• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.65-68
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• 2004

본 논문에서는 반복학습으로 음소모델을 강건하게 하여 음소기반 핵심어 검출 시스템의 성능을 개선하고자 하였다. 가변어휘 핵심어 검출 시스템은 인식 대상 핵심어의 추가와 변경이 용이하도록 모노폰 단위로 핵심어 모델과 필러 모델을 구성하였다. 핵심어 모델과 필러 모델은 동일한 음소모델을 이용하므로 각각의 음소 모델의 분별력 향상은 핵심어 검출 성능과 밀접한 관계에 있다. 따라서 본 논문에서는 음소 HMM(Hidden Markov Model)의 학습시에 반복 학습을 통하여 음소 모델을 강건하게 만든 후 핵심어 검출 실험을 수행하였다. 그 결과, 10회의 반복학습을 통하여 얻어진 음소 HMM을 이용한 핵심어 검출의 성능은 반복학습을 하지 않은 경우보다 핵심어 검출의 CA-CR 평균 성능이 $4\%$ 향상됨을 확인할 수 있었다.

• The Journal of the Acoustical Society of Korea
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• v.23 no.3
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• pp.242-247
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• 2004

Conventional keyword spotting systems use the connected word recognition network consisted by keyword models and filler models in keyword spotting. This is why the system can not construct the language models of word appearance effectively for detecting keywords in large vocabulary continuous speech recognition system with large text data. In this paper to solve this problem, we propose a keyword spotting system using pseudo N-gram language model for detecting key-words and investigate the performance of the system upon the changes of the frequencies of appearances of both keywords and filler models. As the results, when the Unigram probability of keywords and filler models were set to 0.2, 0.8, the experimental results showed that CA (Correctly Accept for In-Vocabulary) and CR (Correctly Reject for Out-Of-Vocabulary) were 91.1% and 91.7% respectively, which means that our proposed system can get 14% of improved average CA-CR performance than conventional methods in ERR (Error Reduction Rate).

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.319-324
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• 2006

We Propose two new methods for non-keyword modeling to improve the performance of speaker- and vocabulary-independent keyword spotting system. The first method is decision tree clustering of monophone at the state level instead of monophone clustering method based on K-means algorithm. The second method is multi-state multiple mixture modeling at the syllable level rather than single state multiple mixture model for the non-keyword. To evaluate our method, we used the ETRI speech DB for training and keyword spotting test (closed test) . We also conduct an open test to spot 100 keywords with 400 sentences uttered by 4 speakers in an of fce environment. The experimental results showed that the decision tree-based state clustering method improve 28%/29% (closed/open test) than the monophone clustering method based K-means algorithm in keyword spotting. And multi-state non-keyword modeling at the syllable level improve 22%/2% (closed/open test) than single state model for the non-keyword. These results show that two proposed methods achieve the improvement of keyword spotting performance.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.305-308
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• 1998

본 논문에서는 핵심어 검출의 성능을 향상시키기 위한 새로운 후처리 방법을 제안한다. 일반적으로 핵심어 검출 시스템에 의해 검출된 상위 n개의 후보 단어들의 우도(likelihood)는 비슷한 경우가 많다. 따라서, 한 음성구간에 대해 음향학적으로 유사한 핵심어들간의 오인식 가능성이 높아진다. 그러나 기존의 핵심어 검출에 사용된 후처리 방법은 음성의 모든 구간에 같은 비중을 두고 우도를 평가하므로 비슷한 음향학적 특징을 가지는 유사한 핵심어들의 비교에 적합하지 못하다. 이를 해결하기 위하여, 본 논문에서는 후보단어들의 부분적인 음향학적 특징 차이에 기반한 가중치를 우도 계산 시에 반영함으로써 보다 변별력을 높이는 알고리즘을 제안한다. 실험 결과, 제안된 방법을 이용하여 유사한 후보단어들간의 변별력을 높일 수 있었고, 인식율이 93%일 때, 우도비검사 방법에 비해 19.6%의 false alarm rate을 감소시킬 수 있었다.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.167-170
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• 2000

본 논문에서는 triphone을 기본단위로 하는 HMM에 의해 핵심어 모델을 구성하고, 사용자가 임의로 핵심어를 추가 및 변경할 수 있도록 가변어휘 핵심어 검출기를 구현하였다. 비핵심어 모델링 방법으로 monophone clustering을 사용한 방법 및 GMM을 사용한 방법의 성능을 비교하였다. 또한 후처리 과정에서 가변어휘 인식구조에 적합한 anti-subword 모델을 사용하였으며 몇 가지 구현방식에 따른 후처리 성능을 검토하였다. 실험결과 비핵심어 모델로 monophone을 clustering하여 사용한 방법보다 GMM을 사용한 경우 약간의 인식성능 개선을 얻을 수 있었으며, 후처리 과정에서 Kullback distance를 이용한 anti-subword 모델링 방식이 다른 방식에 비해 우수한 결과를 나타냈다.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.81-84
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• 2001

본 논문은 중규모 단어급의 핵심어 검출 시스템에서 인식률 향상을 위해 미등록어 거절(Out-of-Vocabulary rejection) 기능을 제어하기 위한 연구이다. 이것은 핵심어 검출기에서 인식된 결과를 확인하는 과정으로 검증시스템이 구현되기 위해서는 매 음소마다 검증 기능이 필요하고, 이를 위해서 반음소(anti-phoneme model) 모델을 사용하였다. 검증의 역할은 인식기에서 인식된 단어가 등록어인지 미등록어인지 판별하는 것이다. 단어인식기는 비터비 탐색을 하므로, 기본적으로 단어단위로 인식을 하지만 그 인식된 단어는 내부적으로 음소단위로 인식된다. 따라서, 최소 검증 오류를 갖는 반음소 모델을 사용하고, 이를 이용하여 인식된 음소 단위들을 각각의 반음소 모델과 비교하여 통계적인 방법에 의해 신뢰도를 구한다 이 음소단위의 신뢰도를 단어 단위의 신뢰도로 환산하기 위해서 음소단위를 평균 내는 방식 을 취한다. 이렇게 함으로서, 등록어와 미등록어 사이의 분별력을 크게 하여 향상된 인식 성능을 얻었다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.209-219
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• 2004

In order to implement a real time hardware for keyword spotting, we propose a Single-End-Point DTW(SEP-DTW) algorithm which is simple and less complex for computation. The SEP-DTW algorithm only needs a single end point which enables efficient applications, and it has a small wont of computations because the global search area is divided into successive local search areas. Also, we adopt new local constraints and a new distance measure for a better performance of the SEP-DTW algorithm. Besides, we make a normalization of feature same vectors so that they have the same variance in each frequency bin, and each frame has the same energy levels. To construct several reference patterns for each keyword, we use a clustering algorithm for all training patterns, and mean vectors in every cluster are taken as reference patterns. In order to detect a key word for input streams of speech, we measure the distances between reference patterns and input pattern, and we make a decision whether the distances are smaller than a pre-defined threshold value. With isolated speech recognition and keyword spotting experiments, we verify that the proposed algorithm has a better performance than other methods.

• Journal of Korea Multimedia Society
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• v.13 no.9
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• pp.1322-1328
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• 2010

Recently, keyword spotting system is greatly in the limelight as UI(User Interface) technology of ubiquitous home network system. Keyword spotting system is vulnerable to non-stationary noises such as TV, radio, dialogue. Especially, speech recognition rate goes down drastically under the embedded DSP(Digital Signal Processor) environments because it is relatively low in the computational capability to process input speech in real-time. In this paper, we propose a new keyword spotting system using the call-command method, which is consisted of small number of recognition networks. We select the call-command such as 'narae', 'home manager' and compose the small network as a token which is consisted of silence with the noise and call commands to carry the real-time recognition continuously for input speeches.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.381-388
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• 2003

This paper presents a method of giving weights to garbage class clustering and Filler model to improve performance of keyword spotting system and a time-saving method of dialogue speech processing system for keyword spotting by calculating keyword transition probability through speech analysis of task domain users. The point of the method is grouping phonemes with phonetic similarities, which is effective in sensing similar phoneme groups rather than individual phonemes, and the paper aims to suggest five groups of phonemes obtained from the analysis of speech sentences in use in Korean morphology and in stock-trading speech processing system. Besides, task-subject Filler model weights are added to the phoneme groups, and keyword transition probability included in consecutive speech sentences is calculated and applied to the system in order to save time for system processing. To evaluate performance of the suggested system, corpus of 4,970 sentences was built to be used in task domains and a test was conducted with subjects of five people in their twenties and thirties. As a result, FOM with the weights on proposed five phoneme groups accounts for 85%, which has better performance than seven phoneme groups of Yapanel [1] with 88.5% and a little bit poorer performance than LVCSR with 89.8%. Even in calculation time, FOM reaches 0.70 seconds than 0.72 of seven phoneme groups. Lastly, it is also confirmed in a time-saving test that time is saved by 0.04 to 0.07 seconds when keyword transition probability is applied.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.380-386
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• 2002

Conventional post-processing as like confidence measure (CM) proposed by Rahim calculates phones' CM using the likelihood between phoneme model and anti-model, and then word's CM is obtained by averaging phone-level CMs[1]. In conventional method, CMs of some specific keywords are tory low and they are usually rejected. The reason is that statistics of phone-level CMs are not consistent. In other words, phone-level CMs have different probability density functions (pdf) for each phone, especially sri-phone. To overcome this problem, in this paper, we propose normalized confidence measure. Our approach is to transform CM pdf of each tri-phone to the same pdf under the assumption that CM pdfs are Gaussian. For evaluating our method we use common keyword spotting system. In that system context-dependent HMM models are used for modeling keyword utterance and contort-independent HMM models are applied to non-keyword utterance. The experiment results show that the proposed NCM reduced FAR (false alarm rate) from 0.44 to 0.33 FA/KW/HR (false alarm/keyword/hour) when MDR is about 8%. It achieves 25% improvement of FAR.