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Training a bottleneck feature (BNF) extractor with multilingual data has been common in low resource keyword search. In a low resource application, the amount of transcribed target language data is limited while there are usually plenty of multilingual data. In this paper, we investigated two methods to train efficient multilingual BNF extractors for low resource keyword search. One method is to use...
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...
The framework of posteriorgram-based template matching has been shown to be successful for query-by-example spoken term detection (STD). This framework employs a tokenizer to convert query examples and test utterances into frame-level posteriorgrams, and applies dynamic time warping to match the query posteriorgrams with test posteriorgrams to locate possible occurrences of the query term. It is not...
Our aim in this paper is to propose a rule-weight learning algorithm in fuzzy rule-based classifiers. The proposed algorithm is presented in two modes: first, all training examples are assumed to be equally important and the algorithm attempts to minimize the error-rate of the classifier on the training data by adjusting the weight of each fuzzy rule in the rule-base, and second, a weight is assigned...
This paper extends our previous work on large margin estimation (LME) of GMM parameters with extend Baum-Welch (EBW) for spoken language recognition. To overcome the problem in the LME that negative samples in the training set are not used in parameter estimation, we propose a soft margin estimation (SME) method in this paper. The soft margin is scaled by a loss function measuring the distance between...
In this paper, we propose a subspace construction and selection strategy (SUBS) for speaker recognition with limited training and testing speech data. Based on the individual Gaussian distributions of Gaussian mixture model (GMM), each speaker's characteristic subspace is constructed by training an SVM using the corresponding Gaussian mean vectors from the GMMs of both enrollment and imposter speakers...
This paper presents a ??non-complicated?? automatic spoken language recognition system which can be effectively implemented using publicly available toolkits (such as HTK, SRILM and SVM-Light) and corpus resources (such as Switchboard, CallFriend, OHSU and NIST LRE07 speech corpora). This system involves two context-independent phone recognizers, a vector space modelling classifier and an equal weight...
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