Abstract
This paper reviews a line of research carried out over the last decade in speech recognition assisted by discriminatively trained, feedforward networks. The particular focus is on the use of multiple layers of processing preceding the hidden Markov model based decoding of word sequences. Emphasis is placed on the use of multiple streams of highly dimensioned layers, which have proven useful for this purpose. This paper ultimately concludes that while the deep processing structures can provide improvements for this genre, choice of features and the structure with which they are incorporated, including layer width, can also be significant factors.
Keywords
Computer scienceFocus (optics)Speech recognitionHidden Markov modelEmphasis (telecommunications)Decoding methodsLayer (electronics)Artificial intelligenceWord (group theory)Feed forwardNatural language processingPattern recognition (psychology)LinguisticsTelecommunications
Affiliated Institutions
Related Publications
Boosting attribute and phone estimation accuracies with deep neural networks for detection-based speech recognition
Generation of high-precision sub-phonetic attribute (also known as phonological features) and phone lattices is a key frontend component for detection-based bottom-up speech rec...
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well ...
Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling
Acoustic models used in hidden Markov model/neural-network (HMM/NN) speech recognition systems are usually trained with a frame-based cross-entropy error criterion. In contrast,...
Deep Belief Networks using discriminative features for phone recognition
Deep Belief Networks (DBNs) are multi-layer generative models. They can be trained to model windows of coefficients extracted from speech and they discover multiple layers of fe...
Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition
Convolutional Neural Networks (CNN) have showed success in achieving translation invariance for many image processing tasks. The success is largely attributed to the use of loca...
Publication Info
- Year
- 2011
- Type
- article
- Volume
- 20
- Issue
- 1
- Pages
- 7-13
- Citations
- 127
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
127
OpenAlex
Cite This
Nelson Morgan
(2011).
Deep and Wide: Multiple Layers in Automatic Speech Recognition.
IEEE Transactions on Audio Speech and Language Processing
, 20
(1)
, 7-13.
https://doi.org/10.1109/tasl.2011.2116010
Identifiers
- DOI
- 10.1109/tasl.2011.2116010