• Journal of Information Technology Services
• /
• v.17 no.1
• /
• pp.79-89
• /
• 2018

Sentiment analysis is a technique of text mining that extracts feelings of the person who wrote the sentence like movie review. The preliminary researches of sentiment analysis identify sentiments by using the dictionary which contains negative and positive words collected in advance. As researches on deep learning are actively carried out, sentiment analysis using deep learning model with morpheme or word unit has been done. However, this model has disadvantages in that the word dictionary varies according to the domain and the number of morphemes or words gets relatively larger than that of phonemes. Therefore, the size of the dictionary becomes large and the complexity of the model increases accordingly. We construct a sentiment analysis model using recurrent neural network by dividing input data into phoneme-level which is smaller than morpheme-level. To verify the performance, we use 30,000 movie reviews from the Korean biggest portal, Naver. Morpheme-level sentiment analysis model is also implemented and compared. As a result, the phoneme-level sentiment analysis model is superior to that of the morpheme-level, and in particular, the phoneme-level model using LSTM performs better than that of using GRU model. It is expected that Korean text processing based on a phoneme-level model can be applied to various text mining and language models.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.9
• /
• pp.3558-3562
• /
• 2010

The exact fundamental frequency (pitch) extraction is important in speech signal processing. However the exact pitch extraction from speech signal is very difficult due to the effect of formant and transitional amplitude. So in this paper, the pitch is detected after flattening the spectrum in frequency region by proposed algorithm for minimized harmonics variance value. Experimental result showed the proposed method appeared an outstanding performance in compared with LPC, Cepstrum. Also, the results show the proposed method is better than conventional method.

• Journal of Oriental Neuropsychiatry
• /
• v.16 no.2
• /
• pp.251-257
• /
• 2005

In this case study, We treated a transcortical aphasia patient with herbal medicine, acupunture and language therapy. We assessed the progress of the patient with Western Aphaia Battery(K-WAB), Boston Naming Test(BNT) and analysed the patient's speech at the series of pictures. The score of K-WAB and K-BNT was improved, the rate of statement at the theme of the picture was improved and the neologistic and verbal paraphasia was reduced. We think that the analysing the speech of the patient at the series of pictures to evaluate the practical problem of the patient would be useful. Further study is necessary about the utility of this assessment tools.

• The Journal of the Acoustical Society of Korea
• /
• v.8 no.5
• /
• pp.78-85
• /
• 1989

This paper describes a statistical methods and perception test for extracting the parameters to be used for the synthesis-by-rule of Korean plosives. Formant synthesizer is chosen for the synthesis of the phonemes. Speech materials for the analysis consists of 72 CV monosyllables from the single male speaker. The analysis is done mainly focused on the variation of parameters in time and frequency domain, then perception tests are executed to estimate the effects of variations of the formant transitions.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.2
• /
• pp.177-182
• /
• 2003

In this study, we research the robust speech recognition for the syllables and phoneme units with the feature parameter including the voiced-unvoiced measures for the confusable words. In order to make it possible, we propose the measure representing the voiced-unvoiced degree by using the HPS(Harmonic Product Spectrum) information, used on pitch detection. We proposed this measures with the sharpnes, peak count and height measure of HPS. We reconstructed the feature parameter including this measures, then we performs the speech recognition experiments and compared with the typical feature parameters under the CVC type confusable syllables DB.

• The Journal of the Acoustical Society of Korea
• /
• v.10 no.3
• /
• pp.31-36
• /
• 1991

We propose an efficient pre-classification algorithm extracting candidate words to reduce the recognition time in a large-vocabulary recognition system and also propose the use of spectral and temporal smoothing of the observation probability to improve its classification performance. The proposed algorithm computes the coarse likelihood score for each word in a lexicon using the observation probabilities of speech spectra and duration information of recognition units. With the proposed approach we could reduce the computational amount by 74% with slight degradation of recognition accuracy in 1160-word recognition system based on the phoneme-level HMM. Also, we observed that the proposed coarse likelihood score computation algorithm is a good estimator of the likelihood score computed by the Viterbi algorithm.

• The Journal of the Acoustical Society of Korea
• /
• v.24 no.1E
• /
• pp.28-33
• /
• 2005

This study was designed to suggest an assessment tool for analyzing the characteristics of Korean phonetic contrast intelligibility among dysarthric individuals. The intelligibility deficit factors of phonetic contrast in Korean dysarthric patients were analyzed through stepwise regression analysis. The 19 acoustic-phonetic contrasts proposed by Kent et al. (1999) have been claimed to be useful for clinical assessment and research on dysarthria. However, the test cannot be directly applied to Korean patients due to linguistic differences between English and Korean. Thus, it is necessary to devise a Korean word intelligibility test that reflects the distinct characteristics of the Korean language. To identify the speech error characteristics of a Korean dysarthric group, a Korean word list was audio-recorded by 3 spastic, 4 flaccid, and 5 mixed type of dysarthric patients. The word list consisted of monosyllabic consonant-vowel-consonant (CVC) real word pairs. Stimulus words included 41 phonemic contrast pairs and six triplets. The results showed that the percentage of errors in final position contrast was higher than in any other position. Unlike the results of previous studies, the initial-position contrasts were crucial in predicting the overall intelligibility among Korean patients.

• The Journal of the Acoustical Society of Korea
• /
• v.23 no.2
• /
• pp.187-191
• /
• 2004

This paper describes an efficient algorithm to generate compact and complete prompts lists for connected spoken words speech corpus. In building a connected spoken digit recognizer, we have to acquire speech data in various contexts. However, in many speech databases the lists are made by using random generators. We provide an efficient algorithm that can generate compact and complete lists of digits in various contexts. This paper includes the proof of optimality and completeness of the algorithm.

• The Journal of the Acoustical Society of Korea
• /
• v.21 no.2
• /
• pp.190-198
• /
• 2002

In general, we have used the Viterbi algorithm of Speech recognition for decoding. But a decoder in speaker verification has to recognize same word of every speaker differently. In this paper, we propose a noble decoding algorithm that could replace the typical Viterbi algorithm for the speaker verification system. We utilize for the proposed algorithm the speaker adaptation algorithms that transform feature vectors into the region of the client' characteristics in the speech recognition. There are many adaptation algorithms, but we take MLLR (Maximum Likelihood Linear Regression) and MAP (Maximum A-Posterior) adaptation algorithms for proposed algorithm. We could achieve improvement of performance about 30% of EER (Equal Error Rate) using proposed algorithm instead of the typical Viterbi algorithm.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.40 no.8
• /
• pp.782-791
• /
• 1991

In the fields of pattern recognition such as speech recognition, several new techniques using Artifical Neural network Models have been proposed and implemented. In particular, the Multilayer Perception Model has been shown to be effective in static speech pattern recognition. But speech has dynamic or temporal characteristics and the most important point in implementing speech recognition systems using Artificial Neural Network Models for continuous speech is the learning of dynamic characteristics and the distributed cues and contextual effects that result from temporal characteristics. But Recurrent Multilayer Perceptron Model is known to be able to learn sequence of pattern. In this paper, the results of applying the Recurrent Model which has possibilities of learning tedmporal characteristics of speech to phoneme recognition is presented. The test data consist of 144 Vowel+ Consonant + Vowel speech chains made up of 4 Korean monothongs and 9 Korean plosive consonants. The input parameters of Artificial Neural Network model used are the FFT coefficients, residual error and zero crossing rates. The Baseline model showed a recognition rate of 91% for volwels and 71% for plosive consonants of one male speaker. We obtained better recognition rates from various other experiments compared to the existing multilayer perceptron model, thus showed the recurrent model to be better suited to speech recognition. And the possibility of using Recurrent Models for speech recognition was experimented by changing the configuration of this baseline model.