# Methods of Automatic Matrix Language Determination for Code-Switched Speech

arXiv: https://arxiv.org/abs/2410.02521 · HTML: https://mlsanity.com/paper/2410.02521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [Multilingual Speech Recognition With A Single End-To-End Model](https://mlsanity.com/paper/1711.01694/) (2017)
- [Hash Embeddings for Efficient Word Representations](https://mlsanity.com/paper/1709.03933/) (2017)
- [Multilingual and code-switching ASR challenges for low resource Indian languages](https://mlsanity.com/paper/2104.00235/) (2021)
- [Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation](https://mlsanity.com/paper/1811.02356/) (2018)
- [Building a Unified Code-Switching ASR System for South African Languages](https://mlsanity.com/paper/1807.10949/) (2018)
- [Grammatical Constraints on Intra-sentential Code-Switching: From Theories to Working Models](https://mlsanity.com/paper/1612.04538/) (2016)
- [Syntactic and Semantic Features For Code-Switching Factored Language Models](https://mlsanity.com/paper/1710.01809/) (2017)

## Abstract (arXiv)

Code-switching (CS) is the process of speakers interchanging between two or more languages which in the modern world becomes increasingly common. In order to better describe CS speech the Matrix Language Frame (MLF) theory introduces the concept of a Matrix Language, which is the language that provides the grammatical structure for a CS utterance. In this work the MLF theory was used to develop systems for Matrix Language Identity (MLID) determination. The MLID of English/Mandarin and English/Spanish CS text and speech was compared to acoustic language identity (LID), which is a typical way to identify a language in monolingual utterances. MLID predictors from audio show higher correlation with the textual principles than LID in all cases while also outperforming LID in an MLID recognition task based on F1 macro (60%) and correlation score (0.38). This novel approach has identified that non-English languages (Mandarin and Spanish) are preferred over the English language as the ML contrary to the monolingual choice of LID.