Speech Recognition by Wavelet Analysis

Little attention has been paid so far in the context in which databases used for the study of emotion through vocal channel are recorded. Thus, we propose and evaluate an emotion classification system focusing on the differences between acted and spontaneous emotional speech through the use of two different databases: SAVEE and IEMOCAP. For the purpose of this work, we have examined wavelet packet energy and entropy features applied to Mel, Bark and ERB scale applied with Hidden Markov Model (HMM) as classification system. Experimental results show that the proposed method is a feasible technique for emotion classification for both acted and spontaneous context, pointing out the performance difference of the system between the two contexts. The experimental results shows that ERB scale features gives better performance in comparison with other studied features with recognition accuracy of 78.75% for acted context and 50.06% for spontaneous context.

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“…In the automatic speech recognition, wavelet features are frequently used and gives a promising results 12 .…”

Section: Introductionmentioning

confidence: 99%

Speech Emotion Recognition in Acted and Spontaneous Context

Chenchah

Lachiri

2014

Procedia Computer Science

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“…Therefore an automated means of removing the noise would be an invaluable first stage for many signal processing tasks. Denoising has long been a focus of research Simple methods originally employed the use of time-domain filtering of the corrupted signal [7]; however, this is only successful when removing high frequency noise from low frequency signals and does not provide satisfactory results under real world conditions. To improve performance, modern algorithms filter signals in some transform domain such as z or Fourier.…”

Section: Introductionmentioning

confidence: 99%

Robust Speaker Identification using Denoised Wave Atom and GMM

Alhanjouri¹,

Lubbad²,

Alkurdi³

2013

IJCA

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This paper introduces the use of Wave atom transformation as an efficient speech noise filter with Gaussian mixture models (GMM) for robust text-independent speaker identification. The individual Gaussian components of a GMM are shown to represent some general speaker identity. The focus of this work is on applications which require high robustness of noise and high identification rates using short utterance from noisy (Natural Noise) numerical speech and alphabetical words speech. A Full experimental evaluation of the Gaussian mixture speaker model is conducted on a 10 speakers. The experiments examine algorithmic issues (Preprocessing (Denoising by Wave Atom), Feature Extraction (MFCC), Training using GMM, Pattern Matching (Maximum likelihood estimation ML), Decision Rule (Expectation Maximization EM)). The Proposed algorithm attains 95% identification accuracy using 5 seconds noisy speech utterances without Wave atom preprocessing it attains 90% identification accuracy using 5 seconds noisy speech utterances. Proposed denoisy algorithm increases the identification ratio by 5% for noisy speech signals, this ratio is interesting enough.

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“…Authors of many works in order to identify diseases and pathological changes in the voice used a discrete wavelet transformation DWT [1,41] and support vector machine-based classification method as feature classification tools [1, 5,6,11]. In scientific work [1, 21,44] demonstrated that the most effective algorithm (100% recognition efficiency) is a system composed of wavelet packet transforms along with feature dimension reduction by linear discriminant analysis and a support vector machine-based classification method.…”

Section: Introductionmentioning

confidence: 99%

Advanced Time-Frequency Representation in Voice Signal Analysis

Mika¹,

Jóźwik²

2018

Adv. Sci. Technol. Res. J.

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The most commonly used time-frequency representation of the analysis in voice signal is spectrogram. This representation belongs in general to Cohen's class, the class of time-frequency energy distributions. From the standpoint of properties of the resolution, spectrogram representation is not optimal. In Cohen class representations are known which have a better resolution properties. All of them are created by smoothing the Wigner-Ville'a distribution characterized by the best resolution, however, the biggest harmful interference. The used smoothing functions decide about a compromise between the properties of resolution and eliminating harmful interference term. Another class of time-frequency energy distributions is the affine class of distributions. From the point of view of readability of analysis of the best properties are known so called Redistribution of energy caused by the use of a general methodology referred to as reassignment to any time-frequency representation. Reassigned distributions efficiently combine a reduction of the interference terms provided by a well adapted smoothing kernel and an increased concentration of the signal components.

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Speech Recognition by Wavelet Analysis

Cited by 25 publications

References 8 publications

Speech Emotion Recognition in Acted and Spontaneous Context

Speech Emotion Recognition in Acted and Spontaneous Context

Robust Speaker Identification using Denoised Wave Atom and GMM

Advanced Time-Frequency Representation in Voice Signal Analysis

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