Third-Order Moments of Filtered Speech Signals for Robust Speech Recognition

Indrebo, Kevin M.; Povinelli, Richard J.; Johnson, Michael T.

doi:10.1007/11613107_24

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Furthermore, one of the most important weaknesses of the spectral features is its low robustness in noisy environment. These features are very sensitive to additive noise [15].…”

Section: Related Workmentioning

confidence: 99%

Infrasound Source Identification Based on Spectral Moment Features

Madankan¹,

Riahi

Ranjbar

2016

IJIIS

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Infrasound signals have a frequency range below the human hearing frequency range, and originate from different sources. Since these waves contain useful information about the occurrence of some important event, in this paper we intend to present a method for the recognition of sources of these signals. In the present paper, by using the feature spectral moment along with Mel-frequency cepstral coefficients (MFCC) and linear prediction coefficients (LPC) and also selecting a subset from the feature which plays a more discriminative role for the signal sources, and then by using classifier ensembles, we reached a 98.1% precision in the infrasound source identification.

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“…Furthermore, one of the most important weaknesses of the spectral features is its low robustness in noisy environment. These features are very sensitive to additive noise [15].…”

Section: Related Workmentioning

confidence: 99%

Infrasound Source Identification Based on Spectral Moment Features

Madankan¹,

Riahi

Ranjbar

2016

IJIIS

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“…In contrast, the ASR tasks that have to deal with a very large vocabulary, with under-resourced languages [2], or with noisy environments have to try alternative techniques. An interesting set of alternatives come in the form of nonlinear analysis [3], and some works [4,5,6] show that combining nonlinear features with MFCC's can produce higher recognition accuracies without substituting the whole linear system with novel nonlinear approaches.…”

Section: Introductionmentioning

confidence: 99%

Combining Mel Frequency Cepstral Coefficients and Fractal Dimensions for Automatic Speech Recognition

Ezeiza

López-de-Ipiña

Hernández

et al. 2011

Advances in Nonlinear Speech Processing

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Abstract. Hidden Markov Models and Mel Frequency Cepstral Coefficients (MFCC's) are a sort of standard for Automatic Speech Recognition (ASR) systems, but they fail to capture the nonlinear dynamics of speech that are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful when training data is scarce, or when the ASR task is very complex. In this work, the Fractal Dimension (FD) of the observed time series is combined with the traditional MFCC's in the feature vector in order to enhance the performance of two different ASR systems: the first one is a very simple one, with very few training examples, and the second one is a Large Vocabulary Continuous Speech Recognition System for Broadcast News.

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