7
  • The work presents modern transfer learning paired with linguistic analysis and an appropriate architecture as a way to achieve meaningful ASR performance for very low-resource African languages.It is intended as a technical baseline and methodological blueprint for Basaà and other underrepresented languages in Cameroon and Africa.Source: §V · p. 7

APPENDIX

The appendix presents Mahop, a mobile transcription prototype integrating fine-tuned XLS-R, and describes user roles for transcription, data contribution, and validation. Its backend currently runs locally, with cloud deployment planned for wider access.

APPENDIX

  • Mahop integrates the fine-tuned XLS-R model into a speech-transcription mobile application built with Flutter, Flask, and a local Python server.Source: APPENDIX · p. 8
  • Contributors can submit audio and transcriptions for validators to accept or reject, creating a crowdsourcing mechanism aimed at the shortage of labeled Basaà training data.Source: APPENDIX · p. 8
  • Standard users can record speech and receive real-time Basaà transcriptions, while managing their transcription history.Source: APPENDIX · pp. 8–9
  • The backend currently runs locally, stores data in PostgreSQL, and is planned for future deployment on a cloud GPU instance for wider public access.Source: APPENDIX · p. 8
  • Contributors can add and view submissions, while validators can review submissions, view validation statistics, and export validated data in Excel format.Source: APPENDIX · pp. 10–11

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