Information bottleneck based age verification
نویسندگان
چکیده
Word N-gram models can be used for word-based age-group verification. In this paper the Agglomerative Information Bottleneck (AIB) approach is used to tackle one of the most fundamental drawbacks of word N-gram models: its abundant amount of irrelevant information. It is demonstrated that irrelevant information can be omitted by joining words to form word-clusters; this provides a mechanism to transform any sequence of words to a sequence of word-cluster labels. Consequently, word N-gram models are converted to wordcluster N-gram models which are more compact. Age verification experiments were conducted on the Fisher corpora. Their goal was to verify the age-group of the speaker of an unknown speech segment. In these experiments an Ngram model was compressed to a fifth of its original size without reducing the verification performance. In addition, a verification accuracy improvement is demonstrated by disposing irrelevant information.
منابع مشابه
An Information-Theoretic Discussion of Convolutional Bottleneck Features for Robust Speech Recognition
Convolutional Neural Networks (CNNs) have been shown their performance in speech recognition systems for extracting features, and also acoustic modeling. In addition, CNNs have been used for robust speech recognition and competitive results have been reported. Convolutive Bottleneck Network (CBN) is a kind of CNNs which has a bottleneck layer among its fully connected layers. The bottleneck fea...
متن کاملInvestigation of bottleneck features and multilingual deep neural networks for speaker verification
Recently, the integration of deep neural networks (DNNs) with i-vector systems is proved to be effective for speaker verification. This method uses the DNN with senone outputs to produce frame alignments for sufficient statistics extraction. However, two types of data mismatch may degrade the performance of the DNN-based speaker verification systems. First, the DNN requires transcribed training...
متن کاملDNN i-Vector Speaker Verification with Short, Text-Constrained Test Utterances
We investigate how to improve the performance of DNN ivector based speaker verification for short, text-constrained test utterances, e.g. connected digit strings. A text-constrained verification, due to its smaller, limited vocabulary, can deliver better performance than a text-independent one for a short utterance. We study the problem with “phonetically aware” Deep Neural Net (DNN) in its cap...
متن کاملDetecting and displaying novel computer attacks with Macroscope
Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Air Force. Abstract-Macroscope is a network-based intrusion detection system that uses Bottleneck Verification to detect user-to-superuser attacks. Bottleneck Verification (BV) detects novel computer attacks by looking for users performing high privilege ope...
متن کاملTime-Contrastive Learning Based Unsupervised DNN Feature Extraction for Speaker Verification
In this paper, we present a time-contrastive learning (TCL) based unsupervised bottleneck (BN) feature extraction method for speech signals with an application to speaker verification. The method exploits the temporal structure of a speech signal and more specifically, it trains deep neural networks (DNNs) to discriminate temporal events obtained by uniformly segmenting the signal without using...
متن کامل