# Joint Language Identification of Code-Switching Speech using Attention based E2E Network

arXiv: https://arxiv.org/abs/1907.06342 · HTML: https://mlsanity.com/paper/1907.06342/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

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

- [Implicit spoken language diarization](https://mlsanity.com/paper/2306.12913/) (2023)
- [Code-Switching Spoken Language Identification as Multi-Label Set Prediction](https://mlsanity.com/paper/2610.01450/) (2026)

## Abstract (arXiv)

Language identification (LID) has relevance in many speech processing applications. For the automatic recognition of code-switching speech, the conventional approaches often employ an LID system for detecting the languages present within an utterance. In the existing works, the LID on code-switching speech involves modelling of the underlying languages separately. In this work, we propose a joint modelling based LID system for code-switching speech. To achieve the same, an attention-based end-to-end (E2E) network has been explored. For the development and evaluation of the proposed approach, a recently created Hindi-English code-switching corpus has been used. For the contrast purpose, an LID system employing the connectionist temporal classification-based E2E network is also developed. On comparing both the LID systems, the attention based approach is noted to result in better LID accuracy. The effective location of code-switching boundaries within the utterance by the proposed approach has been demonstrated by plotting the attention weights of E2E network.