• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.26 no.2
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• pp.117-121
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• 2015

Background : Cepstral analysis which is obtained from Fourier transformation of spectrum has been known to be effective indicator to analyze the voice disorder. To evaluate the voice disorder, phonation of sustained vowel /a/ sound or continuous speech have been used but the former was limited to capture hoarseness properly. This study is aimed to compare the effectiveness in analysis of cepstrum between the sustained vowel /a/ sound and continuous speech. Methods : From March 2012 to December 2014, total 72 patients was enrolled in this study, including 24 unilateral vocal cord palsy, vocal nodule and vocal polyp patients, respectively. The entire patient evaluated their voice quality by VHI (Voice Handicap Index) before and after treatment. Phonation of sustained vowel /a/ sample and continuous speech using the first sentence of autumn paragraph was subjected by cepstral analysis and compare the pre-treatment group and post-treatment group. Results : The measured values of pre and post treatment in CPP-a (cepstral peak prominence in /a/ vowel sound) was 13.80, 13.91 in vocal cord palsy, 16.62, 17.99 in vocal cord nodule, 14.19, 18.50 in vocal cord polyp respectively. Values of CPP-s (cepstral peak prominence in text-based speech) in pre and post treatment was 11.11, 12.09 in vocal cord palsy, 12.11, 14.09 in vocal cord nodule, 12.63, 14.17 in vocal cord polyp. All 72 patients showed subjective improvement in VHI after treatment. CPP-a showed statistical improvement only in vocal polyp group, but CPP-s showed statistical improvement in all three groups (p<0.05). Conclusion : In analysis of cepstrum, text-based analysis is more representative in voice disorder than vowel sound speech. So when the acoustic analysis of voice by cepstrum, both phonation of sustained vowel /a/ sound and text based speech should be performed to obtain more accurate result.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.297-299
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• 2021

Text classification is one of the popular tasks in Natural Language Processing (NLP) used to classify text or document applications such as sentiment analysis and email filtering. Nowadays, state-of-the-art (SOTA) Machine Learning (ML) and Deep Learning (DL) algorithms are the core engine used to perform these classification tasks with high accuracy, and they show satisfying results. This paper conducts a benchmarking performance's analysis of multiple SOTA algorithms on the first known labeled Korean voice phishing dataset called KorCCVi. Experimental results reveal performed on a test set of 366 samples reveal which algorithm performs the best considering the training time and metrics such as accuracy and F1 score.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4706-4724
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• 2020

With various structured data, such as the company size, loan balance, and savings accounts, the voice of customer (VOC), which is text data containing contact history and counseling details was analyzed in this study. To analyze unstructured data, the term frequency-inverse document frequency (TF-IDF) analysis, semantic network analysis, sentiment analysis, and a convolutional neural network (CNN) were implemented. A performance comparison of the models revealed that the predictive model using the CNN provided the best performance with regard to predictive power, followed by the model using the TF-IDF, and then the model using semantic network analysis. In particular, a character-level CNN and a word-level CNN were developed separately, and the character-level CNN exhibited better performance, according to an analysis for the Korean language. Moreover, a systematic selection model for optimal text mining techniques was proposed, suggesting which analytical technique is appropriate for analyzing text data depending on the context. This study also provides evidence that the results of previous studies, indicating that individual customers leave when their loyalty and switching cost are low, are also applicable to corporate customers and suggests that VOC data indicating customers' needs are very effective for predicting their behavior.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.432-439
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• 2020

This study aims at exploring features of Korean university students' written text, focusing on the written voice, rhetorical patterns, and writing practices through English letters. The data comprised examples of students' English job applications, and a 'purpose-will' model was adopted for the data analysis. The findings showed that the students used unique ways of strategies to convey their voice in a recontextualized setting. Their written voice in the job applications were various, and nobody applied the Korean convention of weather opening. Their rhetorical patterns were a transformation from convergence to divergence, showing integrated patterns of written voice. Students' writing practices revealed their internal values of writing for a task, and they do not directly learn from the teacher's syllabus. This supports the sociocultural framework that learning is a situated activity in a specific discourse community. The study concludes that writing teachers should understand that life-world and learning experience can impact on students' written voice and practices.

• Speech Sciences
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• v.2
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• pp.25-44
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• 1997

When listening the various speech synthesis systems developed and being used in our country, we find that though the quality of these systems has improved, they lack naturalness. Moreover, since the voice color of these systems are limited to only one recorded speech DB, it is necessary to record another speech DB to create different voice colors. 'Voice Color' is an abstract concept that characterizes voice personality. So speech synthesis systems need a voice color control function to create various voices. The aim of this study is to examine several factors of voice color control rules for the text-to-speech system which makes natural and various voice types for the sounding of synthetic speech. In order to find such rules from natural speech, glottal source parameters and frequency characteristics of the vocal tract for several voice colors have been studied. In this paper voice colors were catalogued as: deep, sonorous, thick, soft, harsh, high tone, shrill, and weak. For the voice source model, the LF-model was used and for the frequency characteristics of vocal tract, the formant frequencies, bandwidths, and amplitudes were used. These acoustic parameters were tested through multiple regression analysis to achieve the general relation between these parameters and voice colors.

• Speech Sciences
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• v.14 no.4
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• pp.169-178
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• 2007

This thesis describes the development of a service system for small-sized shops which support not only music broadcasting, but editing and generating voice announcement using the TTS(Text-To-Speech) technology. The system has been developed based on web environments with an easy access whenever and wherever it is needed. The system is able to control the sound using silverlight media player based on the ASP .NET 2.0 technology without any additional application software. Use of the Ajax control allows for multiple users to get the maximum load when needed. TTS is built in the server side so that the service can be provided without user's computer. Due to convenience and usefulness of the system, the business sector can provide better service to many shops. Further additional functions such as statistical analysis will undoubtedly help shop management provide desirable services.

• Cross-Cultural Studies
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• v.48
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• pp.223-252
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• 2017

This study explores effects of two types of chatbots - voice-based and text-based - on Korean EFL learners' speaking competence and learner perception. Participants were 80 freshmen students taking an English-speaking class at a university in Korea. They were divided into two experimental groups at random. During the sixteen-week experimental period, participants engaged in 10 chat sessions with the two different types of chatbots. To take a close examination of effects on the improvement of speaking competence, they took the TOEIC speaking test as pre- and post-tests. Structured questionnaire-based surveys were conducted before and after treatment to determine if there are changes in perception. Findings reveal two chatbots effectively contribute to improvement of speaking competence among EFL learners. Particularly, the voice-based chatbot was as effective as the text-based chatbot. An analysis of survey results indicates perception of chatbot-assisted language learning changed positively over time. In particular, most participants preferred voice-based chatbot over text-based chatbot. This study provides insight on the use of chatbots in EFL learning, suggesting that EFL teachers should integrate chatbot technology in their classrooms.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.197-210
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• 2016

With the advent of text analytics, VOC (Voice of Customer) data become an important resource which provides the managers and marketing practitioners with consumer's veiled opinion and requirements. In other words, making relevant use of VOC data potentially improves the customer responsiveness and satisfaction, each of which eventually improves business performance. However, unstructured data set such as customers' complaints in VOC data have seldom used in marketing practices such as predicting service time as an index of service quality. Because the VOC data which contains unstructured data is too complicated form. Also that needs convert unstructured data from structure data which difficult process. Hence, this study aims to propose a prediction model to improve the estimation accuracy of the level of customer satisfaction by combining unstructured from textmining with structured data features in VOC. Also the relationship between the unstructured, structured data and service processing time through the regression analysis. Text mining techniques, sentiment analysis, keyword extraction, classification algorithms, decision tree and multiple regression are considered and compared. For the experiment, we used actual VOC data in a company.

• Speech Sciences
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• v.8 no.3
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• pp.43-50
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• 2001

This paper deals with voice similarities between sisters who are supposed to have common physiological characteristics from a single biological mother. Nine pairs of sisters who are believed to have similar voices participated in this experiment. The speech samples obtained from one pair of sisters were eliminated in the analysis because their perceptual score was relatively low. The words were measured in both isolation and context, and the subjects were asked to read the text five times with about three seconds of interval between readings. Recordings were made at natural speed in a quiet room. The data were analyzed in pitch and formant frequencies using CSL (Computerized Speech Lab) and PCQuirer. It was found that data of the initial vowels are much more similar and homogeneous than those of vowels in other positions. The acoustic data showed that voice similarities are strikingly high in both pitch and formant frequencies. It is assumed that statistical data obtained from this experiment can be used as a guideline for modelling speaker identification and speaker verification.

• Dementia and Neurocognitive Disorders
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• v.23 no.3
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• pp.146-160
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• 2024

Background and Purpose: The emotions of people at various stages of dementia need to be effectively utilized for prevention, early intervention, and care planning. With technology available for understanding and addressing the emotional needs of people, this study aims to develop speech emotion recognition (SER) technology to classify emotions for people at high risk of dementia. Methods: Speech samples from people at high risk of dementia were categorized into distinct emotions via human auditory assessment, the outcomes of which were annotated for guided deep-learning method. The architecture incorporated convolutional neural network, long short-term memory, attention layers, and Wav2Vec2, a novel feature extractor to develop automated speech-emotion recognition. Results: Twenty-seven kinds of Emotions were found in the speech of the participants. These emotions were grouped into 6 detailed emotions: happiness, interest, sadness, frustration, anger, and neutrality, and further into 3 basic emotions: positive, negative, and neutral. To improve algorithmic performance, multiple learning approaches were applied using different data sources-voice and text-and varying the number of emotions. Ultimately, a 2-stage algorithm-initial text-based classification followed by voice-based analysis-achieved the highest accuracy, reaching 70%. Conclusions: The diverse emotions identified in this study were attributed to the characteristics of the participants and the method of data collection. The speech of people at high risk of dementia to companion robots also explains the relatively low performance of the SER algorithm. Accordingly, this study suggests the systematic and comprehensive construction of a dataset from people with dementia.