A Swedish wav2vec versus Google speech-to-text

Abstract: As the automatic speech recognition technology is becoming more advanced, the possibilities of in which fields it can operate are growing. The best automatic speech recognition technologies today are mainly based on - and made for - the English language. However, the national library of Sweden recently released open-source wav2vec models purposefully with the Swedish language in mind. With the interest of investigating their performance, one of their models is chosen to assess how well they transcribe the Swedish news broadcasts ”kvart-i-fem”-ekot, comparing its results with Google speech-to-text. The results present wav2vec as the prominent model for this type of audio data, securing a word error rate average that is 9 percentage points less than Google-speech-to-text. A part of this performance could be attributed to the self-supervising method the wav2vec model uses to access large amounts of unlabeled data in its training. In spite of this, both models displayed difficulty with transcribing audio that has poor quality such as disturbing background noise and stationary sounds. Words like abbreviations and names was also difficult for them both to correctly transcribe. Google speech-to-text did however perform better than the wav2vec model on this part.