Proceedings Article10.1109/ISCIT.2004.1413870
Robust speech recognition with feature extraction using combined method of RSF and DRA
N. Wada,Noboru Hayasaka,Shingo Yoshizawa,Yoshikazu Miyanaga +3 more
- 26 Oct 2004
- Vol. 2, pp 1001-1004
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TL;DR: The paper explores the extraction of speech features aiming at noise robustness for speech recognition and proposes advanced speech analysis techniques named RSF/DRA (running spectrum filtering/dynamic range adjustment).
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Abstract: The paper explores the extraction of speech features aiming at noise robustness for speech recognition and proposes advanced speech analysis techniques named RSF/DRA (running spectrum filtering/dynamic range adjustment). The proposed techniques, DRA and RSF, focus on speech feature adjustment. DRA normalizes cepstral dynamic ranges and RSF eliminates the jitter influences of speech feature parameters. Experiments on isolated word recognition were carried out using 40 male and 40 female speakers for training and 5 male and 5 female speakers for recognition. The results of the recognition rate improving from 17% to 63% versus running car noise at -10 dB SNR show the effectiveness and high noise robustness of the proposed method.
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Citations
Dynamic Time Warping for Speech Recognition with Training Part to Reduce the Computation
TL;DR: This paper proposed a method to reduce the number of reference templates, thus reduces the computing time and memory resource and also keep the high recognition rate.
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TL;DR: In this paper, the use of TVLPC among feature extraction algorithms to improve the robustness of automatic speech recognition (ASR) systems against various multiplicative and additive noises is discussed.
References
A tutorial on hidden Markov models and selected applications in speech recognition
Lawrence R. Rabiner
- 01 Feb 1989
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Suppression of acoustic noise in speech using spectral subtraction
TL;DR: A stand-alone noise suppression algorithm that resynthesizes a speech waveform and can be used as a pre-processor to narrow-band voice communications systems, speech recognition systems, or speaker authentication systems.
5.3K
RASTA processing of speech
Hynek Hermansky,Nelson Morgan +1 more
TL;DR: 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.
2.1K
Hidden Markov model decomposition of speech and noise
Andrew Varga,Roger K. Moore +1 more
- 03 Apr 1990
TL;DR: A technique of signal decomposition using hidden Markov models is described that provides an optimal method of decomposing simultaneous processes and has wide implications for signal separation in general and improved speech modeling in particular.
577
Cepstral parameter compensation for HMM recognition in noise
Mark J. F. Gales,Steve Young +1 more
TL;DR: The PMC technique is based on parallel model combination in which the parameters of corresponding pairs of speech and noise states are combined to yield a set of compensated parameters, which improves on earlier cepstral mean compensation methods in that it also adapts the variances and as a result can deal with much lower SNRs.
189