  Conventional automatic speech recognition (ASR) typically performs
multi-level pattern recognition tasks that map the acoustic speech waveform
into a hierarchy of speech units. But, it is widely known that information loss
in the earlier stage can propagate through the later stages. After the
resurgence of deep learning, interest has emerged in the possibility of
developing a purely end-to-end ASR system from the raw waveform to the
transcription without any predefined alignments and hand-engineered models.
However, the successful attempts in end-to-end architecture still used
spectral-based features, while the successful attempts in using raw waveform
were still based on the hybrid deep neural network - Hidden Markov model
(DNN-HMM) framework. In this paper, we construct the first end-to-end
attention-based encoder-decoder model to process directly from raw speech
waveform to the text transcription. We called the model as "Attention-based
Wav2Text". To assist the training process of the end-to-end model, we propose
to utilize a feature transfer learning. Experimental results also reveal that
the proposed Attention-based Wav2Text model directly with raw waveform could
achieve a better result in comparison with the attentional encoder-decoder
model trained on standard front-end filterbank features.
