Research on truncated speech in speaker verification

Summary form only given. The speech truncating phenomenon is a general problem is practical speaker recognition system. After the speech was truncated by amplitude, the spectral was changed during the process, resulting in the decreasing in the system`s performance. The paper describes the observation and the conclusion on the impact of the truncated segments, studies the reason of the impact on the recognition performance, gives out the ways of the truncated segments detection and reducing the decreasing of the performance. The simulation on NIST SRE08 shows that, just when the amplitude truncating ratio remains high (more than the 80% of the maximum amplitude), the performance drops sharply; the performance of traditional GMM-UBM system and I-vector system behavior familiar when the amplitude truncating is low, while I-vector gives a better robustness when is high. The paper gives out a proposal on truncating segments detection based on subspace discriminant information, which is then used to discard the truncating segments. The experiments show that this proposal could well detect the truncated segments. However, the results show that there are still speaker discriminant information in the truncated segments, when the amplitude truncated ratio remains low, it´s better to remain the data to sustain the performance, otherwise, the speaker should take another recording to keep the system performance.