Assessment of Syllable Intelligibility Based on Convolutional Neural Networks for Speech Rehabilitation After Speech Organs Surgical Interventions

Kostuchenko, Evgeny; Novokhrestova, Dariya; Pekarskikh, Svetlana; Shelupanov, Alexander; Немирович-Данченко, М М; Choynzonov, E. L.; Balatskaya, L. N.

doi:10.1007/978-3-030-26061-3_37

Cited by 2 publications

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“…To solve such problems, various algorithms and smart systems are used, for example, KNN [4] and NSGA-III [5]. However, in the modern world neural networks can be used to solve such problems, there are a lot of projects that use neural networks for different tasks: speaker verification [6], speech rehabilitation after speech organs surgical interventions [7], authorship identification [8], water level estimation in sewer pipes [9], determining the presence of lung cancer or diabetes in patients on the basis of symptoms [10,11], etc.…”

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confidence: 99%

Neural Network-Based Price Tag Data Analysis

et al. 2022

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This paper compares neural networks, specifically Unet, MobileNetV2, VGG16 and YOLOv4-tiny, for image segmentation as part of a study aimed at finding an optimal solution for price tag data analysis. The neural networks considered were trained on an individual dataset collected by the authors. Additionally, this paper covers the automatic image text recognition approach using EasyOCR API. Research revealed that the optimal network for segmentation is YOLOv4-tiny, featuring a cross validation accuracy of 96.92%. EasyOCR accuracy was also calculated and is 95.22%.

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Section: Introductionmentioning

confidence: 99%

Neural Network-Based Price Tag Data Analysis

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Metric of Highlighting the Synchronicity of Time Series and Its Application in Analyzing the Fundamental Frequencies of the Speaker’s Speech Signal

et al. 2020

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As part of the study, the problem of developing and applying a metric for assessing the degree of similarity of time series is considered, which makes it possible to consider the assumptions about the model of a series when comparing, as well as to compare the values of the corresponding characteristics of the series. Characteristics can be values that describe the structure of a series, or directly the values of the approximating function, which can be obtained using nonparametric statistics methods. One of the directions in which this approach can be applied to assessing the similarity of time series is the study of vocal performances. In practice, the degree of similarity in the performance of melodies by several speakers was analyzed. It was determined that, using the synchronicity metric, it is possible to implement an exercise in which students need to repeat the melody after the teacher. In addition, this approach was applied in the segment identification module with an abrupt change in the sounding of the fundamental frequency. This work is devoted to the modification of the program complex for vocal recognition in order to identify notes with a sharp change in the fundamental frequency. The complex is aimed at carrying out additional independent training in teaching vocals. The use of the software package will allow, in real time, providing feedback to the user with an assessment of the quality of their singing. This should allow students to study not only under the supervision of a teacher, but also independently in the early stages of learning. The basic algorithm of the program recognizes notes without sharp changes in frequencies with high accuracy, which is confirmed by experiments. In order to recognize by the algorithms of the program notes sung vibrato and glissando in singing, a new analysis method based on the metric of time series synchronicity is proposed.

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Assessment of Syllable Intelligibility Based on Convolutional Neural Networks for Speech Rehabilitation After Speech Organs Surgical Interventions

Cited by 2 publications

References 3 publications

Neural Network-Based Price Tag Data Analysis

Neural Network-Based Price Tag Data Analysis

Metric of Highlighting the Synchronicity of Time Series and Its Application in Analyzing the Fundamental Frequencies of the Speaker’s Speech Signal

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