๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Noise adaptation algorithms for robust speech recognition

โœ Scribed by H.M. Cung; Y. Normandin

Publisher
Elsevier Science
Year
1993
Tongue
English
Weight
781 KB
Volume
12
Category
Article
ISSN
0167-6393

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

Near-field Adaptive Beamformer for Robus
Near-field Adaptive Beamformer for Robust Speech Recognition
โœ Iain A. McCowan; Darren C. Moore; S. Sridharan ๐Ÿ“‚ Article ๐Ÿ“… 2002 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 237 KB

This paper investigates a new microphone array processing technique specifically for the purpose of speech enhancement and recognition. The main objective of the proposed technique is to improve the low frequency directivity of a conventional adaptive beamformer, as low frequency performance is crit

Robust speech recognition in additive an
Robust speech recognition in additive and convolutional noise using parallel model combination
โœ M.F.J. Gales; S.J. Young ๐Ÿ“‚ Article ๐Ÿ“… 1995 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 166 KB

The method of Parallel Model Combination (PMC) has been shown to be a powerful technique for compensating a speech recognizer for the effects of additive noise. In this paper, the PMC scheme is extended to include the effects of convolutional noise. This is done by introducing a modified "mismatch"

Integrated bias removal techniques for r
Integrated bias removal techniques for robust speech recognition
โœ Craig Lawrence; Mazin Rahim ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 522 KB

In this paper, we present a family of maximum likelihood (ML) techniques that aim at reducing an acoustic mismatch between the training and testing conditions of hidden Markov model (HMM)-based automatic speech recognition (ASR) systems. Our study is conducted in two phases. In the first phase, we e