Out Of Vocabulary Research Articles

Error Correction Methode Improve System using Out-of Vocabulary Rejection

In the generated model for the recognition vocabulary, tri-phones which is not make preparations are produced. Therefore this model does not generate an initial estimate of parameter words, and the system can not configure the model appear as disadvantages. As a result, the sophistication of the Gaussian model is fall will degrade recognition. In this system, we propose the error correction system using out-of vocabulary rejection algorithm. When the systems are creating a vocabulary recognition model, recognition rates are improved to refuse the vocabulary which is not registered. In addition, this system is seized the lexical analysis and meaning using probability distributions, and this system deactivates the string before phoneme change was applied. System analysis determine the rate of error correction using phoneme similarity rate and reliability, system performance comparison as a result of error correction rate improve represent 2.8% by method using error patterns, fault patterns, meaning patterns.

Uyghur Word Segmentation using a Combination of Rules and Statistics

Rich morphology of Uyghur produces a large number of words and leads to high out of vocabulary (OOV) rates that can cause many errors in Uyghur natural language processing (NLP). Morphological word segmentation is the very important component to overcome this problem caused by Uyghur morphology. This paper depicts some morphological rules by analyzing the universal structure of Uyghur words and presents a partly supervised word segmentation method. In this method, the suffix corpus was utilized to give all the possible morphological word segmentations, from which the optimal word segmentation is selected by the MAP-based model. In addition, cascaded language model was used to improve the accuracy of word segmentation. The test set composed of 5000 words was collected and segmented by hand. The experiment on this test set was given and experimental results show that the proposed method was more effective.

미등록어 거절 알고리즘에서 가우시안 모델 최적화를 이용한 신뢰도 정규화 향상

어휘 인식에서는 인식 학습 시 나타나지 않는 미 출현 트라이 폰이 존재하며, 이들 시스템에서는 모델 파라미터들의 초기 추정치를 생성하지 못하고 음소 데이터에 대한 모델을 구성할 수 없는 단점으로 인하여 가우시안 모델의 정확성을 확보하지 못하게 된다. 이를 개선하기 위하여 확률 분포를 이용한 모델 파라미터의 가우시안 모델 최적화 방법을 제안한다. 확률 분포의 가우시안 모델을 최적화하여 가우시안 모델의 정확성을 제공하고, 음소 단위로 데이터의 탐색을 지원하여 신뢰도가 향상되었다. 제안된 방법의 성능 평가를 위하여 실제 다양한 미등록어가 관측될 수 있는 대상으로 실험을 수행하였으며 본 연구에서 제안한 정규화 신뢰도를 이용한 미등록어 거절 알고리즘이 기존의 방법들에 비하여 평균 1.7%의 성능향상을 나타내었다. In vocabulary recognition has unseen tri-phone appeared when recognition training. This system has not been created beginning estimation figure of model parameter. It's bad points could not be created that model for phoneme data. Therefore it's could not be secured accuracy of Gaussian model. To improve suggested Gaussian model to optimized method of model parameter using probability distribution. To improved of confidence that Gaussian model to optimized of probability distribution to offer by accuracy and to support searching of phoneme data. This paper suggested system performance comparison as a result of recognition improve represent 1.7% by out-of vocabulary rejection algorithm using normalization confidence.

Automatic Word Decompounding for ASR in a Morphologically Rich Language: Application to Amharic

This paper investigates a data-driven word decompounding algorithm for use in automatic speech recognition. An existing algorithm, called ldquoMorfessor,rdquo has been enhanced in order to address the problem of increased phonetic confusability arising from word decompounding by incorporating phonetic properties and some constraints on recognition units derived from forced alignments experiments. Speech recognition experiments have been carried out on a broadcast news task for the Amharic language to validate the approach. The out of vocabulary (OOV) word rates were reduced by 35% to 50% and a small reduction in word error rate (WER) has been achieved. The algorithm is relatively language independent and requires minimal adaptation to be applied to other languages.

Approaches to reduce the effects of OOV queries on indexed spoken audio

We present several novel approaches to the Out of Vocabulary (OOV) query problem for spoken audio: indexing based on syllable-like units called particles and query expansion according to acoustic confusability for a word index. We also examine linear and OOV-based combination of indexing schemes. We experiment on 75 h of broadcast news, comparing our techniques to a word index, a phoneme index and a phoneme index queried with phoneme sequences. Our results show that our approaches are superior to both a word index and a phoneme index for OOV words, and have comparable performance to the sequence of phonemes scheme. The particle system has worse performance than the acoustic query expansion scheme. The best system uses word queries for in-vocabulary words and a linear combination of the phoneme sequence scheme and acoustic query expansion for OOV words. Using the best possible weights for linear combination, this system improves the average precision from 0.35 for a word index to 0.40, a result only obtainable if the weights could be learnt on a development query set. The next best system used a word index for in-vocabulary words and the phoneme sequence system otherwise and had average precision of 0.39.

Detection of unknown words in large vocabulary speech recognition.

This paper describes the relation between vocabulary sizes and detection errors of unknown words in large vocabulary speech recognition through recognition and detection experiments. Although the relation between vocabulary sizes and recognition performances has been reported, the relation between vocabulary sizes and detection performances has not yet been studied. Especially, it has not for the cases of vocabulary sizes of over 1, 000 words. Experiments were conducted using the speech material of speaker MAU's ATR word speech database. The entries of the dictionary used is 40, 000 words from the Shinmeikai Japanese Language Dictionary. It is shown that when the vocabulary size increases from 1, 000 words to 40, 000 words, the relation between vocabulary sizes and detection errors has a similar tendency with the relation between vocabulary sizes and recognition errors. And increases of detection errors caused by increases of vocabulary sizes are shown to be small for the case of within vocabulary, compared with increases of detection errors for the case of out of vocabulary. These results should be taken into accounts in designing large vocabulary speech recognition systems including unknown word processing.