---
title: "MPSA-DenseNet — paper by Song et al. (2023)"
description: "The paper addresses automatic recognition of a speaker's English accent by country or region, rather than transcribing… 25 references."
authors: ["Tianyu Song", "Linh Thi Hoai Nguyen", "Ton Viet Ta"]
year: 2023
venue: "arXiv:2306.08798"
source: "https://arxiv.org/abs/2306.08798"
doi: "10.48550/arxiv.2306.08798"
arxiv: "2306.08798"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/song-2023-mpsa-densenet-novel-deep-learning/"
---
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# MPSA-DenseNet: A novel deep learning model for English accent classification

**Authors:** Tianyu Song, Linh Thi Hoai Nguyen and Ton Viet Ta · **Year:** 2023 · **Venue:** [arXiv:2306.08798](https://md.research.elamur.ai/free/en/venues/computer-speech-language/) ([Computer Speech & Language 2023](https://md.research.elamur.ai/free/en/venues/computer-speech-language/2023/)) · **Publisher:** [Elsevier BV](https://md.research.elamur.ai/free/en/publishers/elsevier-bv/) · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2306.08798) · [PDF](https://arxiv.org/pdf/2306.08798) · [DOI 10.48550/arxiv.2306.08798](https://doi.org/10.48550/arxiv.2306.08798) · [arXiv 2306.08798](https://arxiv.org/abs/2306.08798) · [OpenAlex](https://openalex.org/W4399188511)

**Citations:** 19 citations (OpenAlex, 2026-10-14)

## Summary
The paper addresses automatic recognition of a speaker's English accent by country or region, rather than transcribing accented speech. The authors assembled 9117 recordings covering six varieties: three native (British, American, Scottish) and three non-native (Chinese, German, Indian). The recordings come from the Speech Accent Archive, the CSTR VCTK Corpus and their own collection. Each clip was converted to wav, resampled, fixed to six seconds, lightly noised and turned into MFCC feature maps. On a DenseNet-121 backbone they built three variants:

- **Multi-DenseNet** adds speaker age and gender as auxiliary prediction tasks over a shared network.
- **PSA-DenseNet** inserts a PSA attention module.
- **MPSA-DenseNet** combines both.

All variants were trained with identical hyperparameters and compared with plain DenseNet-121 and EPSANet. The combined model scored best, at about 97% on F0.5 and validation accuracy, and each single addition already beat the baselines. The work suggests that cheap side labels and attention can noticeably sharpen a fine-grained audio classifier.

## Key points
- Data: 9117 clips, six accents, plus labels for five age bands and binary gender used only as auxiliary targets.
- Preprocessing: FFmpeg conversion to wav, 44,100 Hz, two channels, trim or silence-pad to 6 s, Gaussian noise, pre-emphasis α = 0.97, and 64 MFCCs over 516 frames (hop 512).
- Training: MXNet, Adam, learning rate 0.0001, 128 epochs, batch 16, softmax cross-entropy, with the same settings for every model.
- Results on the test set (micro / macro F0.5) and validation accuracy:

  | Model | Micro F0.5 | Macro F0.5 | Val. accuracy | Parameters |
  |---|---|---|---|---|
  | MPSA-DenseNet | 97.2% | 96.4% | 97.5% | 19.64M |
  | Multi-DenseNet | 95.3% | 94.9% | 95.1% | 5.56M |
  | PSA-DenseNet | 94.8% | 94.2% | 93.8% | 19.92M |
  | EPSANet | 94.3% | 93.5% | 94.3% | 20.6M |
  | DenseNet-121 | 93.6% | 92.7% | 93.9% | 7.98M |

- Error analysis: German was recognised perfectly, while Mandarin and American were the weakest classes but still above 94%. The most common mistake was Indian labelled as Mandarin (4.3%).

## Interesting ideas
- Predicting easily available side attributes (age, gender) through separate heads on one shared trunk acts as a regulariser and boosted the main task without extra annotation effort.
- Each component was tested alone and in combination under one frozen hyperparameter set, with parameter counts reported next to scores, so gains can be attributed rather than tuned in.
- Micro and macro F-scores are reported together with a normalised confusion matrix, which exposes skewed classes and names the single most frequent confusion pair.
- Inputs are normalised to a fixed shape and mildly perturbed with noise so the model learns features that survive small distortions.

## For Mova

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

- **I1** (tree): gparse could share its encoder with light auxiliary heads such as UPOS, morphological features or distance-to-head, which would push the encoder toward syntax-relevant representations at almost no labelling cost.
- **I2** (learn): when adding a new feature or repair rule, run the base, each addition alone, and the full combination with identical settings, and log model size, to keep improvement claims honest.
- **I3** (learn): dependency-label and frame-role evaluations could routinely report macro scores and the top confused label pairs, pointing directly at which UD relations or VerbNet roles need targeted repair rules.
- **I4** (tokens): the tagger and lemmatizer could be trained on lightly corrupted copies of the text, with typos, casing changes and missing punctuation, to stay stable on messy school or web input.

*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

- [Kyushu University](https://md.research.elamur.ai/free/en/universities/kyushu-university/) — University, Fukuoka, Japan · 4 works · 25 citations

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🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,263 works

## Cite

Cite as: Tianyu Song, Linh Thi Hoai Nguyen and Ton Viet Ta. 2023. MPSA-DenseNet: A novel deep learning model for English accent classification. arXiv:2306.08798.

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

```bibtex
@misc{song2023mpsa,
  title = {{MPSA-DenseNet: A novel deep learning model for English accent classification}},
  author = {Tianyu Song, Linh Thi Hoai Nguyen and Ton Viet Ta},
  year = {2023},
  doi = {10.48550/arxiv.2306.08798},
  eprint = {2306.08798},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2306.08798}
}
```

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## References

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*Updated 29 October 2026*