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RASTA processing of speech
The theoretical and experimental foundations of the RASTA method are reviewed, the relationship with human auditory perception is discussed, the original method is extended to combinations of additive noise and convolutional noise, and an application is shown to speech enhancement.
Connectionist Speech Recognition: A Hybrid Approach
From the Publisher: Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state-of-the-art continuous…
The ICSI Meeting Corpus
- Adam L. Janin, D. Baron, Chuck Wooters
- Computer ScienceIEEE International Conference on Acoustics…
- 6 April 2003
A corpus of data from natural meetings that occurred at the International Computer Science Institute in Berkeley, California over the last three years is collected, which supports work in automatic speech recognition, noise robustness, dialog modeling, prosody, rich transcription, information retrieval, and more.
A view of the parallel computing landscape
Writing programs that scale with increasing numbers of cores should be as easy as writing programs for sequential computers.
RASTA-PLP speech analysis technique
- H. Hermansky, N. Morgan, A. Bayya, P. Kohn
- Physics[Proceedings] ICASSP-92: IEEE International…
- 23 March 1992
The authors have developed a technique that is more robust to such steady-state spectral factors in speech that is conceptually simple and computationally efficient.
Robust speech recognition using the modulation spectrogram
Speech and Audio Signal Processing - Processing and Perception of Speech and Music, Second Edition
This Second Edition of Speech and Audio Signal Processing will update and revise the original book to augment it with new material describing both the enabling technologies of digital music distribution and a range of exciting new research areas in automatic music content processing that have emerged in the past five years, driven by the digital music revolution.
On using MLP features in LVCSR
Recognition results show that MLP features can significantly improve recognition performance in large vocabulary continuous speech recognition (LVCSR) tasks for the NIST 2001 Hub-5 evaluation set with models trained on the Switchboard Corpus, even when discriminative training and system combination are used.
Towards increasing speech recognition error rates
Deep and Wide: Multiple Layers in Automatic Speech Recognition
- N. Morgan
- Computer ScienceIEEE Transactions on Audio, Speech, and Language…
It is concluded 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.