A new wavelet function for audio and speech processing

Mantilla-Caeiros, A.; Nakano-Miyatake, Mariko; Pérez-Meana, Héctor

doi:10.1109/mwscas.2007.4488547

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…However if the speech signals are processed taking into account the form in which they are perceived by the human ear, similar or even better results may be obtained. Thus using an ear model-based feature extraction method might represent an attractive alternative, since this approach allows characterizing the speech signal in the form that it is perceived [8]. This section proposes a feature extraction method based on an inner ear model, which takes into account the fundamentals concepts of critical bands.…”

Section: Feature Extraction Methods Based On Wavelet Transformmentioning

confidence: 99%

Esophageal Speech Enhancement Using a Feature Extraction Method Based on Wavelet Transform

Caeiros¹,

Meana²

2012

Modern Speech Recognition Approaches With Case Studies

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Section: Feature Extraction Methods Based On Wavelet Transformmentioning

confidence: 99%

Esophageal Speech Enhancement Using a Feature Extraction Method Based on Wavelet Transform

Caeiros¹,

Meana²

2012

Modern Speech Recognition Approaches With Case Studies

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“…However, if the speech signals are processed taking in account the form in which they are perceived by the human ear, similar or even better results may be obtained. Thus, the use of an ear model-based feature extraction method may be an attractive alternative because this approach allows characterizing the speech signal in the form that it is perceived [16]. Thus, a feature extraction method, based on an inner ear model taking into account the fundamentals concepts of critical bands, will be developed.…”

Section: Feature Vector Extractionmentioning

confidence: 99%

“…In all cases, 650 different alaryngeal voiced segments with a convergence factor equal to 0.009 are used, achieving a global mean square error of 0.1 after 400,000 iterations. [16]. Figure 4 shows the plot of mono-aural recordings of the Spanish word "abeja", pronounced by normal and esophageal speakers with a sample frequency of 8 kHz, respectively, including the detected voiced segments.…”

Section: Classification Stagementioning

confidence: 99%

A pattern recognition based esophageal speech enhancement system

Caeiros

Miyatake

Meana

2010

JART

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A system for improving the intelligibility and quality of alaryngeal speech based on the replacement of voiced segments of alaryngeal speech with the equivalent segments of normal speech is proposed. To this end, the system proposed identifies the voiced segments of the alaryngeal speech signal by using isolate speech recognition methods, and replaces them by their equivalent voiced segments of normal speech, keeping the silence and unvoiced segments without change. Evaluation results using objective and subjective evaluation methods show that the proposed system proposed provides a fairly good improvement of the quality and intelligibility of alaryngeal speech signals.

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Isolate Speech Recognition Based on Time-Frequency Analysis Methods

Mantilla-Caeiros

Nakano-Miyatake

Pérez-Meana

2009

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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A feature extraction method for isolate speech recognition is proposed, which is based on a time frequency analysis using a critical band concept similar to that performed in the inner ear model; which emulates the inner ear behavior by performing signal decomposition, similar to carried out by the basilar membrane. Evaluation results show that the proposed method performs better than other previously proposed feature extraction methods when it is used to characterize normal as well as esophageal speech signal.

show abstract

A new wavelet function for audio and speech processing

Cited by 4 publications

References 12 publications

Esophageal Speech Enhancement Using a Feature Extraction Method Based on Wavelet Transform

Esophageal Speech Enhancement Using a Feature Extraction Method Based on Wavelet Transform

A pattern recognition based esophageal speech enhancement system

Isolate Speech Recognition Based on Time-Frequency Analysis Methods

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