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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
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...
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.
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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