• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.12
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• pp.819-829
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• 2017

In this paper, we describe the current state of speech recognition technology and identify the basic speech recognition technology and algorithms first, and then explain the code flow of API necessary for speech recognition technology. We use the application programming interface (API) of Google, Naver, and Daum KaKao, which have the most famous search engine among the speech recognition APIs, to create a voice recognition app in the Android studio tool. Then, we perform a speech recognition experiment on people's standard words and dialects according to gender, age, and region, and then organize the recognition rates into a table. Experiments were conducted on the Gyeongsang-do, Chungcheong-do, and Jeolla-do provinces where the degree of tongues was severe. And Comparative experiments were also conducted on standardized dialects. Based on the resultant sentences, the accuracy of the sentence is checked based on spacing of words, final consonant, postposition, and words and the number of each error is represented by a number. As a result, we aim to introduce the advantages of each API according to the speech recognition rate, and to establish a basic framework for the most efficient use.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.89-97
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• 2021

Voice interface can not only make daily life more convenient through artificial intelligence speakers but also improve the working environment of the factory. This paper presents a voice-assisted work management system that supports both speech and speaker recognition. This system is able to provide machine control and authorized worker authentication by voice at the same time. We applied two speech recognition methods, Google's Speech application programming interface (API) service, and DeepSpeech speech-to-text engine. For worker identification, the SincNet architecture for speaker recognition was adopted. We implemented a prototype of the work management system that provides voice control with 26 commands and identifies 100 workers by voice. Worker identification using our model was almost perfect, and the command recognition accuracy was 97.0% in Google API after post- processing and 92.0% in our DeepSpeech model.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.79-85
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• 2021

Speech recognition technology refers to a technology in which a computer interprets the speech language spoken by a person and converts the contents into text data. This technology has recently been combined with artificial intelligence and has been used in various fields such as smartphones, set-top boxes, and smart TVs. Examples include Google Assistant, Google Home, Samsung's Bixby, Apple's Siri and SK's NUGU. Google and Daum Kakao offer free open APIs for speech recognition technologies. This paper selects three APIs that are free to use by ordinary users, and compares each recognition rate according to the three types. First, the recognition rate of "numbers" and secondly, the recognition rate of "Ga Na Da Hangul" are conducted, and finally, the experiment is conducted with the complete sentence that the author uses the most. All experiments use real voice as input through a computer microphone. Through the three experiments and results, we hope that the general public will be able to identify differences in recognition rates according to the applications currently available, helping to select APIs suitable for specific application purposes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.495-496
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• 2018

As more and more companies introduce artificial intelligent(AI) speakers, the price of the speakers has become a burden to someone. Based on some knowledge and dexterity, it is not difficult to make an AI speaker that acquires sensor information and environmental information of the house in accordance with your own taste. In this paper, we implement an AI speaker using Raspberry Pie, Google Cloud Speech (GCS) and Naver's Clova Speech Synthesis (CSS) API.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.151-157
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• 2019

In this paper, we present a prototype system for controlling IoT home appliances via voice-based commands. A voice command has been widely deployed as one of unobtrusive user interfaces for applications in a variety of IoT domains. However, interoperability between diverse IoT systems is limited by several dominant companies providing voice assistants like Amazon Alexa or Google Now due to their proprietary systems. A global IoT standard, oneM2M has been proposed to mitigate the lack of interoperability between IoT systems. In this paper, we deployed oneM2M-based platforms for a voice record device like a wrist band and LED control device like a home appliance. We developed all the components for recording voices and controlling IoT devices, and demonstrate the feasibility of our proposed method based on oneM2M platforms and Google STT (Speech-to-Text) API for controlling home appliances by showing a user scenario for turning the LED device on and off via voice commands.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.55-61
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• 2019

In order to realize foreign language education, various existing educational media have been provided, but there are disadvantages in that the cost of the parish and the media program is high and the real-time responsiveness is poor. In this paper, we propose an artificial intelligence English conversation system based on VR and speech recognition. We used Google CardBoard VR and Google Speech API to build the system and developed artificial intelligence algorithms for providing virtual reality environment and talking. In the proposed speech recognition server system, the sentences spoken by the user can be divided into word units and compared with the data words stored in the database to provide the highest probability. Users can communicate with and respond to people in virtual reality. The function provided by the conversation is independent of the contextual conversations and themes, and the conversations with the AI assistant are implemented in real time so that the user system can be checked in real time. It is expected to contribute to the expansion of virtual education contents service related to the Fourth Industrial Revolution through the system combining the virtual reality and the voice recognition function proposed in this paper.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.697-699
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• 2019

IT 기술의 발달에 따라 전자기기의 이용량은 증가하였지만, 시각장애인들이나 지체 장애인들이 이용하는 데에 어려움이 있다. 따라서 본 논문에서는 Google Cloud API를 활용하여 음성으로 프로그램을 제어할 수 있는 음성 인터페이스를 제안한다. Google Cloud에서 제공하는 STT(Speech To Text)와 TTS(Text To Speech) API를 이용하여 사용자의 음성을 인식하면 텍스트로 변환된 음성이 시스템을 통해 응용 프로그램을 제어할 수 있도록 설계한다. 이 시스템은 장애인들이 전자기기를 사용하는데 많은 편리함을 줄 것으로 예상하며 나아가 장애인들뿐 아니라 비장애인들도 활용 가능할 것으로 기대한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.129-130
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• 2020

본 논문에서는 청각장애인의 의사소통을 위한 안드로이드 애플리케이션 시스템 구현 결과를 보인다. 구글 클라우드 플랫폼(Google Cloud Platform)의 STT(Speech to Text) API를 이용하여 음성 인식을 통해 대화의 내용을 텍스트의 형태로 출력한다. 그리고 TTS(Text to Speech)를 이용한 음성 합성을 통해 텍스트를 음성으로 출력한다. 또한, 포그라운드 서비스(Service)에서 가속도계 센서(Accelerometer Sensor)를 이용하여 스마트폰을 2~3회 흔들었을 때 해당 애플리케이션을 실행할 수 있도록 하여 애플리케이션의 활용성을 높인 시스템을 개발하였다.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.209-214
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• 2020

VoIP technology has been widely used for exchanging voice or image data through IP networks. VoIP technology, often called Internet Telephony, sends and receives voice data over the RTP protocol during the session. However, there is an exposition risk in the voice data in VoIP using the RTP protocol, where the RTP protocol does not have a specification for encryption of the original data. We implement programs that can extract meaningful information from the user's dialogue. The meaningful information means the information that the program user wants to obtain. In order to do that, our implementation has two parts. One is the client part, which inputs the keyword of the information that the user wants to obtain, and the other is the server part, which sniffs and performs the speech recognition process. We use the Google Speech API from Google Cloud, which uses machine learning in the speech recognition process. Finally, we discuss the usability and the limitations of the implementation with the example.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.105-110
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• 2021

In this paper, we design and implement an Android mobile application that helps hearing impaired people communicate based on STT(Speech-to-Text) and TTS(Text-to-Speech) APIs and accelerometer sensor of a smartphone. This application provides the ability to record what the hearing-Impairment person's interlocutor is saying with a microphone, convert it to text using the STT API, and display it to the hearing-Impairment person. In addition. In addition, when a hearing-impaired person inputs a text using the TTS API, it is converted into voice and told to the interlocutor. When a hearing-impaired person shakes their smartphone, an accelerometer based background service function is provided to run the application. The application implemented in this paper provides a function that allows hearing impaired people to communicate easily with other people when communicating with others without using sign language as a video call.