• Phonetics and Speech Sciences
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• v.12 no.4
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• pp.81-90
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• 2020

This paper describes a method of building Korean conversational speech data in the emergency medical domain and proposes an annotation method for the collected data in order to improve speech recognition performance. To suggest future research directions, baseline speech recognition experiments were conducted by using partial data that were collected and annotated. All voices were recorded at 16-bit resolution at 16 kHz sampling rate. A total of 166 conversations were collected, amounting to 8 hours and 35 minutes. Various information was manually transcribed such as orthography, pronunciation, dialect, noise, and medical information using Praat. Baseline speech recognition experiments were used to depict problems related to speech recognition in the emergency medical domain. The Korean conversational speech data presented in this paper are first-stage data in the emergency medical domain and are expected to be used as training data for developing conversational systems for emergency medical applications.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.267-286
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• 2023

The conversational agents such as AI speakers utilize voice conversation for human-computer interaction. Voice recognition errors often occur in conversational situations. Recognition errors in user utterance records can be categorized into two types. The first type is misrecognition errors, where the agent fails to recognize the user's speech entirely. The second type is misinterpretation errors, where the user's speech is recognized and services are provided, but the interpretation differs from the user's intention. Among these, misinterpretation errors require separate error detection as they are recorded as successful service interactions. In this study, various text separation methods were applied to detect misinterpretation. For each of these text separation methods, the similarity of consecutive speech pairs using word embedding and document embedding techniques, which convert words and documents into vectors. This approach goes beyond simple word-based similarity calculation to explore a new method for detecting misinterpretation errors. The research method involved utilizing real user utterance records to train and develop a detection model by applying patterns of misinterpretation error causes. The results revealed that the most significant analysis result was obtained through initial consonant extraction for detecting misinterpretation errors caused by the use of unregistered neologisms. Through comparison with other separation methods, different error types could be observed. This study has two main implications. First, for misinterpretation errors that are difficult to detect due to lack of recognition, the study proposed diverse text separation methods and found a novel method that improved performance remarkably. Second, if this is applied to conversational agents or voice recognition services requiring neologism detection, patterns of errors occurring from the voice recognition stage can be specified. The study proposed and verified that even if not categorized as errors, services can be provided according to user-desired results.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.59-70
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• 2018

With the breakthrough of speech recognition technology, conversational agents have become pervasive through smartphones and smart speakers. The recognition accuracy of speech recognition technology has developed to the level of human beings, but it still shows limitations on understanding the underlying meaning or intention of words, or understanding long conversation. Accordingly, the users experience various errors when interacting with the conversational agents, which may negatively affect the user experience. In addition, in the case of smart speakers with a voice as the main interface, the lack of feedback on system and transparency was reported as the main issue when the users using. Therefore, there is a strong need for research on how users can better understand the capability of the conversational agents and mitigate negative emotions in error situations. In this study, we applied social strategies, "forewarning" and "apology", to conversational agent and investigated how these strategies affect users' perceptions of the agent in breakdown situations. For the study, we created a series of demo videos of a user interacting with a conversational agent. After watching the demo videos, the participants were asked to evaluate how they liked and trusted the agent through an online survey. A total of 104 respondents were analyzed and found to be contrary to our expectation based on the literature study. The result showed that forewarning gave a negative impression to the user, especially the reliability of the agent. Also, apology in a breakdown situation did not affect the users' perceptions. In the following in-depth interviews, participants explained that they perceived the smart speaker as a machine rather than a human-like object, and for this reason, the social strategies did not work. These results show that the social strategies should be applied according to the perceptions that user has toward agents.

• Proceedings of the Korean Society for Language and Information Conference
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• 2003.06a
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• pp.35-51
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• 2003

• Language and Information
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• v.19 no.2
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• pp.37-53
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• 2015

It has been widely accepted that Korean -i/ka, known as the nominative marker, can give rise to exhaustivity effect. However, it has been rarely discussed and still remains unclear how and why the particle can convey exhaustivity. The purpose of this paper is to shed light on this issue, based on Kim's (2014) analysis of the function of -i/ka. The main claim is that the exhaustivity expressed by -i/ka is a pragmatic effect derived from the interaction of the function of -i/ka, namely, unique specification, and the existence of alternatives to the denotation of an -i/ka-marked phrase in discourse context. Thus, the nature of the exhaustive implicature induced by -i/ka is not conventional but conversational.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.4
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• pp.595-603
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• 2002

In this research, it has been shown that how the computer can recognize and understand spoken natural language and its symbolization using VoiceXML and Grammar Specific Language in developing telephone based conversational automatic speech recognition flight information system. In order for user to hear correct information, ASP Module has been revised and its effectivities has been experimented on the Voice portal airplane information system platform.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.3-42
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• 2002

Fuzzy Neural Network (FNN) is a compulsory element in a voice-controlled machine due to its inherent capability of interpreting imprecise natural language commands. To control such a machine, user's perception of imprecise words is very important because the words' meaning is highly subjective. This paper presents a voice based controller centered on an adaptable FNN to capture the user's perception of imprecise words. Conversational interface of the machine facilitates the learning through interaction. The system consists of a dialog manager (DM), the conversational interface, a Knowledge base, which absorbs user's perception and acts as a replica of human understanding of imprecise words,...

• The Journal of the Acoustical Society of Korea
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• v.23 no.2E
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• pp.51-55
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• 2004

In this paper, we propose a style-specific language model adaptation scheme using n-gram based tf*idf similarity for Korean spontaneous speech recognition. Korean spontaneous speech shows especially different style-specific characteristics such as filled pauses, word omission, and contraction, which are related to function words and depend on preceding or following words. To reflect these style-specific characteristics and overcome insufficient data for training language model, we estimate in-domain dependent n-gram model by relevance weighting of out-of-domain text data according to their n-. gram based tf*idf similarity, in which in-domain language model include disfluency model. Recognition results show that n-gram based tf*idf similarity weighting effectively reflects style difference.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.320-327
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• 2021

We aim to investigate the effects of task type and user gender on the expected gender of chatbots. We conducted an online study of 381 participants who selected the gender (female, male, or neutral) for chabots performing six different tasks. Our results indicate that users expect human- gendered chatbots for all tasks and that the expected gender of a chatbot is significantly different depending on the task type. Users expected chatting, counseling, healthcare and clerical work to be done by female chatbots; professional and customer service work were expected to be done by male chatbots. A tendency for participants to prefer chatbots of the same-gendered as themselves is revealed in several tasks for both male and female users. However, this homophily tendency is stronger for female users. We conclude by suggesting practical guidelines for designing chatbot services that reflect user expectations.

• Asia pacific journal of information systems
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• v.33 no.4
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• pp.957-976
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• 2023

ChatGPT has had a significant impact on society, business, and academia by influencing individuals and organizations through knowledge generation and supporting users in locating conversational inquiries and answers. It can transform how people seek answers by combining human-like conversational skills with AI. By eradicating the cumbersome process of selecting from multiple options, users can conduct preliminary research or create optimized solutions. The purpose of this research is to investigate how consumers use ChatGPT and digital transformation, specifically in terms of knowledge development, searching and recommending, and optimizing accessible possibilities. Using many linked theories, we address the potential implications and insights that can be gained from ChatGPT's early stages and its integration with other applications such as robotics, service automation, and the metaverse. Finally, the application of ChatGPT has practical, theoretical, and phenomenological impacts, in addition to improving users' experiences.