  In this paper, we present how to hybridize a Word2vec model and an
attention-based end-to-end speech recognition model. We build a phoneme
recognition system based on Listen, Attend and Spell model. And the phoneme
recognition model uses a word2vec model to initialize the embedding matrix for
the improvement of the performance, which can increase the distance among the
phoneme vectors. At the same time, in order to solve the problem of overfitting
in the 61 phoneme recognition model on TIMIT dataset, we propose a new training
method. A 61-39 phoneme mapping comparison table is used to inverse map the
phonemes of the dataset to generate more 61 phoneme training data. At the end
of training, replace the dataset with a standard dataset for corrective
training. Our model can achieve the best result under the TIMIT dataset which
is 16.5% PER (Phoneme Error Rate).
