# PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic Information for Language Identification

arXiv: https://arxiv.org/abs/2203.12366 · HTML: https://mlsanity.com/paper/2203.12366/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2022.

## Built on by (most cited first, 5 of 5)

- [A Compact End-to-End Model with Local and Global Context for Spoken Language Identification](https://mlsanity.com/paper/2210.15781/) (2022)
- [Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization](https://mlsanity.com/paper/2302.05110/) (2023)
- [Investigating model performance in language identification: beyond simple error statistics](https://mlsanity.com/paper/2305.18925/) (2023)
- [LID Models are Actually Accent Classifiers: Implications and Solutions for LID on Accented Speech](https://mlsanity.com/paper/2506.00628/) (2025)
- [Impact of Frame Rates on Speech Tokenizer: A Case Study on Mandarin and English](https://mlsanity.com/paper/2505.17076/) (2025)

## Abstract (arXiv)

We propose a novel model to hierarchically incorporate phoneme and phonotactic information for language identification (LID) without requiring phoneme annotations for training. In this model, named PHO-LID, a self-supervised phoneme segmentation task and a LID task share a convolutional neural network (CNN) module, which encodes both language identity and sequential phonemic information in the input speech to generate an intermediate sequence of phonotactic embeddings. These embeddings are then fed into transformer encoder layers for utterance-level LID. We call this architecture CNN-Trans. We evaluate it on AP17-OLR data and the MLS14 set of NIST LRE 2017, and show that the PHO-LID model with multi-task optimization exhibits the highest LID performance among all models, achieving over 40% relative improvement in terms of average cost on AP17-OLR data compared to a CNN-Trans model optimized only for LID. The visualized confusion matrices imply that our proposed method achieves higher performance on languages of the same cluster in NIST LRE 2017 data than the CNN-Trans model. A comparison between predicted phoneme boundaries and corresponding audio spectrograms illustrates the leveraging of phoneme information for LID.