A feature selection method in spectro-temporal domain based on Gaussian Mixture Models

Esfandian, Nafiseh; Razzazi, Farbod; Behrad, Alireza; Valipour, Sara

doi:10.1109/icosp.2010.5656082

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Therefore, in recent researches, clustering methods were used to reduce dimension of spectro-temporal features space and extract valuable discriminative information of speech signal. In these methods, output of this model was considered as the primary features vectors and clustered using Gaussian Mixture Model and weighted K-Means [14][15][16][17]. Then, *Corresponding Author Institutional Email: na_esfandian@Qaemiau.ac.ir (N. Esfandian) the mean vectors and covariance matrices elements of the clusters are considered as secondary features in each speech frame.…”

Section: Introductionmentioning

confidence: 99%

A Clustering-Based Approach for Features Extraction in Spectro-Temporal Domain Using Artificial Neural Network

Esfandian

Hosseinpour²

2021

IJE

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In this paper, a new feature extraction method is presented based on spectro-temporal representation of speech signal for phoneme classification. In the proposed method, an artificial neural network approach is used to cluster spectro-temporal domain. Self-organizing map artificial neural network (SOM) was applied to clustering of features space. Scale, rate and frequency were used as spatial information of each point and the magnitude component was used as similarity attribute in clustering algorithm. Three mechanisms were considered to select attributes in spectro-temporal features space. Spatial information of clusters, the magnitude component of samples in spectro-temporal domain and the average of the amplitude components of each cluster points were considered as secondary features. The proposed features vectors were used for phonemes classification. The results demonstrate that a significant improvement is obtained in classification rate of different sets of phonemes in comparison to previous clustering-based methods. The obtained results of new features indicate the system error is compensated in all vowels and consonants subsets in compare to weighted K-means clustering.

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Section: Introductionmentioning

confidence: 99%

A Clustering-Based Approach for Features Extraction in Spectro-Temporal Domain Using Artificial Neural Network

Esfandian

Hosseinpour²

2021

IJE

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“…The high dimensionality of the cortical output of the auditory model makes the system impractical in this domain and affects the parameter estimation accuracy in the training phase of the phoneme classifier. In this study, the proposed method follows our previous research in which clustering methods had been used to cluster spectro-temporal feature space in order to extract secondary features vectors [11,12]. Therefore, the phoneme was presented using the attributes of speech clusters in this feature space.…”

Section: Introductionmentioning

confidence: 99%

Phoneme Classification Using Temporal Tracking of Speech Clusters in Spectro-temporal Domain

Esfandian

2020

IJE

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This article presents a new feature extraction technique based on the temporal tracking of clusters in spectro-temporal features space. In the proposed method, auditory cortical outputs were clustered. The attributes of speech clusters were extracted as secondary features. However, the shape and position of speech clusters change during the time. The clusters temporally tracked and temporal tracking parameters were considered in secondary features. The new architecture was proposed for phoneme classification by a combining classifier using both tracked and energy-based features. Clustered based spectro-temporal features vectors were used for the classification of several subsets of TIMIT database phonemes. The results show that the phoneme classification rate was improved Using tracked spectrotemporal features. The results were improved to 78.9% on voiced plosives classification which was relatively 3.3% higher than the results of non-tracked spectro-temporal feature vectors. The results on other subsets of phonemes showed good improvement in classification rate too.

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A clustering based feature selection method in spectro-temporal domain for speech recognition

Esfandian

Razzazi

Behrad

2012

Engineering Applications of Artificial Intelligence

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A feature selection method in spectro-temporal domain based on Gaussian Mixture Models

Cited by 4 publications

References 13 publications

A Clustering-Based Approach for Features Extraction in Spectro-Temporal Domain Using Artificial Neural Network

A Clustering-Based Approach for Features Extraction in Spectro-Temporal Domain Using Artificial Neural Network

Phoneme Classification Using Temporal Tracking of Speech Clusters in Spectro-temporal Domain

A clustering based feature selection method in spectro-temporal domain for speech recognition

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