# Dynamic Multi-scale Convolution for Dialect Identification

arXiv: https://arxiv.org/abs/2108.07787 · HTML: https://mlsanity.com/paper/2108.07787/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 2 Aug 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [Large Margin Softmax Loss for Speaker Verification](https://mlsanity.com/paper/1904.03479/) (2019)
- [DenseNet for Dense Flow](https://mlsanity.com/paper/1707.06316/) (2017)
- [AP20-OLR Challenge: Three Tasks and Their Baselines](https://mlsanity.com/paper/2006.03473/) (2020)
- [AP19-OLR Challenge: Three Tasks and Their Baselines](https://mlsanity.com/paper/1907.07626/) (2019)
- [AP16-OL7: A Multilingual Database for Oriental Languages and A Language Recognition Baseline](https://mlsanity.com/paper/1609.08445/) (2016)
- [AP17-OLR Challenge: Data, Plan, and Baseline](https://mlsanity.com/paper/1706.09742/) (2017)
- [A Deep Neural Network for Short-Segment Speaker Recognition](https://mlsanity.com/paper/1907.10420/) (2019)

## Built on by (most cited first, 2 of 2)

- [An Overview of Indian Spoken Language Recognition from Machine Learning Perspective](https://mlsanity.com/paper/2212.03812/) (2022)
- [Unsupervised Out-of-Distribution Dialect Detection with Mahalanobis Distance](https://mlsanity.com/paper/2308.04886/) (2023)

## Abstract (arXiv)

Time Delay Neural Networks (TDNN)-based methods are widely used in dialect identification. However, in previous work with TDNN application, subtle variant is being neglected in different feature scales. To address this issue, we propose a new architecture, named dynamic multi-scale convolution, which consists of dynamic kernel convolution, local multi-scale learning, and global multi-scale pooling. Dynamic kernel convolution captures features between short-term and long-term context adaptively. Local multi-scale learning, which represents multi-scale features at a granular level, is able to increase the range of receptive fields for convolution operation. Besides, global multi-scale pooling is applied to aggregate features from different bottleneck layers in order to collect information from multiple aspects. The proposed architecture significantly outperforms state-of-the-art system on the AP20-OLR-dialect-task of oriental language recognition (OLR) challenge 2020, with the best average cost performance (Cavg) of 0.067 and the best equal error rate (EER) of 6.52%. Compared with the known best results, our method achieves 9% of Cavg and 45% of EER relative improvement, respectively. Furthermore, the parameters of proposed model are 91% fewer than the best known model.