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Voice Recognition for Wheelchair Control Theo Theodoridis, Xin Liu, and Huosheng Hu.

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Presentation on theme: "Voice Recognition for Wheelchair Control Theo Theodoridis, Xin Liu, and Huosheng Hu."— Presentation transcript:

1 Voice Recognition for Wheelchair Control Theo Theodoridis, Xin Liu, and Huosheng Hu

2 Contents Introduction Speech Recognition Structure Microsoft Speech SDK Testing

3 Introduction Task: To use voice recognition for controlling a wheelchair Purpose: To aid people with limited physical capability Software: The Microsoft Speech SDK Hardware: The Essex robotic wheelchair Experimentation: The Essex robotic arena

4 Speech Recognition Structure Driving components: - Start: Capture voice command - Sampling: Sample voice signal in real-time - Calculate energy: Validate signal’s presence - Calculate zero-crossing rate: Validate signal’s changes - Calculate entropy: Validate signal’s utterance - Speech recognition by parser: Microsoft Speech SDK - Driving: Execute the commands (Forw, Back, Left, Right, Stop) Speech Recognition flow chart Start Sampling real-time signals Calculate energy Calculate zero- crossing rate Calculate entropy Speech recognition by parser Driving

5 Microsoft Speech SDK Features: ∙ Developed by Microsoft’s Speech Technologies Group ∙ Aims to recognize audio speech and perform text-to-speech synthesizing ∙ This API can be used on common programming languages including C++ FFTW Core: ∙ FFTW is a ready-made library for computing discrete Fourier transform (DFT) ∙ Developed using the C++ language by MIT ∙ Can be used for increasing the running speed Recognition Accuracy: ∙ Four commands are employed for control ∙ Exceptional recognition accuracy ∙ Adequate real-time control CommandAccuracy Forw90% Back93% Right92% Left86% Stop90%

6 Microsoft Speech SDK Recognition Tests: ∙ Three noisy background cases applied: (a) Silent – no noise (66db) (b) Music – noisy background (76db) (c) Live Singing – very noisy background (76db) ∙ Accuracies achieved: (a) Silent – 91% at 66db (b) Music – 84% at 66-76db (c) Live Singing – 58% at 66-76db Overall accuracy: 77.7% TypeDecibelCommandAccuracy Rate Silent 58dB Move92% Stop90% Right92% Left86% One93% Music Equipment <66dB Move92% Stop88% Right92% Left85% One93% Move80% Stop78% Right80% Left75% One80% Sing Song <66dB Move80% Stop84% Right80% Left84% One83% Move28% Stop4% Right2% Left60% One75%

7 Testing Experimental Environment 1 Environment: A simple corridor with no obstacles Task: Reach destination at the same horizontal coordinate as the origin Experimental Environment 2 Environment: An open area with two obstacles Task: Avoid obstacles in a zigzag fashion and return back to the origin

8 Testing Test Time (sec) Environment 1 Time (sec) Environment 2 1135.1220.1 2134.9223.5 3134.5218.3 4135.3214.4 5126.4209.1 6123.1208.8 7114.8204.6 8116.1197.9 9112.9205.3 10115.3206.4 Average ≈ 2min≈ 3.5min

9 Thank You


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