Keyword spotting in singing with duration-modeled HMMs

Keyword spotting in speech is a very well-researched problem, but there are almost no approaches for singing. Most speech-based approaches cannot be applied easily to singing because the phoneme durations in singing vary a lot more than in speech, especially the vowel durations. To represent expected phoneme durations, several duration modeling techniques have been developed over the years in the field of ASR. To the best of our knowledge, these approaches have not been used for keyword spotting yet. In this paper, we present a new approach for keyword spotting in singing. We first extract various features (MFCC, TRAP, PLP, RASTA-PLP) and generate phoneme posteriograms from these features. We then perform keyword spotting on these posteriograms using keyword-filler HMMs and test two different duration modeling techniques on these HMMs: Explicit-duration modeling and Post-processor duration modeling. We evaluate our approach on a small singing data set without accompaniment.