Recent Advances in Robust Speech Recognition Technology


Javier Ramírez, Juan Manuel Górriz

DOI: 10.2174/9781608051724111010
eISBN: 978-1-60805-172-4, 2011
ISBN: 978-1-60805-389-6

Indexed in: Scopus, EBSCO.

This E-book is a collection of articles that describe advances in speech recognition technology. Robustness in speech recognition refe...[view complete introduction]
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Effective Multiple Regression for Robust Singleand Multi-channel Speech Recognition

- Pp. 169-174 (6)

Weifeng Li, Kazuya Takeda and Fumitada Itakura


Conventional single- andmulti-channel speech enhancementmethods aimat improving the signal-to-noise ratio (SNR) of the signal signals captured through distant microphones, which do not specifically target the improvements of ASR performance. We investigate a nonlinear multiple regression to extract robust features for automatic speech recognition (ASR). The idea is to approximate the log spectra of a close-talking microphone by effectively combining of the log spectra of distant microphones. The devised system turns out to be a generalized log spectral subtraction framework for the robust speech recognition. We demonstrate the effectiveness of the proposed approach through our extensive evaluations on the single- and multi-channel isolated word recognition experiments conducted in 15 real car-driving environments.

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