Domain Adaptation with N-gram Language Models for Swedish Automatic Speech Recognition : Using text data augmentation to create domain-specific n-gram models for a Swedish open-source wav2vec 2.0 model

Abstract: Automatic Speech Recognition (ASR) enables a wide variety of practical applications. However, many applications have their own domain-specific words, creating a gap between training and test data when used in practice. Domain adaptation can be achieved through model fine-tuning, but it requires domain-specific speech data paired with transcripts, which is labor intensive to produce. Fortunately, the dependence on audio data can be mitigated to a certain extent by incorporating text-based language models during decoding. This thesis explores approaches for creating domain-specific 4-gram models for a Swedish open-source wav2vec 2.0 model. The three main approaches extend a social media corpus with domain-specific data to estimate the models. The first approach utilizes a relatively small set of in-domain text data, and the second approach utilizes machine transcripts from another ASR system. Finally, the third approach utilizes Named Entity Recognition (NER) to find words of the same entity type in a corpus to replace with in-domain words. The 4-gram models are evaluated by the error rate (ERR) of recognizing in-domain words in a custom dataset. Additionally, the models are evaluated by the Word Error Rate (WER) on the Common Voice test set to ensure good overall performance. Compared to not having a language model, the base model improves the WER on Common Voice by 2.55 percentage points and the in-domain ERR by 6.11 percentage points. Next, adding in-domain text to the base model results in a 2.61 WER improvement and a 10.38 ERR improvement over not having a language model. Finally, adding in-domain machine transcripts and using the NER approach results in the same 10.38 ERR improvement as adding in-domain text but slightly less significant WER improvements of 2.56 and 2.47, respectively. These results contribute to the exploration of state-of-the-art Swedish ASR and have the potential to enable the adoption of open-source ASR models for more use cases.