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Voice activation using speaker recognition for controlling humanoid robot
Tuasikal D.A.A.a, Fakhrurroja H.a, MacHbub C.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 IEEE.Voice activation and speaker recognition are needed in many applications today. Speaker recognition is the process of automatically recognizing who speaks based on the voice signal. The introduction of these speakers is generally required on systems that use security and privacy. One example of this paper application is for activation and security in controlling humanoid robots. Voice recording process using Kinect 2.0. The first step in the speech recognition process is feature extraction. In this paper use Mel Frequency Cepstrum Coefficient (MFCC) on characteristic extraction process and Dynamic Time Warping (DTW) used as feature matching technique. The test was performed by 5 different speakers, with 2 types of words (‘aktifkan’ means activate and ‘hello slim’), and test with different recording distance (0.5m, 2m, 4m). Robot activation using two different types of words has an average accuracy of 91.5%. At the next difficulty level for testing the recording distance accuracy decreased from 97.5% to 85% to 65%.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Dynamic time warping,Humanoid robot,Kinect 2.0,Mel frequency cepstrum coefficients,Speaker recognition[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bioloid GP,Dynamic Time Warping (DTW),Humanoid Robot,Kinect 2.0,Mel Frequency Cepstrum Coefficient (MFCC),Speaker Recognition[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]ACKNOWLEDGMENT This work was supported by Program of Post Graduate Team Research 2018 from The Ministry of Research, Technology and Higher Education, Republic of Indonesia.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICSEngT.2018.8606366[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]