Hierarchical Neural Network Structures for Phoneme Recognition
| By: | Daniel Vasquez; Rainer Gruhn; Wolfgang Minker |
| Publisher: | Springer Nature |
| Print ISBN: | 9783642432101 |
| eText ISBN: | 9783642344251 |
| Edition: | 0 |
| Copyright: | 2013 |
| Format: | Page Fidelity |
eBook Features
Instant Access
Purchase and read your book immediately
Read Offline
Access your eTextbook anytime and anywhere
Study Tools
Built-in study tools like highlights and more
Read Aloud
Listen and follow along as Bookshelf reads to you
In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are mainly evaluated within the phoneme recognition task under the Hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN) paradigm. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron (MLP). Additionally, the output of the first level is used as an input for the second level. This system can be substantially speeded up by removing the redundant information contained at the output of the first level.