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In this study, a new keyword spotting system (KWS) that utilizes phone confusion networks (PCNs) is presented. The new system exploits the compactness and accuracy of phone confusion networks to deliver fast and accurate results. Special design considerations are provided within the new algorithm to account for phone
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
This paper presents a new technique for preparing word templates to improve the performance of dynamic time warping based keyword spotting. The proposed technique selects one reference template from a small set of examples and in contrast to existing model based approaches does not require extensive training
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
. This paper presents an initial study using n-best recognition hypotheses for two tasks, extractive summarization and keyword extraction. We extend the approach used on 1-best output to n-best hypotheses: MMR (maximum marginal relevance) for summarization and TFIDF (term frequency, inverse document frequency) weighting for
This paper proposes a new keyword extraction method that uses bag-of-concept to extract keywords from Arabic text. The proposed algorithm utilizes semantic vector space model instead of traditional vector space model to group words into classes. The new method built word-context matrix where the synonym words will be
In this paper we describe the 2016 BBN conversational telephone speech keyword spotting system; the culmination of four years of research and development under the IARPA Babel program. The system was constructed in response to the NIST Open Keyword Search (OpenKWS) evaluation of 2016. We present our technological
Word posterior probability has been widely used as the confidence estimation of automatic speech recognition (ASR) systems and has been proved to be quite effective in related applications such as keyword search. However, word posterior probability tends to overestimate the true probability of a hypothesis, as it is
integrated feature set is obtained after normalization of both sets of features thus obtained. This integrated feature set is used in a Hidden Markov Modeling (HMM) framework along with a novel sliding syllable protocol for keyword spotting. Keyword spotting experiments are conducted on the Hindi language database developed for
We use query-by-example keyword spotting (QbyE-KWS) approach to solve the personalized wake-up word detection problem for small-footprint, low-computational cost on-device applications. QbyE-KWS takes keywords as templates, and matches the templates across an audio stream via DTW to see if the keyword is included. In
Keyword spotting refers to detection of all occurrences of any given word in a speech utterance. In this paper, we define the keyword spotting problem as a binary classification problem and propose a discriminative approach for solving it. Our approach exploits evolutionary algorithm to determine the separating hyper
spontaneous speech with momentous word error rate, which is a negative aspect of standard retrieval system. To prevail over such a constraint, we propose a method for spoken document retrieval based on spoken keyword spotting using Auto Associative Neural Networks (AANN). The proposed work concerns the exploit of the
Keyword extraction is widely used for information indexing, compressing, summarizing, etc. Existing keyword extraction techniques apply various text-based algorithms and metrics to locate the keywords. At the same time, some types of audio and audiovisual content, e. g. lectures, talks, interviews and other speech
We address the problem of keyword spotting in continuous speech streams when training and testing conditions can be different. We propose a keyword spotting algorithm based on sparse representation of speech signals in a time-frequency feature space. The training speech elements are jointly represented in a common
Keyword spotting is the task of identifying the occurrences of certain desired keywords in an arbitrary speech signal. Keyword spotting has many applications one of them is telephone routing. In particular, we consider a big company which receives thousands of telephone calls daily. We are interested with the
We present a novel approach to query-by-example keyword spotting (KWS) using a long short-term memory (LSTM) recurrent neural network-based feature extractor. In our approach, we represent each keyword using a fixed-length feature vector obtained by running the keyword audio through a word-based LSTM acoustic model
Keyword spotting in speech is a very well-researched problem, but there are almost no approaches for singing. Most speech-based approaches cannot be applied easily to singing because the phoneme durations in singing vary a lot more than in speech, especially the vowel durations. To represent expected phoneme durations
Summary keywords are words that are used in the reference extracted summary, therefore can be used to discriminate between summary sentences from non-summary ones. Finding these words is important for the extractive summarization algorithms that measure the importance of a sentence based on the importance of its
This paper presents an audio keywords detection method for highlight retrieval in basketball video. The keywords contain shoes squeaking sound, speech, cheer, long whistle and short whistle, which correspond to basketball game events. After feature analysis, the Simple Excellent Feature Combination based on Pearson
keywords which are used as features to distinguish different sports. Finally, based on the keyword spotting (KWS) results and specific keywords selected for each kind of sports, a score ranking strategy is designed for conducting classification automatically. For robust KWS in our system, adaptation techniques for acoustic
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