The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Learning a better representation of speech soundwaves using restricted boltzmann machines
Abstract
State of the art speech recognition systems rely on preprocessed speech features such as Mel cepstrum or linear predictive coding coefficients that collapse high dimensional speech sound waves into low dimensional encodings. While these have been successfully applied in speech recognition systems, such low dimensional encodings may lose some relevant information and express other information in a way that makes it difficult to use for discrimination. Higher dimensional encodings could both improve performance in recognition tasks, and also be applied to speech synthesis by better modeling the statistical structure of the sound waves. In this paper we present a novel approach for modeling speech sound waves using a Restricted Boltzmann machine (RBM) with a novel type of hidden variable and we report initial results demonstrating phoneme recognition performance better than the current state-of-the-art for methods based on Mel cepstrum coefficients.
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Self-citationAn Information-Maximization Approach to Blind Separation and Blind Deconvolution
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Self-citation
Research works printed but not linked: 1
- Mohamed, Deep belief networks for phone recognition
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Sorted by citations
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239
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BackgroundTowards End-To-End Speech Recognition with Recurrent Neural Networks
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Self-citationDeep Learning: Methods and Applications
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Improving deep neural networks for LVCSR using rectified linear units and dropout
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“the only use of these units in speech to date was in [10] where Gaussian-ReLU RBMs were used to learn features from raw, unlabeled acoustic data to later use in phone recognition experiments on TIMIT.”
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Metadata
- Authors
- Jaitly, N., & Hinton, G.
- Affiliations
- University of Toronto (CA)
- Venue
- 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2011-05
- Pages
- 5884-5887
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2132037657
- Cited by
- 231
- References
- 19
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 3 · 2023: 11 · 2022: 8 · 2021: 24 · 2020: 34 · 2019: 29 · 2018: 21 · 2017: 16 · 2016: 22 · 2015: 16 · 2014: 17 · 2013: 9 · 2012: 5
- FWCI
- 9.01
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Keywords
- restricted Boltzmann machine, phoneme recognition, hidden variables, speech feature extraction, speech synthesis
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