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# Transfer Learning for Less-Resourced Semitic Languages Speech Recognition: the Case of Amharic

**Authors:** Yonas Woldemariam · **Year:** 2020 · **Venue:** Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL) **Link:** [aclanthology.org/2020.sltu-1.9](https://aclanthology.org/2020.sltu-1.9/) · library `aclbulk-2020-woldemariam-transfer-learning-less-resourced-semitic` · **Licence:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — summary only

[**5** citations](https://openalex.org/works?filter=cites:W3028910522) openalex · 2026-10-18[**3** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2020-woldemariam-transfer-learning-less-resourced-semitic/) of the 140673 papers whose bibliographies we read[Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)](https://aclanthology.org/volumes/2020.sltu-1/)[licence](https://creativecommons.org/licenses/by-nc/4.0/)

- [Publisher's page](https://aclanthology.org/2020.sltu-1.9/)
- [PDF](https://aclanthology.org/2020.sltu-1.9.pdf)
- [OpenAlex](https://openalex.org/W3028910522)

**Authors (1)**

1. [Woldemariam, Yonas](https://openalex.org/A5042510853) [ORCID](https://orcid.org/0000-0001-5496-5041) · h 3 · 86 citations · 13 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

Amharic has little speech data, so the author reuses acoustic models trained on English (100 h and 460 h) and Mandarin (178 h). He keeps the hidden layers of each source network, replaces the output layer, and fine-tunes on 20 h of Amharic read speech with a smaller learning rate. Word error rate on Amharic falls from 38.72% (baseline) to 24.50% with the English-460 h source (14.22 points absolute), to 25.06% with English-100 h, and to 28.47% with Mandarin. The author judges the relatedness of source and target to matter more than the other factors he tests (source model quality and source data size). He names the Amharic lexicon (65k entries, against 130k for English), the letter-to-sound rules, and an epenthetic vowel that is spoken but not written as the main error sources.

## Ideas

- I1. Measure out-of-vocabulary rate for morpheme-level vocabularies. The paper segments transcripts into morphemes and finds this helps, but OOV words remain. In `tools/hbo`, count OOV on held-out unpointed text for a word-level lexicon against the segmenter's morpheme lexicon.
- I2. Vowels that are pronounced but not written are a known source of confusion here. The Hebrew analogue is the unwritten vowel of unpointed text. A rule table of such cases, with attested examples, would be a test set for the phonology chapter.
- I3. Before carrying a model from one Semitic variety to another, measure feature overlap between source and target and predict which source should transfer better. Test it across the Aramaic varieties: does a model from `arc` carry over to `arc-x-targum` better than one from `tmr`?

## For Mova

I1 goes into `tools/hbo` (segmenter tables and a held-out OOV count). I2 is a rule table for [Phonology of Biblical Hebrew](https://md.mova.elamur.ai/free/en/docs/languages/hbo/03-phonology/). I3 is a test across the varieties in `languages/arc/`. Mova does no speech; only the text-side ideas apply.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work.

```
@inproceedings{woldemariam2020transfer,
  title = {{Transfer Learning for Less-Resourced Semitic Languages Speech Recognition: the Case of Amharic}},
  author = {Woldemariam, Yonas},
  booktitle = {Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)},
  year = {2020},
  url = {https://aclanthology.org/2020.sltu-1.9/}
}
```

- [← previous Comparing Neural Network Parsers for a Less-resourced and Morphologically-rich Language: Amharic Dependency Parser](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2020-seyoum-comparing-neural-network-parsers-less/)
- [next → Camelira: An Arabic Multi-Dialect Morphological Disambiguator](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2022-obeid-camelira-arabic-multi-dialect-morphological/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

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