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Most traditional template matching based keyword recognition methods don't need training data, just rely on frame matching. However, the recognition speed is relatively slow and it can't be used in practice. The LVCSR-based method needs to convert the speech signal into text signal before recognition, which has an
A keyword detection system with zero-resources techniques is presented. It consists of a primary alignment method and a later rescoring of its hipotheses. Both stages based on a segmental dynamic time warping method and a segmental model respectively. The resulting system is totally language independent and has no pre
Keyword spotting (KWS) refers to detection of a limited number of given keywords in speech utterances. In this paper, we evaluate a robust keyword spotting system based on hidden markov models for speaker independent Persian conversational telephone speech. Performance of base line keyword spotter is improved by means
This paper presents a robust keyword detection system for criminal scene analysis. The system follows the classical keyword spotting framework. A universal background model is designed and served as the filler model and anti-word model in keyword recognition and verification, respectively. Specifically, we analyze the
The paper presents unsupervised method for word detection in recorded spoken language signal. The method is based on examining signal similarity of two analyzed media description: registered voice and a word (textual query) synthesized by using Text-to-Speech tools. The descriptions of media were given by a sequence of Mel-Frequency Cepstral Coefficients or Human-Factor Cepstral Coefficients. Dynamic...
proposed approach uses a segmental DTW, wherein search is carried out only at syllable boundaries. This reduces the search complexity by 9 times compared to conventional sliding window DTW. The first pass of the proposed method uses a minimum set of templates for a keyword to search through the segmented audio. New templates
Turkish video lectures using a large vocabulary continuous speech recognition (LVCSR) system and finding keywords on the lattices obtained from the LVCSR system using a speech retrieval system based on keyword search. While developing this system, first a state-of-the-art LVCSR system was developed for Turkish using advance
Cepstral Coefficients(FBCC) is used in this paper. Here, from the spoken example of a keyword, segmental Dynamic Time Warping is used to compare the Gaussian Posteriorgrams, which are created from the FBCC feature vector. The keyword detection result obtained using MediaEval 2012 database shows that this system outperforms
methodology, reaching up to 91.9% average keyword accuracy on the Challenge test set at signal-to-noise ratios from −6 to 9 dB-the best result reported so far on these data.
Speech boundary detection contributes to performance of speech based applications such as speech recognition and speaker recognition. Speech boundary detector implemented in this study works on broadcast audio as a pre-processor module of a keyword spotter. Speech boundary detection is handled in 3 steps. At first
This paper presents a new method for Vietnamese text-dependent speaker recognition. The system is modeled for each speaker using mixture model Gaussian GMM (Gaussian Mixture Model). The phonemes in the keywords are represented by hidden Markov models HMM. The prior and posterior probabilities for keywords and speakers
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