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Query-by-Example Spoken Term Detection(QbE-STD) has been a hot research topic in speech recognition field. While template representation is the key composition part of QbE-STD, many researchers have been committed to developing effective template representations to obtain the better performance. Gaussian posteriorgram has been widely used due to that the GMM model which generates the Gaussian posteriorgram...
It is necessary to identify speech segments carrying important information for speech intelligibility, particularly in noise. Earlier work based on a relative rootmean-square (RMS) level based segmentation suggested that middle-level (ranging from the overall RMS level to 10 dB below) segments contained more vowel-consonant boundaries wherein the spectral change was often most prominent, and perhaps...
We propose strategies for a state-of-the-art keyword search (KWS) system developed by the SINGA team in the context of the 2014 NIST Open Keyword Search Evaluation (OpenKWS14) using conversational Tamil provided by the IARPA Babel program. To tackle low-resource challenges and the rich morphological nature of Tamil, we present highlights of our current KWS system, including: (1) Submodular optimization...
This paper considers an unsupervised data selection problem for the training data of an acoustic model and the vocabulary coverage of a keyword search system in low-resource settings. We propose to use Gaussian component index based n-grams as acoustic features in a submodular function for unsupervised data selection. The submodular function provides a near-optimal solution in terms of the objective...
In this paper, we propose to use acoustic feature based submodular function optimization to select a subset of untranscribed data for manual transcription, and retrain the initial acoustic model with the additional transcribed data. The acoustic features are obtained from an unsupervised Gaussian mixture model. We also integrate the acoustic features with the phonetic features, which are obtained...
A new front-end feature extraction scheme creating so called LDA-projected magnitude spectrum (L-PMS) features is proposed for speaker recognition systems. Mainstream feature extraction schemes usually use filter-bank or linear predictive coding (LPC) in the process of converting high-dimensional speech data into low-dimensional feature vectors, which may lose important discriminative information...
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