# Insights into End-to-End Learning Scheme for Language Identification

arXiv: https://arxiv.org/abs/1804.00381 · HTML: https://mlsanity.com/paper/1804.00381/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2018.

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

- [On the Robustness of Arabic Speech Dialect Identification](https://mlsanity.com/paper/2306.03789/) (2023)
- [Fine-Tuned Self-Supervised Speech Representations for Language Diarization in Multilingual Code-Switched Speech](https://mlsanity.com/paper/2312.09645/) (2023)
- [VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System](https://mlsanity.com/paper/2310.11069/) (2023)

## Abstract (arXiv)

A novel interpretable end-to-end learning scheme for language identification is proposed. It is in line with the classical GMM i-vector methods both theoretically and practically. In the end-to-end pipeline, a general encoding layer is employed on top of the front-end CNN, so that it can encode the variable-length input sequence into an utterance level vector automatically. After comparing with the state-of-the-art GMM i-vector methods, we give insights into CNN, and reveal its role and effect in the whole pipeline. We further introduce a general encoding layer, illustrating the reason why they might be appropriate for language identification. We elaborate on several typical encoding layers, including a temporal average pooling layer, a recurrent encoding layer and a novel learnable dictionary encoding layer. We conducted experiment on NIST LRE07 closed-set task, and the results show that our proposed end-to-end systems achieve state-of-the-art performance.