• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2310-2332
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• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.617-623
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• 2024

Identifying emotions through speech poses a significant challenge due to the complex relationship between language and emotions. Our paper aims to take on this challenge by employing feature engineering to identify emotions in speech through a multimodal classification task involving both speech and text data. We evaluated two classifiers-Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM)-both integrated with a BERT-based pre-trained model. Our assessment covers various performance metrics (accuracy, F-score, precision, and recall) across different experimental setups). The findings highlight the impressive proficiency of two models in accurately discerning emotions from both text and speech data.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.2
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• pp.45-52
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• 2020

Since the opening of the national petition site, it has attracted much attention. In this paper, we perform topic analysis of the national petition site and propose a prediction model for answerable petitions based on deep learning. First, 1,500 petitions are collected, topics are extracted based on the petitions' contents. Main subjects are defined using K-means clustering algorithm, and detailed subjects are defined using topic modeling of petitions belonging to the main subjects. Also, long short-term memory (LSTM) is used for prediction of answerable petitions. Not only title and contents but also categories, length of text, and ratio of part of speech such as noun, adjective, adverb, verb are also used for the proposed model. Our experimental results show that the type 2 model using other features such as ratio of part of speech, length of text, and categories outperforms the type 1 model without other features.

• Phonetics and Speech Sciences
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• v.1 no.4
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• pp.47-59
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• 2009

The purpose of this paper is to propose an automatic evaluation technique for the prosodic aspect of an English sentence uttered by Korean speakers learning English. The underlying hypothesis is that the consistency of the manual prosody scoring is reflected in an imaginary space of prosody evaluation model constructed out of the three physical properties of the prosody considered in this paper, namely: the fundamental frequency (F0) contour, the intensity contour, and the segmental durations. The evaluation proceeds first by building a prosody evaluation model for the sentence. For the creation of the model, utterances from native speakers of English and Korean learners for the target sentence are manually scored by either native teachers of English or Korean phoneticians in terms of their prosody. Multiple native utterances from the manual scoring are selected as the "model" native utterances against which all the other Korean learners' utterances as well as the model utterances themselves can be semi-automatically evaluated by comparison in terms of the three prosodic aspects [7]. Each learner utterance, when compared to the multiple model native utterances, produces multiple coordinates in a three-dimensional space of prosody evaluation, each axis of which corresponds to the three prosodic aspects. The 3D coordinates from all the comparisons form a prosody evaluation model for the particular sentence and the associated manual scores can display regions of particular scores. The model can then be used as a predictive model against which other Korean utterances of the target sentence can be evaluated. The model from a Korean phonetician appears to support the hypothesis.

• Phonetics and Speech Sciences
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• v.7 no.4
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• pp.49-55
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• 2015

It is argued in this paper that, in determining semantic similarity, Korean words should be recategorized with a focus on the semantic relation to ontology in light of cross-linguistic morphological variations. It is proposed, in particular, that Korean semantic similarity should be measured on three tracks, human judgements track, relatedness track, and cross-part-of-speech relations track. As demonstrated in Yang et al. (2015), GloVe, the unsupervised learning machine on semantic similarity, is applicable to Korean with its performance being compared with human judgement results. Based on this compatability, it was further thought that the model's performance might most likely vary with different kinds of specific relations in different languages. An attempt was made to analyze them in terms of two major Korean-specific categories involved in their lexical and cross-POS-relations. It is concluded that languages must be analyzed by varying methods so that semantic components across languages may allow varying semantic distance in the vector space models.

• Speech Sciences
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• v.12 no.1
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• pp.75-87
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• 2005

In this study I investigate how native Korean listeners perceive English vowels /i/, /I/, and /$\epsilon$/. I extend Flege et al's (1997) study with synthesized /i/-/I/ and /I/-/$\epsilon$/ continua, and apply the results to Flege's (1995) Speech Learning Model (SLM). The statistical results show that native speakers of English rely more on spectral steps than on vowel duration when they identify the /i/-/I/ continuum, whereas native speakers of Korean rely more on vowel duration than on spectral steps when they identify the same continuum. In the case of the /I/-/$\epsilon$/ continuum, both groups rely on spectral steps when they identify the /$\epsilon$/, which supports the SLM; Koreans identified the /$\epsilon$/ categorically since Korean has the equivalent vowel. However, there was not statistical difference between Korean subjects with more English experience (KE) and those with less English experience in the identification of both continua. This contradicts the SLM, which posits that experienced L2 learners are better than inexperienced L2 learners in perception of L2 sounds. The exact nature of this should be further investigated in the SLM.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.842-857
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• 2023

Currently, most sentiment classification models on microblogging platforms analyze sentence parts of speech and emoticons without comprehending users' emotional inclinations and grasping moral nuances. This study proposes a hybrid sentiment analysis model. Given the distinct nature of microblog comments, the model employs a combined stop-word list and word2vec for word vectorization. To mitigate local information loss, the TextCNN model, devoid of pooling layers, is employed for local feature extraction, while BiLSTM is utilized for contextual feature extraction in deep learning. Subsequently, microblog comment sentiments are categorized using a classification layer. Given the binary classification task at the output layer and the numerous hidden layers within BiLSTM, the Tanh activation function is adopted in this model. Experimental findings demonstrate that the enhanced TextCNN-BiLSTM model attains a precision of 94.75%. This represents a 1.21%, 1.25%, and 1.25% enhancement in precision, recall, and F1 values, respectively, in comparison to the individual deep learning models TextCNN. Furthermore, it outperforms BiLSTM by 0.78%, 0.9%, and 0.9% in precision, recall, and F1 values.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.109-118
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• 2023

Cardiac arrest is a critical medical emergency where immediate response is essential for patient survival. This is especially true for Out-of-Hospital Cardiac Arrest (OHCA), for which the actions of emergency medical services in the early stages significantly impact outcomes. However, in Korea, a challenge arises due to a shortage of dispatcher who handle a large volume of emergency calls. In such situations, the implementation of a machine learning-based OHCA detection program can assist responders and improve patient survival rates. In this study, we address this challenge by developing a machine learning-based OHCA detection program. This program analyzes transcripts of conversations between responders and callers to identify instances of cardiac arrest. The proposed model includes an automatic transcription module for these conversations, a text-based cardiac arrest detection model, and the necessary server and client components for program deployment. Importantly, The experimental results demonstrate the model's effectiveness, achieving a performance score of 79.49% based on the F1 metric and reducing the time needed for cardiac arrest detection by 15 seconds compared to dispatcher. Despite working with a limited dataset, this research highlights the potential of a cardiac arrest detection program as a valuable tool for responders, ultimately enhancing cardiac arrest survival rates.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.981-984
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• 2010

In this paper, we used Support Vector Machines(SVMs) as the learning method, one of Artificial Neural Network, to segregated from the continuous speech into phonemes, an initial, medial, and final sound, and then, performed continuous speech recognition from it. A Decision boundary of phoneme is determined by algorithm with maximum frequency in a short interval. Speech recognition process is performed by Continuous Hidden Markov Model(CHMM), and we compared it with another phoneme segregated from the eye-measurement. From the simulation results, we confirmed that the method, SVMs, we proposed is more effective in an initial sound than Gaussian Mixture Models(GMMs).

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.1-10
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• 2022

Visually impaired people have very difficulty using retail stores due to the absence of braille information on products and any other support system. In this paper, we propose a basic algorithm for a system that recognizes products in retail stores and explains them as a voice. First, the deep learning model detects hand objects and product objects in the input image. Then, it finds a product object that most overlapping hand object by comparing the coordinate information of each detected object. We determine that this is a product selected by the user, and the system read the nutritional information of the product as Text-To-Speech. As a result of the evaluation, we confirmed a high performance of the learning model. The proposed algorithm can be actively used to build a system that supports the use of retail stores for the visually impaired.