Browsing by Subject "Finnish"

The importance of Automatic Speech Recognition cannot be underestimated in today’s worlds as they play a significant role in human computer interaction. ASR systems have been studied deeply over time, but their maximum potential is yet to be explored for the Finnish language. Development of a traditional ASR system involves a number of hand-crafted engineering which has made this technology quite difficult and resourceful to develop. However, with advancements in the field of neural networks, end-to-end ASR neural networks can be developed which can automatically learn the mappings of audio to its corresponding transcript., therefore reducing hand crafted engineering requirements. End-to-end neural network ASR systems have been largely developed commercially by tech giants such as Microsoft, Google and Amazon. However, there are limitations to these commercial services such as data privacy and cost of usage. In this thesis, we explored existing studies in the development of an end-to-end neural network ASR for Finnish language. One successful technique utilized in the development of neural network ASR in the advent of inadequate data is Transfer learning. This is the approach explored in this thesis for the development of the end-to-end neural network ASR system. In addition, the success of this approach was evaluated. In order to achieve this purpose, dataset collected from the Finnish Bank of Finland and Kaggle were used to fine-tune Mozilla DeepSpeech model which is a pretrained end-to-end neural network ASR in English language. The results obtained by fine-tuning the pretrained neural network ASR in English for Finnish language showed a word error rate as low as 40% and character error rate as low as 22%. We therefore concluded that transfer learning is a successful technique for creating ASR model for a new language using a pretrained model in another language with little effort, data and resources.