Hierarchical ANN system for stuttering identification

Wietlicka, Izabela; Kuniszyk-JóKowiak, WiesłAwa; SmołKa, Elbieta

doi:10.1016/j.csl.2012.05.003

Cited by 23 publications

(15 citation statements)

References 60 publications

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“…The results of our proposed method were compared with those of three high-performance researches[789] in automatic dysfluency classification. Hariharan et al .…”

Section: Methodsmentioning

confidence: 99%

“…Świetlicka et al . [9] utilized amplitudes of weight filters on Fourier transform of speech frames (i.e., FFT measures) as the feature set, as well as an order of Kohonen and ANN for feature selection and classification, respectively. In these studies, the prolongations were segmented manually.…”

Section: Methodsmentioning

confidence: 99%

“…[5] To support objective classification of speech dysfluencies, several researches have been performed on the classification of prolongation either individually[67] or in combination with other dysfluencies. [8910]…”

Section: Introductionmentioning

confidence: 99%

“…The segments were then labeled as prolongation if the duration of highly similar successive frames exceeded a threshold specified by the speaking rate. All previous studies have employed either a fixed threshold for minimum sustaining duration in sounds[67] or classifiers that were trained on predefined speaking rates,[891012] although it must actually be judged according to how slow or fast the individual’s speech is. The performance of the proposed method was appraised using English and Persian language databases by comparing our results with those of three accurate methods[789] from the previous researches.…”

Section: Introductionmentioning

confidence: 99%

“…All previous studies have employed either a fixed threshold for minimum sustaining duration in sounds[67] or classifiers that were trained on predefined speaking rates,[891012] although it must actually be judged according to how slow or fast the individual’s speech is. The performance of the proposed method was appraised using English and Persian language databases by comparing our results with those of three accurate methods[789] from the previous researches. This paper is organized as follows: the proposed method and databases are explained in the next section; results of experiments and its comparison with other researches are given in the following section; and finally, the last section contains the conclusion of this study.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Automatic Prolongation Detection Approach in Continuous Speech With Robustness Against Speaking Rate Variations

2017

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In recent years, many methods have been introduced for supporting the diagnosis of stuttering for automatic detection of prolongation in the speech of people who stutter. However, less attention has been paid to treatment processes in which clients learn to speak more slowly. The aim of this study was to develop a method to help speech-language pathologists (SLPs) during diagnosis and treatment sessions. To this end, speech signals were initially parameterized to perceptual linear predictive (PLP) features. To detect the prolonged segments, the similarities between successive frames of speech signals were calculated based on correlation similarity measures. The segments were labeled as prolongation when the duration of highly similar successive frames exceeded a threshold specified by the speaking rate. The proposed method was evaluated by UCLASS and self-recorded Persian speech databases. The results were also compared with three high-performance studies in automatic prolongation detection. The best accuracies of prolongation detection were 99 and 97.1% for UCLASS and Persian databases, respectively. The proposed method also indicated promising robustness against artificial variation of speaking rate from 70 to 130% of normal speaking rate.

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“…The results of our proposed method were compared with those of three high-performance researches[789] in automatic dysfluency classification. Hariharan et al .…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%