# Low-resource speech recognition and dialect identification of Irish in a multi-task framework

arXiv: https://arxiv.org/abs/2405.01293 · HTML: https://mlsanity.com/paper/2405.01293/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 18 of 18)

- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [Intermediate Loss Regularization for CTC-based Speech Recognition](https://mlsanity.com/paper/2102.03216/) (2021)
- [E-Branchformer: Branchformer with Enhanced merging for speech recognition](https://mlsanity.com/paper/2210.00077/) (2022)
- [Multilingual Speech Recognition With A Single End-To-End Model](https://mlsanity.com/paper/1711.01694/) (2017)
- [CTC-Segmentation of Large Corpora for German End-to-end Speech Recognition](https://mlsanity.com/paper/2007.09127/) (2020)
- [Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model](https://mlsanity.com/paper/1712.01541/) (2017)
- [Improving Massively Multilingual ASR With Auxiliary CTC Objectives](https://mlsanity.com/paper/2302.12829/) (2023)
- [JTubeSpeech: corpus of Japanese speech collected from YouTube for speech recognition and speaker verification](https://mlsanity.com/paper/2112.09323/) (2021)
- [OLR 2021 Challenge: Datasets, Rules and Baselines](https://mlsanity.com/paper/2107.11113/) (2021)
- [Towards dialect-inclusive recognition in a low-resource language: are balanced corpora the answer?](https://mlsanity.com/paper/2307.07295/) (2023)
- [AISPEECH-SJTU accent identification system for the Accented English Speech Recognition Challenge](https://mlsanity.com/paper/2102.09828/) (2021)
- [A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning](https://mlsanity.com/paper/2305.13331/) (2023)
- [Towards spoken dialect identification of Irish](https://mlsanity.com/paper/2307.07436/) (2023)
- [Phonetic Temporal Neural Model for Language Identification](https://mlsanity.com/paper/1705.03151/) (2017)

## Built on by (most cited first, 3 of 3)

- [Fotheidil: an Automatic Transcription System for the Irish Language](https://mlsanity.com/paper/2501.00509/) (2024)
- [Enhancing Code-Switching ASR Leveraging Non-Peaky CTC Loss and Deep Language Posterior Injection](https://mlsanity.com/paper/2412.08651/) (2024)
- [Jointly Improving Dialect Identification and ASR in Indian Languages using Multimodal Feature Fusion](https://mlsanity.com/paper/2607.02862/) (2026)

## Abstract (arXiv)

This paper explores the use of Hybrid CTC/Attention encoder-decoder models trained with Intermediate CTC (InterCTC) for Irish (Gaelic) low-resource speech recognition (ASR) and dialect identification (DID). Results are compared to the current best performing models trained for ASR (TDNN-HMM) and DID (ECAPA-TDNN). An optimal InterCTC setting is initially established using a Conformer encoder. This setting is then used to train a model with an E-branchformer encoder and the performance of both architectures are compared. A multi-task fine-tuning approach is adopted for language model (LM) shallow fusion. The experiments yielded an improvement in DID accuracy of 10.8% relative to a baseline ECAPA-TDNN, and WER performance approaching the TDNN-HMM model. This multi-task approach emerges as a promising strategy for Irish low-resource ASR and DID.