Harf-Speech: A Clinically Aligned Framework for Arabic Phoneme-Level Speech Assessment
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Azad, Asif Shanto, MD Sadik Hossain Hossain, Mohammad Sadat Alwuqaysi, Bdour Boughorbel, Sabri Bokhari, Yahya Aljouie, Abdulrhman Sindi, Ayah Othman Hoque, Ehsan |
| author_facet | Azad, Asif Shanto, MD Sadik Hossain Hossain, Mohammad Sadat Alwuqaysi, Bdour Boughorbel, Sabri Bokhari, Yahya Aljouie, Abdulrhman Sindi, Ayah Othman Hoque, Ehsan |
| contents | Automated phoneme-level pronunciation assessment is vital for scalable speech therapy and language learning, yet validated tools for Arabic remain scarce. We present Harf-Speech, a modular system scoring Arabic pronunciation at the phoneme level on a clinical scale. It combines an MSA phonetizer, a fine-tuned speech-to-phoneme model, Levenshtein alignment, and a blended scorer using longest common subsequence and edit-distance metrics. We fine-tune three ASR architectures on Arabic phoneme data and benchmark them with zero-shot multimodal models; the best, OmniASR-CTC-1B-v2, achieves 8.92\% phoneme error rate. Three certified speech-language pathologists independently scored 40 utterances for clinical validation. Harf-Speech attains a Pearson correlation of 0.791 and ICC(2,1) of 0.659 with mean expert scores, outperforming existing end-to-end assessment frameworks. These results show Harf-Speech yields clinically aligned, interpretable scores comparable to inter-rater expert agreement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06191 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Harf-Speech: A Clinically Aligned Framework for Arabic Phoneme-Level Speech Assessment Azad, Asif Shanto, MD Sadik Hossain Hossain, Mohammad Sadat Alwuqaysi, Bdour Boughorbel, Sabri Bokhari, Yahya Aljouie, Abdulrhman Sindi, Ayah Othman Hoque, Ehsan Audio and Speech Processing Artificial Intelligence Computation and Language Sound Automated phoneme-level pronunciation assessment is vital for scalable speech therapy and language learning, yet validated tools for Arabic remain scarce. We present Harf-Speech, a modular system scoring Arabic pronunciation at the phoneme level on a clinical scale. It combines an MSA phonetizer, a fine-tuned speech-to-phoneme model, Levenshtein alignment, and a blended scorer using longest common subsequence and edit-distance metrics. We fine-tune three ASR architectures on Arabic phoneme data and benchmark them with zero-shot multimodal models; the best, OmniASR-CTC-1B-v2, achieves 8.92\% phoneme error rate. Three certified speech-language pathologists independently scored 40 utterances for clinical validation. Harf-Speech attains a Pearson correlation of 0.791 and ICC(2,1) of 0.659 with mean expert scores, outperforming existing end-to-end assessment frameworks. These results show Harf-Speech yields clinically aligned, interpretable scores comparable to inter-rater expert agreement. |
| title | Harf-Speech: A Clinically Aligned Framework for Arabic Phoneme-Level Speech Assessment |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language Sound |
| url | https://arxiv.org/abs/2604.06191 |