Voice activity detection based on improved speech features and extreme learning machine

Voice Activity Detection (VAD) refers to the determination of the existence of speech in a given speech signal frame. Robust VAD helps to improve the automation efficiency of speech applications, such as speech enhancement, speaker recognition, and hearing aids and so on. In order to improve the accuracy and efficiency of voice activity detection under low SNR, a new speech feature—Low Frequency De-noising Energy (LFDE) is proposed, which is applied to VAD and utilizes LFDE and existing acoustic features (Mel frequency cepstrum parameters, formants Frequency) combined with the Extreme Learning Machine (ELM) classifier.Simulation experiments show that the accuracy and efficiency of voice activity detection are improved.