• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.407-412
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• 2019

In this paper, we propose a technique for processing of speech recognition in korean words. Speech recognition is a technology that converts acoustic signals from sensors such as microphones into words or sentences. Most foreign languages have less difficulty in speech recognition. On the other hand, korean consists of vowels and bottom consonants, so it is inappropriate to use the letters obtained from the voice synthesis system. That improving the conventional structure speech recognition can the correct words recognition. In order to solve this problem, a new algorithm was added to the existing speech recognition structure to increase the speech recognition rate. Perform the preprocessing process of the word and then token the results. After combining the result processed in the Levenshtein distance algorithm and the hashing algorithm, the normalized words is output through the consonant comparison algorithm. The final result word is compared with the standardized table and output if it exists, registered in the table dose not exists. The experimental environment was developed by using a smartphone application. The proposed structure shows that the recognition rate is improved by 2% in standard language and 7% in dialect.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.59-66
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• 2022

This research conducted a study classifying gender of speakers by analyzing feature vectors extracted from the voice data. The study provides convenience in automatically recognizing gender of customers without manual classification process when they request any service via voice such as phone call. Furthermore, it is significant that this study can analyze frequently requested services for each gender after gender classification using a learning model and offer customized recommendation services according to the analysis. Based on the voice data of males and females excluding blank spaces, the study extracts feature vectors from each data using MFCC(Mel Frequency Cepstral Coefficient) and utilizes SVM(Support Vector Machine) models to conduct machine learning. As a result of gender classification of voice data using a learning model, the gender recognition rate was 94%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3267-3274
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• 2011

Recently, speech recognition technology has dramatically developed, with the increase in the user environment of various mobile devices and influence of a variety of speech recognition software. However, for speech recognition for multi-language, lack of understanding of multi-language lexical model and limited capacity of systems interfere with the improvement of the recognition rate. It is not easy to embody speech expressed with multi-language into a single acoustic model and systems using several acoustic models lower speech recognition rate. In this regard, it is necessary to research and develop a multi-language speech recognition system in order to embody speech comprised of various languages into a single acoustic model. This paper studied a system that can recognize Korean and English as International Phonetic Language (IPA), based on the research for using a multi-language acoustic model in mobile devices. Focusing on finding an IPA model which satisfies both Korean and English phonemes, we get 94.8% of the voice recognition rate in Korean and 95.36% in English.

• The Journal of Society for e-Business Studies
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• v.21 no.4
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• pp.29-40
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• 2016

Voice User Interface (VUI) is the most familiar and comfortable interface to human. Recently, with the development of cloud and AI technologies VUI has been applied to various products. The aim of this study was to identify the problems of the current VUI and to find the direction of future study by investigating the utilization status and user perception of the VUI of smartphones. A survey was conducted with 163 college students using Google Forms. The results showed that the level of recognition of VUI is high but the rate of usage is very low, and many users feel uncomfortable about the voice recognition rate, reaction speed and operation method. Most of the survey participants tried VUI out of curiosity, but only a small portion of them found it useful to continue to use it. Many participants disliked talking to machines and also did not want others to listen. The study results will guide future research efforts for improving the utilization of VUI.

• Proceedings of the KOSOMBE Conference
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• v.1993 no.05
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• pp.90-93
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• 1993

In this study, Korean speech was recognized by using spatio-temporal recognition neural network. The subjects of speech are numeric speech from zero to nine and basic command which might be used for motorized wheelchair developed it own Lab. Rabiner and Sambur's method of speech detection was used in determining end-point of speech, speech parameter was extracted by using LPC 16 order. The recognition rate was over 90%.

• Smart Media Journal
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• v.11 no.11
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• pp.17-24
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• 2022

With the spread of smart speakers based on voice recognition technology and deep learning technology, not only non-disabled people, but also the blind or physically handicapped can easily control home appliances such as lights and TVs through voice by linking home network services. This has greatly improved the quality of life. However, in the case of speech-impaired people, it is impossible to use the useful services of the smart speaker because they have inaccurate pronunciation due to articulation or speech disorders. In this paper, we propose a personalized voice classification technique for the speech-impaired to use for some of the functions provided by the smart speaker. The goal of this paper is to increase the recognition rate and accuracy of sentences spoken by speech-impaired people even with a small amount of data and a short learning time so that the service provided by the smart speaker can be actually used. In this paper, data augmentation and one cycle learning rate optimization technique were applied while fine-tuning ResNet18 model. Through an experiment, after recording 10 times for each 30 smart speaker commands, and learning within 3 minutes, the speech classification recognition rate was about 95.2%.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.1-9
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• 2022

Recently, the functions of smart speakers have diversified, and the penetration rate of smart speakers is increasing. As it becomes more widespread, various techniques have been proposed to cause anomalous behavior against smart speakers. Dolphin Attack, which causes anomalous behavior against the Voice Controllable System (VCS) during various attacks, is a representative method. With this method, a third party controls VCS using ultrasonic band (f>20kHz) without the user's recognition. However, since the method uses the ultrasonic band, it is necessary to install an ultrasonic speaker or an ultrasonic dedicated device which is capable of outputting an ultrasonic signal. In this paper, a smart speaker is controlled by generating an audio signal modulated at a frequency (18 to 20) which is difficult for a person to hear although it is in the human audible frequency band without installing an additional device, that is, an ultrasonic device. As a result with the method proposed in this paper, while humans could not recognize voice commands even in the audible band, it was possible to control the smart speaker with a probability of 82 to 96%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.555-567
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• 2019

Speech recognition rate is lowered even in a noisy environment, and it is difficult for a person with a speech disability or an inconvenient language to use it in a social life. In addition to improving the inconvenience of using the language, 280 words were selected using the word selection method which was improved when the word was selected considering the pronunciation characteristics of the language impaired. The MEMS development device used in the experiment was made considering material, lead wire type, length and direction. We improved the speech recognition rate by using the developed word selection method and the MEMS device developed to improve the speech recognition rate due to incorrect pronunciation and severe noise. The new method of selecting words and the mems device were improved and the results were included.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.111-116
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• 2016

According to the Ministry of Science, ICT and Future Planning's survey of information gap, smartphone retention rate of disabled people stayed in one-third of non-disabled people, the situation is significantly less access to information for people with disabilities than non-disabled people. In this paper, we develop an application, CallHelper, that helps to be more convenient to use mobile voice calls to the auditory disabled people. CallHelper runs automatically when a call comes in, translates caller's voice to text output on the mobile screen, and displays the emotion reasoning from the caller's voice to visualize emoticons. It also saves voice, translated text, and emotion data that can be played back.

• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.302-308
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• 2013

In this research, we have explored personal authentication system through multimodal biometrics for mobile computing environment. We have selected face and speaker recognition for the implementation of multimodal biometrics system. For face recognition part, we detect the face with Modified Census Transform (MCT). Detected face is pre-processed through eye detection module based on k-means algorithm. Then we recognize the face with Principal Component Analysis (PCA) algorithm. For speaker recognition part, we extract features using the end-point of voice and the Mel Frequency Cepstral Coefficient (MFCC). Then we verify the speaker through Dynamic Time Warping (DTW) algorithm. Our proposed multimodal biometrics system shows improved verification rate through combining two different biometrics described above. We implement our proposed system based on Android environment using Galaxy S hoppin. Proposed system presents reduced false acceptance ratio (FAR) of 1.8% which shows improvement from single biometrics system using the face and the voice (presents 4.6% and 6.7% respectively).