csikasote/bembaspeech-exps

Bemba ASR model obtained by fine-tuning a well performing DeepSpeech English pretrained model. # BembaSpeech Baseline Experiments This repository contains resources (dataset and notebooks) for reproducing experiments in the BembaSpeech: A Speech Recognition Corpus for the Bemba Language . Please consider citing as follows if you use part of the code or data in your work or project: @InProceedings{sikasote-anastasopoulos:2022:LREC, author = {Sikasote, Claytone and Anastasopoulos, Antonios}, title = {BembaSpeech: A Speech Recognition Corpus for the Bemba Language}, booktitle = {Proceedings of the Language Resources and Evaluation Conference}, month = {June}, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {7277--7283}, abstract = {We present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting over 24 hours of read speech in the Bemba language, a written but low-resourced language spoken by over 30\% of the population in Zambia. To assess its usefulness for training and testing ASR systems for Bemba, we explored different approaches; supervised pre-training (training from scratch), cross-lingual transfer learning from a monolingual English pre-trained model using DeepSpeech on the portion of the dataset and fine-tuning large scale self-supervised Wav2Vec2.0 based multilingual pre-trained models on the complete BembaSpeech corpus. From our experiments, the 1 billion XLS-R parameter model gives the best results. The model achieves a word error rate (WER) of 32.91\%, results demonstrating that model capacity significantly improves performance and that multilingual pre-trained models transfers cross-lingual acoustic representation better than monolingual pre-trained English model on the BembaSpeech for the Bemba ASR. Lastly, results also show that the corpus can be used for building ASR systems for Bemba language.}, url = {aclanthology.org } ## 1. DeepSpeech Experiments In this proje …