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Unsupervised Spoken Keyword Spotting via Segmental DTW on Gaussian Posteriorgrams
Unsupervised Spoken Keyword Spotting Segmental DTW Gaussian Posterior Grams
2014/11/27
In this paper, we present an unsupervised learning framework to address the problem of detecting spoken keywords. Without any transcription information, a Gaussian Mixture Model is trained to label sp...
Unsupervised Pattern Discovery in Speech
Speech processing unsupervised pattern discovery word acquisition
2014/11/27
We present a novel approach to speech processing based on the principle of pattern discovery. Our work represents a departure from traditional models of speech recognition, where the end goal is to cl...
Making Sense of Sound: Unsupervised Topic Segmentation over Acoustic Input
Making Sense of Sound Unsupervised Topic Segmentation over Acoustic Input
2014/11/27
We address the task of unsupervised topic segmentation of speech data operating over raw acoustic information. In contrast to existing algorithms for topic segmentation of speech, our approach does no...
UNSUPERVISED WORD ACQUISITION F ROM SPEECH USING PATTERN DISCOVERY
UNSUPERVISED WORD ACQUISITION SPEECH USING PATTERN
2014/11/27
In this paper, we present an unsupervised method for automatically discovering words from speech using a combination of acoustic pattern discovery, graph clustering, and baseform searching. The algori...
Unsupervised Learning of Dyadic Processes: Models, Methods, and Simulation
dyadic processes micro-social dynamics Hierarchical Dirichlet Hidden semi-Markov Model
2014/8/8
The dynamics that arise from dyadic processes, such as those
observed in married couples, generate a cascade of eectssome good
and some badon each partner, other family members, and other social ...
In this paper, we proposed a fast and robust unsupervised framework for anomaly detection and localization in crowed scenes. Our method avoids modeling the normal state of the crowds which is a very c...
Unsupervised texture classification: Automatically discover and classify texture patterns
Unsupervised texture classifi cation NMF PLSI Invariant descriptor
2013/7/17
In this paper, we present a novel approach to classify texture collections. This approach does not require experts to provide annotated training set. Given the image collection, we extract a set of in...