.png){kind=icon}
.png){kind=icon}
Return
Graph-Based Semisupervised Learning for Acoustic Modeling in Automatic Speech Recognition
.png){kind=icon}
PRE
AI
DOI：10.1109/TASLP.2016.2593800.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Inthis paper, we investigate how to apply graph-based semisupervised learning to acoustic modeling in speech recognition. Graph-based semisupervised learning is a widely used transductive semisupervised learning method in which labeled and unlabeled data are jointly represented as a weighted graph; the resulting graph structure is then used as a constraint during the classification of unlabeled data points. We investigate suitable graph-based learning algorithms for speech data and evaluate two different frameworks for integrating graph-based learning into state-of-the-art, deep neural network (DDN)-based speech recognition systems. The first framework utilizes graph-based learning in parallel with a DNN classifier within a lattice-rescoring framework, whereas the second framework relies on an embedding of graph neighborhood information into continuous space using an autoencoder. We demonstrate significant improvements in frame-level phonetic classification accuracy and consistent reductions in word error rate on large-vocabulary conversational speech recognition tasks.
Keywords:
Automatic speech recognition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
5.1
Papers:
2.6K
Citations:
1.1W