# A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech Recognition

arXiv: https://arxiv.org/abs/2205.03027 · HTML: https://mlsanity.com/paper/2205.03027/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 6 May 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [Modulating early visual processing by language](https://mlsanity.com/paper/1707.00683/) (2017)
- [Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model](https://mlsanity.com/paper/1712.01541/) (2017)
- [Batch Normalized Recurrent Neural Networks](https://mlsanity.com/paper/1510.01378/) (2015)
- [Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition](https://mlsanity.com/paper/1707.06065/) (2017)
- [Joint Modeling of Accents and Acoustics for Multi-Accent Speech Recognition](https://mlsanity.com/paper/1802.02656/) (2018)
- [Visual Reasoning with Multi-hop Feature Modulation](https://mlsanity.com/paper/1808.04446/) (2018)

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

- [Improving the Training Recipe for a Robust Conformer-based Hybrid Model](https://mlsanity.com/paper/2206.12955/) (2022)
- [Accented Speech Recognition With Accent-specific Codebooks](https://mlsanity.com/paper/2310.15970/) (2023)
- [GLoRIA: Gated Low-Rank Interpretable Adaptation for Dialectal ASR](https://mlsanity.com/paper/2603.02464/) (2026)

## Abstract (arXiv)

Despite the success of deep learning in speech recognition, multi-dialect speech recognition remains a difficult problem. Although dialect-specific acoustic models are known to perform well in general, they are not easy to maintain when dialect-specific data is scarce and the number of dialects for each language is large. Therefore, a single unified acoustic model (AM) that generalizes well for many dialects has been in demand. In this paper, we propose a novel acoustic modeling technique for accurate multi-dialect speech recognition with a single AM. Our proposed AM is dynamically adapted based on both dialect information and its internal representation, which results in a highly adaptive AM for handling multiple dialects simultaneously. We also propose a simple but effective training method to deal with unseen dialects. The experimental results on large scale speech datasets show that the proposed AM outperforms all the previous ones, reducing word error rates (WERs) by 8.11% relative compared to a single all-dialects AM and by 7.31% relative compared to dialect-specific AMs.