Voice Controlled Vehicle Dashboard
Shridhar D. Pagar1, Shivani J. Pote2, Ankush S. Anmulwar3, Ashwini S. Shinde4
1Shridhar D. Pagar, Student ,Bachelor of Engineering, Electronics and Telecommunication, Pimpri Chinchwad College of Engineering Nigdi.
2Shivani J. Pote, Student ,Bachelor of Engineering, Electronics and Telecommunication, Pimpri Chinchwad College of Engineering Nigdi.
3Ankush S. Anmulwar , Student ,Bachelor of Engineering, Electronics and Telecommunication, Pimpri Chinchwad College of Engineering Nigdi.
4Mrs. Ashwini S. Shinde, Assistant Professor, Department of Electronics & Telecommunication, Pimpri Chinchwad College of Engineering Nigdi.
Manuscript received on April 13, 2020. | Revised Manuscript received on April 27, 2020. | Manuscript published on May 30, 2020. | PP: 1022-1027 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2148059120/2020©BEIESP | DOI: 10.35940/ijrte.A2148.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Driving a vehicle or a car has become tedious job nowadays due to heavy traffic so focus on driving is utmost important. This makes a scope for automation in Automobiles in minimizing human intervention in controlling the dashboard functions such as Headlamps, Indicators, Power window, Wiper System, and to make it possible this is a small effort from this paper to make driving distraction free using Voice controlled dashboard. and system proposed in this paper works on speech commands from the user (Driver or Passenger). As Speech Recognition system acts Human machine Interface (HMI) in this system hence this system makes use of Speaker recognition and Speech recognition for recognizing the command and recognize whether the command is coming from authenticated user(Driver or Passenger). System performs Feature Extraction and extracts speech features such Mel Frequency Cepstral Coefficients(MFCC),Power Spectral Density(PSD),Pitch, Spectrogram. Then further for Feature matching system uses Vector Quantization Linde Buzo Gray(VQLBG) algorithm. This algorithm makes use of Euclidean distance for calculating the distance between test feature and codebook feature. Then based on speech command recognized controller (Raspberry Pi-3b) activates the device driver for motor, Solenoid valve depending on function. This system is mainly aimed to work in low noise environment as most speech recognition systems suffer when noise is introduced. When it comes to speech recognition acoustics of the room matters a lot as recognition rate differs depending on acoustics. when several testing and simulation trials were taken for testing, system has speech recognition rate of 76.13%. This system encourages Automation of vehicle dashboard and hence making driving Distraction Free.
Keywords: Linde Buzo Gray (LBG), Mel Frequency Cepstral Coefficients (MFCC), Vector Quantization(VQ), Speaker Recognition, Speech Recognition.
Scope of the Article: Frequency Selective Surface