Call For Help Detection In Emergent Situations Using Keyword Spotting And Paralinguistic Analysis

Nowadays, the safety of passengers within the enclosed public space, such as the elevator, becomes more and more important. Though the passengers can click the ”SOS” button to call the remote safety guard, the chances are that some passengers might lose their ability to stand up to click the button or it is not convenient to do so when in emergency situations. Also, people’s first reaction may be to call for help using voice instead of pressing the mayday button. Thus, we believe a speech-based system is very useful under this scenario. This work proposes a system using keyword spotting and paralinguistic analysis to detect whether the passenger calls for help in mandarin and gives real-time feedback which might provide the passenger within time help to prevent the accident. Unlike the standard keyword spotting task which is to detect the pre-defined call for help keyword ”jiu ming” in any scenario, we focus on detecting both the keyword and the paralinguistic states. The system will only be triggered when the keyword and the emergency situation such as shouting or screaming appear at the same time. To this end, we compare the performance of different methods and we find that the deep neural network-based small-footprint keyword spotting methods are effective and efficient for keyword spotting tasks under emotional scenarios.