Automatic Speech Recognition (ASR) is concerned with models, algorithms, and systems for automatically transcribing recorded speech into text. This a hard problem since recorded speech can be highly variable - we do not necessarily who the speaker is, where the speech is recorded, or if there are other acoustic sources (such as noise or competing talkers) in the signal.
Addressing the problem of speech recognition requires some understanding of machine learning, signal processing, and acoustic phonetics. In this course we'll cover the required theoretical background, and how the theory can be transformed into useful speech recognition systems. Lab sessions, and the coursework, will use the open source OpenFst toolkit together with Python and later Kaldi to build and run speech recognition systems.
The perfect background for the ASR course would include the Speech Processing course and a machine learning course such as machine learning and pattern recognition (MLPR) or the machine learning practical (MLP).
However, because of the way people's degree programmes are structured, not many people who do ASR will have the perfect background! This is fine.
If you've done MLPR and/or MLP, but not Speech Processing, then you'll require some speech background. A couple of the earlier lectures will include some material that was in Speech Processing, but it is also recommended that you do some background study:
We'll point out useful links as we go through the course.
If you have taken Speech Processing, but not MLPR or MLP, then you'll require some machine learning background, especially to do with neural networks. There will be a couple of introductory lectures on neural networks, and we'll also point out useful additional background reading when relevant.
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