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Evaluating the accuracy of HMM-based and SVM-based spotters in detecting keywords and recognizing the true place of keyword occurrence shows that the HMM-based spotter detects the place of occurrence more precisely than the SVM-based spotter. On the other hand, the SVM-based spotter performs much better in detecting
We propose two simple methods to improve the performance of a keyword spotting system. In our application, the users are allowed to change the keywords anytime if they want. Thus we focused on phone-based GMM-HMM models since they do not require keyword-specific training data. However, the GMM-HMM based models usually
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
In this paper, a new method of Chinese prosodic word tagging is presented. This method consists of a rule-based algorithm named ??keyword anchor?? and a statistical algorithm based on hidden Markov model (HMM). For keyword anchor algorithm, an anchor of the prosodic word is defined to help the system to find the whole
Keyword spotting (KWS) is an essential technique for speech information retrieval. When doing offline keyword query on large volume spontaneous speech data, fast and accurate KWS methods are required. In this paper, a novel phone-state matrix based vocabulary-independent KWS method is proposed, which has merits of
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
In this paper, a new framework for large vocabulary keyword spotting is proposed, which involves three phases. In the first phase, N-best sub-word lattice is generated by hidden Markov model (HMM). Keyword candidates are hypothesized by dynamic keyword matching during the second phase. In the last phase, two-pass
In this paper we describe a systematic procedure to implement two-stage based keywords spotting system (KWS). In first stage, a phonetic decoding of continuous speech is obtained using a CD-DNN-HMM model built with the Kaldi toolkit. In second stage, these results of phonetic transcriptions will serve to construct a
classification/clustering as features. Also, this approach can be applied in keyword recommendation system in advertisement for different kinds of advertisers because of its expansibility and versatility.
a word-dependent system using the Arabic isolated word /ns10 as10 cs10 as10 ms10//[unk]/ a single keyword for the test utterance. This choice has been made because the word /ns10 as10 cs10 as10 ms10//[unk]/ is mostly used by the Arabic speakers. Speech features are extracted using MFCC. The HTK is used to implement the
approach compares the underlying acoustic models of keywords and a target database to alleviate the impact of mismatched vocabulary and language model, e.g. different domains. Experimental results on the Wall Street Journal (WSJ) database show that the proposed approach achieves a comparable performance, compared with the
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