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Interview speech has become an important part of the NIST Speaker Recognition Evaluations (SREs). Unlike telephone speech, interview speech has substantially lower signal-to-noise ratio, which necessitates robust voice activity detection (VAD). This paper highlights the characteristics of interview speech files in NIST SREs and discusses the difficulties in performing speech/nonspeech segmentation in these files. To overcome these difficulties, this paper proposes using speech enhancement techniques as a preprocessing step for enhancing the reliability of energy-based and statistical-model-based VADs. It was found that spectral subtraction can make better use of the background spectrum than the likelihood-ratio tests in statistical-model-based VADs. A decision strategy is also proposed to overcome the undesirable effects caused by impulsive signals and sinusoidal background signals. Results on NIST 2010 SRE show that the proposed VAD outperforms the statistical-model-based VAD, the ETSI-AMR speech coder, and the ASR transcripts provided by NIST SRE Workshop.