Recurrent Neural Networks for End-to-End Speech Recognition : A comparison of gated units in an acoustic model

End-to-end speech recognition is the problem of mapping raw audio signal all the way to text. In doing so the process is not explicitly divided into modules. e.g. [signal → phoneme, phoneme → word]. Recurrentneural networks equipped with specialised temporal based loss functions have recently demonstrated breakthrough results for the end-to-end problem.In this thesis we evaluate a number of neural network architectures for end-to-end learning. LSTM (Long Short Term Memory) is a specialised gated recurrent unit that preserves a signal within a neura lnetwork over period of time. GRU (Gated recurrent Unit) is a recently discovered refinement of LSTM with still unknown performance characteristics. It is reported that different architectures works better or worse depending on the problem at hand. We explore these characteristics for the end-to-end speech recognition problem. Specifically we evaluate various networks on the LibriSpeech corpus. All the audio is read in English by people from different parts of the world. The audio files are excerpts sourced from audio books. The LibriSpeech corpus is divided into both noisy and clean audio. The noisy audio is considered to be more challenging. This corpus is pre-segmented and thus contain ready sub sets for testing. These include both noisy and clean audio and we will evaluate the end-to-end models on both sets. The findings of our experiments shows that GRU can not perform on the same level as LSTM variants. Especially not when trained on noisy data where the GRU network stop improving after only a small part of the allotted training time