Small vocabulary isolated-word automatic speech recognition for single-word commands in Arabic spoken

Obaid, Mahmoud; Hodrob, Rami; Mwais, Allam Abu; Aldababsa, Mahmoud

doi:10.1007/s00500-023-07959-7

Cited by 4 publications

(1 citation statement)

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“…Dynamic Time Warping (DTW) measures the similarity between two time sequences, which might be obtained by sampling a source with varying sampling rates or by recording the same phenomenon occurring with varying speeds. It has a wide range of applications in speech recognition and action recognition [17][18][19][20][21]. However, there are few studies on the application of DTW in head gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

Head Gesture Recognition Combining Activity Detection and Dynamic Time Warping

Li,

2024

J. Imaging

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The recognition of head movements plays an important role in human–computer interface domains. The data collected with image sensors or inertial measurement unit (IMU) sensors are often used for identifying these types of actions. Compared with image processing methods, a recognition system using an IMU sensor has obvious advantages in terms of complexity, processing speed, and cost. In this paper, an IMU sensor is used to collect head movement data on the legs of glasses, and a new approach for recognizing head movements is proposed by combining activity detection and dynamic time warping (DTW). The activity detection of the time series of head movements is essentially based on the different characteristics exhibited by actions and noises. The DTW method estimates the warp path distances between the time series of the actions and the templates by warping under the time axis. Then, the types of head movements are determined by the minimum of these distances. The results show that a 100% accuracy was achieved in the task of classifying six types of head movements. This method provides a new option for head gesture recognition in current human–computer interfaces.

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