RASTA-PLP speech analysis technique

Heřmanský, Hynek; Morgan, Nelson; Bayya, Aruna; Kohn, Phil

doi:10.1109/icassp.1992.225957

Cited by 213 publications

(100 citation statements)

References 7 publications

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“…In [7], PLP speech analysis was first introduced as a method to represent speech signals with respect to the human perception and with as few parameters as possible. However, PLP was, as most of the other analysis techniques, sensitive towards steady-state spectral factors caused by transmission channels, such as telephone recordings or the usage of different microphones [8]. Therefore, [8] introduced the RASTA methodology for PLP rendering it more robust towards these channel distortions and reducing error rates in several speech recognition experiments with only a slightly more computationally expensive extraction method.…”

Section: Relative Spectral Perceptual Linear Predictive Coding (Rastamentioning

confidence: 99%

“…However, PLP was, as most of the other analysis techniques, sensitive towards steady-state spectral factors caused by transmission channels, such as telephone recordings or the usage of different microphones [8]. Therefore, [8] introduced the RASTA methodology for PLP rendering it more robust towards these channel distortions and reducing error rates in several speech recognition experiments with only a slightly more computationally expensive extraction method. In this study 21 critical bands are analyzed in frames of 25 ms in length and with a 10 ms offset.…”

Section: Relative Spectral Perceptual Linear Predictive Coding (Rastamentioning

confidence: 99%

“…After the critical band analysis and equal loudness conversion, the subsequent steps required for the relative spectral (RASTA) processing extension, follow the implementation recommendations in [8]. After transforming the spectrum to the logarithmic domain and the application of RASTA filtering, the signal is transformed back using the exponential function.…”

Section: Relative Spectral Perceptual Linear Predictive Coding (Rastamentioning

confidence: 99%

See 2 more Smart Citations

Multiple Classifier Systems for the Classification of Audio-Visual Emotional States

Glodek

Tschechne

Layher

et al. 2011

Affective Computing and Intelligent Interaction

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Abstract. Research activities in the field of human-computer interaction increasingly addressed the aspect of integrating some type of emotional intelligence. Human emotions are expressed through different modalities such as speech, facial expressions, hand or body gestures, and therefore the classification of human emotions should be considered as a multimodal pattern recognition problem. The aim of our paper is to investigate multiple classifier systems utilizing audio and visual features to classify human emotional states. For that a variety of features have been derived. From the audio signal the fundamental frequency, LPCand MFCC coefficients, and RASTA-PLP have been used. In addition to that two types of visual features have been computed, namely form and motion features of intermediate complexity. The numerical evaluation has been performed on the four emotional labels Arousal, Expectancy, Power, Valence as defined in the AVEC data set. As classifier architectures multiple classifier systems are applied, these have been proven to be accurate and robust against missing and noisy data.

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Section: Relative Spectral Perceptual Linear Predictive Coding (Rastamentioning

confidence: 99%

Section: Relative Spectral Perceptual Linear Predictive Coding (Rastamentioning

confidence: 99%

Section: Relative Spectral Perceptual Linear Predictive Coding (Rastamentioning

confidence: 99%

See 1 more Smart Citation

Multiple Classifier Systems for the Classification of Audio-Visual Emotional States

Glodek

Tschechne

Layher

et al. 2011

Affective Computing and Intelligent Interaction

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“…Most often, two exclusive directions are followed: either enhance the speech signal itself by ideally filtering out the noise [1,2,3], or change the way acoustic features are extracted from the signal [4,5]. This paper presents an intermediary approach that enhances the feature extraction process at a level as close as possible to the original signal : at the magnitude spectrogram level, i.e.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised spectral subtraction for noise-robust ASR

Lathoud

Magimai-Doss

Mesot

et al. 2005

IEEE Workshop on Automatic Speech Recognition and Understanding, 2005.

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This paper proposes a simple, computationally efficient 2-mixture model approach to discriminate between speech and background noise at the magnitude spectrogram level. It is directly derived from observations on real data, and can be used in a fully unsupervised manner, with the EM algorithm. In this paper, the 2-mixture model is used in an "Unsupervised Spectral Subtraction" scheme that can be applied as a pre-processing step for any acoustic feature extraction scheme, such as MFCCs or PLP. The goal is to improve noise-robustness of the acoustic features. Experimental results on both OGI Numbers 95 and Aurora 2 tasks yielded a major improvement on all noise conditions, while retaining a similar performance on clean conditions.

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“…For example, introducing a new type of feature [1,2] or applying a feature normalization algorithm [3][4][5][6][7] requires retraining of the acoustic model. Acoustic model adaptation [8,9] can be used only when one has access to the detailed structure of the acoustic model.…”

Section: Introductionmentioning

confidence: 99%

Noise suppression method for preprocessor of time-lag speech recognition system based on bidirectional optimally modified log spectral amplitude estimation

Obuchi

Takeda

Togami

2013

Acoust. Sci. & Tech.

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In this paper, we propose a new noise suppression method, that is best used as a preprocessor for time-lag speech recognition. Assuming that a time lag of a few seconds is acceptable in various speech recognition applications, the proposed method is realized as a combination of forward and backward estimation flows over time. Each estimation flow is based on the optimally modified log spectral amplitude (OM-LSA) speech estimator, but a look-ahead estimation mechanism is additionally equipped to make the estimation more robust. Evaluation experiments using various databases confirm that the speech recognition accuracy can be greatly improved by adding the proposed method to the existing system.

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RASTA-PLP speech analysis technique

Cited by 213 publications

References 7 publications

Multiple Classifier Systems for the Classification of Audio-Visual Emotional States

Multiple Classifier Systems for the Classification of Audio-Visual Emotional States

Unsupervised spectral subtraction for noise-robust ASR

Noise suppression method for preprocessor of time-lag speech recognition system based on bidirectional optimally modified log spectral amplitude estimation

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