Speech Recognition Algorithm of Substation Inspection Robot Based on Improved DTW

Han, Lei; Gao, Changchun; Zhang, Shujing; Li, Dongsong; Sun, Zhizhou; Yang, Guoqing; Li, Jian; Zhang, Chuanyou; Shao, Guangting

doi:10.1007/978-3-030-02804-6_6

Cited by 3 publications

(1 citation statement)

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“…The amount of warping needed for the alignment is then used as a measure of the difference between the two time-series. A typical application of DTW is speech recognition [2], where it is used to determine if two waveforms represent the same spoken phrase. In a speech waveform, the duration of each spoken sound and the interval between sounds can vary, but the overall speech waveforms must have a similar “shape”.…”

Section: Introductionmentioning

confidence: 99%

Efficient Kernel-Based Subsequence Search for Enabling Health Monitoring Services in IoT-Based Home Setting

Candelieri

Fedorov

Messina

2019

Sensors

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This paper presents an efficient approach for subsequence search in data streams. The problem consists of identifying coherent repetitions of a given reference time-series, also in the multivariate case, within a longer data stream. The most widely adopted metric to address this problem is Dynamic Time Warping (DTW), but its computational complexity is a well-known issue. In this paper, we present an approach aimed at learning a kernel approximating DTW for efficiently analyzing streaming data collected from wearable sensors, while reducing the burden of DTW computation. Contrary to kernel, DTW allows for comparing two time-series with different length. To enable the use of kernel for comparing two time-series with different length, a feature embedding is required in order to obtain a fixed length vector representation. Each vector component is the DTW between the given time-series and a set of “basis” series, randomly chosen. The approach has been validated on two benchmark datasets and on a real-life application for supporting self-rehabilitation in elderly subjects has been addressed. A comparison with traditional DTW implementations and other state-of-the-art algorithms is provided: results show a slight decrease in accuracy, which is counterbalanced by a significant reduction in computational costs.

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Section: Introductionmentioning

confidence: 99%

Efficient Kernel-Based Subsequence Search for Enabling Health Monitoring Services in IoT-Based Home Setting

Candelieri

Fedorov

Messina

2019

Sensors

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The morphing for continuous generalization of linear features based on Dynamic Time Warping distance

Mao

2023

Geocarto International

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VarASV: Enabling Pitch-variable Automatic Speaker Verification via Multi-task Learning

Gao

et al. 2021

2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2)

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Speech Recognition Algorithm of Substation Inspection Robot Based on Improved DTW

Cited by 3 publications

References 0 publications

Efficient Kernel-Based Subsequence Search for Enabling Health Monitoring Services in IoT-Based Home Setting

Efficient Kernel-Based Subsequence Search for Enabling Health Monitoring Services in IoT-Based Home Setting

The morphing for continuous generalization of linear features based on Dynamic Time Warping distance

VarASV: Enabling Pitch-variable Automatic Speaker Verification via Multi-task Learning

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