  The present study tackles the problem of automatically discovering spoken
keywords from untranscribed audio archives without requiring word-by-word
speech transcription by automatic speech recognition (ASR) technology. The
problem is of practical significance in many applications of speech analytics,
including those concerning low-resource languages, and large amount of
multilingual and multi-genre data. We propose a two-stage approach, which
comprises unsupervised acoustic modeling and decoding, followed by pattern
mining in acoustic unit sequences. The whole process starts by deriving and
modeling a set of subword-level speech units with untranscribed data. With the
unsupervisedly trained acoustic models, a given audio archive is represented by
a pseudo transcription, from which spoken keywords can be discovered by string
mining algorithms. For unsupervised acoustic modeling, a deep neural network
trained by multilingual speech corpora is used to generate speech segmentation
and compute bottleneck features for segment clustering. Experimental results
show that the proposed system is able to effectively extract topic-related
words and phrases from the lecture recordings on MIT OpenCourseWare.
