In this paper, we present a generic methodology to detect nonlinguistic vocalizations using ALISP (Automatic Language Independent Speech Processing), which is a data-driven audio segmentation approach. Using Maximum Likelihood Linear Regression (MLLR) and Maximum A Posterior (MAP) techniques, the proposed method adapts ALISP models, which then facilitate detection of local regions of nonlinguistic vocalizations with the standard Viterbi decoding algorithm. We also illustrate how a simple majority voting scheme, using a sliding window on ALISP sequences, can be helpful in eliminating outliers from the Viterbi-predicted sequence automatically. We evaluate the performance of our method on detection of laughter, a nonlinguistic vocalization, in comparison with global acoustic models such as GMMs, left-to-right HMMs and ergodic HMMs. The results indicate that adapted ALISP acoustic models perform better than global acoustic models in terms of F-measure. Moreover, our majority voting scheme on ALISP-sequences further improves the performance yielding, in total, an increase of 19.6%, 8.1% and 5.6% on the F-measure against global acoustic models GMMs, left-to-right HMMs, and ergodic HMMs respectively