A method for detecting a voice signal, comprising: performing, in a unit of first timeframe frame length, framing on a continuous voice sample to obtain a plurality of first timeframes, detecting energy of each of the first timeframes, and determining a target first timeframe comprising a potential abrupt exception of a voice signal by analyzing a relationship between the energy of the plurality of first timeframes, wherein the potential abrupt exception of a voice signal comprises one of potential abrupt interruption, abrupt start, and abrupt stop of a voice signal, and wherein an abrupt interruption corresponds to an occurrence of a pair comprising an abrupt stop and abrupt start in the same section of a segment of the voice signal; performing, in a unit of second timeframe frame length, framing on the continuous voice sample to obtain a plurality of second timeframes, wherein a frame length of each of the second timeframes is an integral multiple of the first timeframe frame length, and a second timeframe comprising the target first timeframe is a target second timeframe; and processing each of the second timeframes to acquire a tone feature, wherein tone feature processing comprises performing a fast-Fourier transform on each one of the second timeframes to acquire a power density spectrum, determining a local maximum point according to the power density spectrum, and analyzing a segment of a frequency domain range centered on the local maximum point to determine whether a tonal component exists in a frequency band in which the local maximum point is located; and determining, by analyzing the acquired tone feature of at least one of the second timeframes comprising at least one of the target first timeframe, whether the potential abrupt exception of a voice signal comprised in the target first timeframe comprised in the target second timeframe is a real abrupt exception of a voice signal. performing, in a unit of first timeframe frame length, framing on a continuous voice sample to obtain a plurality of first timeframes, detecting energy of each of the first timeframes, and determining a target first timeframe comprising a potential abrupt exception of a voice signal by analyzing a relationship between the energy of the plurality of first timeframes, wherein the potential abrupt exception of a voice signal comprises one of potential abrupt interruption, abrupt start, and abrupt stop of a voice signal, and wherein an abrupt interruption corresponds to an occurrence of a pair comprising an abrupt stop and abrupt start in the same section of a segment of the voice signal; performing, in a unit of second timeframe frame length, framing on the continuous voice sample to obtain a plurality of second timeframes, wherein a frame length of each of the second timeframes is an integral multiple of the first timeframe frame length, and a second timeframe comprising the target first timeframe is a target second timeframe; and processing each of the second timeframes to acquire a tone feature, wherein tone feature processing comprises performing a fast-Fourier transform on each one of the second timeframes to acquire a power density spectrum, determining a local maximum point according to the power density spectrum, and analyzing a segment of a frequency domain range centered on the local maximum point to determine whether a tonal component exists in a frequency band in which the local maximum point is located; and determining, by analyzing the acquired tone feature of at least one of the second timeframes comprising at least one of the target first timeframe, whether the potential abrupt exception of a voice signal comprised in the target first timeframe comprised in the target second timeframe is a real abrupt exception of a voice signal.