We propose a BERT-based language identification system (BERT-LID) to improve language identification performance, especially on short-duration speech segments. We extend the original BERT model by taking the phonetic posteriorgrams (PPG) derived from the front-end phone recognizer as input. Then we deployed the optimal deep classifier followed by it for language identification.

We use OLR20, TIMIT&THCHS-30, TAL_ASR datasets for experiments. Among them, in order to test in segment audio, we perform segmentation processing on TIMIT&THCHS-30, where the window length is 1s and the window movement is 1s for segmentation. The specific usage of data is shown in data. The TAL_ASR data is forcibly aligned.

We directly obtains the phoneme features of the audio through Phoneme recognizer based on long temporal context, and then obtains tokens through the BertTokenizer model provided by BERT. We take this feature as input. For the specific processing of data, see the 'get_data.py'.

Load the data by adjusting the path parameters in 'load_data.py'. In the experiment, we divided the dataset into three datasets: train, test, and dev.

Our model is saved in BertCNN, BertRCNN, BertDPCNN, BertLSTM, and pay attention to whether bert in the model-related file is turned on during use.

Run the program using the following command:

python run_me.py

Important parameters are as follows:

• model_name: Select the input model

• label_list：Given label list

• bert_voacb_file：Input the bert model

Or you can use the following command to run multiple times and adjust the super parameters at the same time:

bash run_me.sh

[1] P. Schwarz, "Phoneme Recognition based on Long Temporal Context, PhD Thesis", Brno University of Technology, 2009

[2] TAL_ASR: https://ai.100tal.com/dataset

[2] Devlin, Jacob, et al. "Bert: Pre-training of deep bidirectional transformers for language understanding." arXiv preprint arXiv:1810.04805 (2018).

[4] https://github.com/codertimo/BERT-pytorch

[5] Schwarz, Petr, et al. "Phoneme recognizer based on long temporal context." Speech Processing Group, Faculty of Information Technology, Brno University of Technology.[Online]. Available: http://speech. fit. vutbr. cz/en/software (2006).

Please cite our paper if you find this work useful:

@InProceedings{NieYT2022_ISCSLP,
  author    = {Yuting Nie and Junhong Zhao and Wei-Qiang Zhang and Jinfeng Bai},
  booktitle = {International Symposium on Chinese Spoken Language Processing (ISCSLP)},
  title     = {BERT-LID: Leveraging BERT to improve spoken language identification},
  address   = {Singapore},
  month     = {12},
  year      = {2022},
  url       = {https://arxiv.org/abs/2203.00328},
}