Application of Word2vec in Phoneme Recognition
This is an incremental improvement for speech recognition researchers, addressing overfitting in phoneme recognition on a specific dataset.
The paper tackles phoneme recognition by hybridizing Word2vec with an attention-based end-to-end model and introducing a new training method to reduce overfitting, achieving a 16.5% phoneme error rate on the TIMIT dataset.
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).