We investigate the use of intrinsic spectral analysis(ISA)for query-byexample spoken term detection(QbE-STD).In the task,spoken queries and test utterances in an audio archive are converted to ISA features,and dynamic time warping is applied to match the feature sequence in each query with those in test utterances.Motivated by manifold learning,ISA has been proposed to recover from untranscribed utterances a set of nonlinear basis functions for the speech manifold,and shown with improved phonetic separability and inherent speaker independence.