Audio Segmentation for Speech Recognition Using Segment Features

Bhandari, Gayatri; Kawitkar, R. S.; Borawake, Madhuri P.

doi:10.1007/978-3-319-03095-1_23

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…This study explored and proposed the solution to these three problems mentioned above, inspired by a method used for audio signal analysis and thumbnail generation [ 2 , 3 , 4 , 5 ]. A moderately similar operation was introduced for speech recognition [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation

et al. 2022

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Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications. One of the universal preparation steps is data segmentation and labelling/annotation. This work proposes a practical and manageable way to automatically segment and label single-channel or multimodal biosignal data using a self-similarity matrix (SSM) computed with signals’ feature-based representation. Applied to public biosignal datasets and a benchmark for change point detection, the proposed approach delivered lucid visual support in interpreting the biosignals with the SSM while performing accurate automatic segmentation of biosignals with the help of the novelty function and associating the segments grounded on their similarity measures with the similarity profiles. The proposed method performed superior to other algorithms in most cases of a series of automatic biosignal segmentation tasks; of equal appeal is that it provides an intuitive visualization for information retrieval of multimodal biosignals.

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Section: Introductionmentioning

confidence: 99%

Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation

et al. 2022

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“…The CPD problem naturally arises in the context of many applied problems such as image analysis, speech recognition, human activity analysis, medical condition monitoring, financial and environmental data analysis, and modeling (see, e.g., Antoch & Jarušková, ; Aue, Horváth, Hušková, & Kokoszka, ; Bhandari, Kawitkar, & Borawake, ; Khapalova, Jandhyala, Fotopoulos, & Overland, ; MacNeill, ; Radke, Andra, Al‐Kofahi, & Roysam, ; Tran, ; Yang, Dumont, & Ansermino, ). When analyzing environmental data, change points may arise due to several factors such as changes in the experimental design (e.g., change in equipment, procedure, location, etc.)…”

Section: Introductionmentioning

confidence: 99%

Harnessing the power of topological data analysis to detect change points

2019

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We introduce a novel geometry-oriented methodology, based on the emerging tools of topological data analysis, into the change-point detection framework.The key rationale is that change points are likely to be associated with changes in geometry behind the data-generating process. While the applications of topological data analysis to change-point detection are potentially very broad, in this paper, we primarily focus on integrating topological concepts with the existing nonparametric methods for change-point detection. In particular, the proposed new geometry-oriented approach aims to enhance detection accuracy of distributional regime shift locations. Our simulation studies suggest that integration of topological data analysis with some existing algorithms for change-point detection leads to consistently more accurate detection results. We illustrate our new methodology in application to the two closely related environmental time series data sets-ice phenology of the Lake Baikal and the North Atlantic Oscillation indices, in a research query for a possible association between their estimated regime shift locations.

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