---
title: "Identifying dialects with textual and acoustic cues — Hanani 2017"
description: "This paper describes the Birzeit/Fitchburg team's (ahaqst) entries in two VarDial 2017… 63% accuracy. 17 references, cited by 8 papers here."
authors: ["Abualsoud Hanani", "Aziz Qaroush", "Stephen Taylor"]
year: 2017
venue: "Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)"
source: "https://aclanthology.org/W17-1211/"
doi: "10.18653/v1/w17-1211"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/hanani-2017-identifying-dialects-textual-acoustic-cues/"
---
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# Identifying dialects with textual and acoustic cues

**Authors:** [Abualsoud Hanani](https://md.research.elamur.ai/free/en/authors/abualsoud-hanani/), [Aziz Qaroush](https://md.research.elamur.ai/free/en/authors/aziz-qaroush/) and [Stephen Taylor](https://md.research.elamur.ai/free/en/authors/stephen-taylor/) · **Year:** 2017 · **Venue:** [Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)](https://md.research.elamur.ai/free/en/venues/vardial/) ([VarDial 2017](https://md.research.elamur.ai/free/en/venues/vardial/2017/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/W17-1211/) · [PDF](https://aclanthology.org/W17-1211.pdf) · [DOI 10.18653/v1/w17-1211](https://doi.org/10.18653/v1/w17-1211) · [OpenAlex](https://openalex.org/W2741270771)

**Citations:** 16 citations (OpenAlex, 2026-10-14) · [8](https://md.research.elamur.ai/free/en/papers/hanani-2017-identifying-dialects-textual-acoustic-cues/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This paper describes the Birzeit/Fitchburg team's (ahaqst) entries in two VarDial 2017 DSL shared tasks. The Arabic task (ADI) had five dialect classes. The Swiss-German task (GDI) had four. For Arabic, the organizers supplied ASR transcripts and 400-dimensional I-vectors, and the team also downloaded the raw .wav files. The Swiss-German data was text only, transcribed phonetically with the Dieth guidelines.

The team built several kinds of classifiers:
- text classifiers, including per-dialect word n-gram language models scored by cross-entropy, a model that tiles the input with character strings seen in training, and Weka-based voting;
- acoustic classifiers, including GMM-UBM, GMM tokenization and I-vector systems.

Their scores were merged with Focal multiclass logistic regression. On Arabic, combining text and audio gave the best result, a weighted F1 of 0.628. Audio alone did much better than text alone. On Swiss-German, the best run reached about 63% accuracy.

The authors also found a large gap between scores on held-out training lines and on the official test set. They attribute it to topic bias or to an inhomogeneous corpus, which is a useful warning about how evaluation splits are built.

## Key points
- **ADI data:** 14,000 training lines (1.7MB), 1524 development lines and 1492 test lines. The team ran three runs:

  | Run | Input | Accuracy | Weighted F1 |
  |---|---|---|---|
  | run2 | text only | 0.323 | 0.314 |
  | run3 | acoustic only | 0.593 | 0.586 |
  | run1 | text + acoustic | 0.629 | 0.628 |

- **Team progress:** the best ADI F1 rose from 0.426 in 2016 to 0.628 in 2017. The top team in 2017 (unibuckernel) reached 0.763.
- **GDI data:** 14,478 lines in total, split into 13,032 for training and two 723-line development files, plus 3638 test lines.
  - The character-string entropy run (run2) scored 0.629 accuracy and 0.614 weighted F1.
  - The word-entropy run (run1) scored 0.562 accuracy.
  - On reserved training data the same two classifiers had scored 79% and 84%.
- **Character-string classifier:** it collects all byte strings of up to 25 bytes from training. For each dialect, it covers the test segment with the longest known strings first and adds up their log-frequencies.
- **Acoustic fusion:** the final acoustic system combines four subsystems with Focal linear logistic regression:
  - GMM-UBM with 2048 components;
  - GMM-tokenization bigram with 256 components;
  - unigram with 2048 components;
  - I-vector.
- **Baseline n-gram order:** in the SVM baseline (svm multiclass), word n-grams longer than 3 made accuracy worse.

## Interesting ideas
- Classify text by covering it greedily with the longest substrings seen in training per class and summing their log-frequencies. This ignores word boundaries entirely, so it can pick up spelling and pronunciation variants.
- To train fusion weights on the development set without leakage, cut it into two halves, fit on one, test on the other, then swap.
- When samples come from coherent conversations, rare topic words (such as a city name) can look like class markers even when they say nothing about the class. A random line-level holdout then overstates accuracy, here 84% on held-out lines versus 56% on test.
- Combine heterogeneous evidence at the level of scores, using a light logistic-regression layer over each subsystem's per-class scores. This let weak text cues still add to strong acoustic ones.
- Note how a transcription convention shapes the data: Arabic transcripts were normalized to standard spelling, while Swiss-German ones were written as pronounced. The same word therefore shows up as one form in one corpus and many forms in the other.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tokens): a similar longest-known-piece covering could score how to segment or normalize unknown and variant-spelled words before tagging, and the chosen pieces would show why.
- **I2** (learn): the same swap-halves scheme could calibrate how much each witness parser's vote counts in silver-data agreement without grading the weights on the data used to fit them.
- **I3** (learn): split evaluation by document or story rather than by sentence, and add negative controls that swap topic words, so that topical shortcuts are not counted as real skill.
- **I4** (learn): a small, inspectable fusion over the per-arc scores of the perceptron parser and gparse could replace plain majority voting and still show each witness's contribution.
- **I5** (tree): when converting or mixing treebanks of different UD dialects or registers, record each source's spelling-normalization policy so that vocabulary differences are not mistaken for grammatical ones.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Birzeit University](https://md.research.elamur.ai/free/en/universities/birzeit-university/) — University, Birzeit, Palestine · 38 works · 798 citations
- [Fitchburg State University](https://md.research.elamur.ai/free/en/universities/fitchburg-state-university/) — University, Fitchburg, United States · 2 works · 39 citations

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## Cite

Cite as: Abualsoud Hanani, Aziz Qaroush and Stephen Taylor. 2017. Identifying dialects with textual and acoustic cues. Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial).

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

```bibtex
@inproceedings{hanani2017identifying,
  title = {{Identifying dialects with textual and acoustic cues}},
  author = {Abualsoud Hanani, Aziz Qaroush and Stephen Taylor},
  booktitle = {Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)},
  year = {2017},
  doi = {10.18653/v1/w17-1211},
  url = {https://doi.org/10.18653/v1/w17-1211}
}
```

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## References

17 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*