Training a Speech-to-Text Model for Dutch on the Corpus Gesproken Nederlands

Speech-to-text, also known as Speech Recognition, is a technology that is able to recognize and transcribe spoken language into text. In subsequent steps, this transcription can be used to complete a multitude of tasks, such as providing automatic subtitles or parsing voice commands. In recent years, Speech-to-Text models have dramatically improved thanks partially to advances in Deep Learning methods. Starting from the open-source project DeepSpeech, we train speech-to-text models for Dutch, using the Corpus Gesproken Nederlands (CGN). First, we contribute a pre-processing pipeline for this dataset, to make it suitable for the task at hand, obtaining a ready-to-use speech-to-text dataset for Dutch. Second, we investigate the performance of Dutch and Flemish models trained from scratch, establishing a baseline for the CGN dataset for this task. Finally, we investigate the issue of transferring speech-to-text models between related languages. In this case, we analyse how a pre-trained English model can be transferred and fine-tuned for Dutch.