SMART-In English: Learn English Using Speech Recognition
Abstract
English is an international language and important to learn. For someone learning English sometimes is a difficulty, especially in pronunciation. Therefore, SMART-In is a prototype of Android App that uses Speech Recognition technology by utilizing services from the Cloud Speech API (Application Programming Interface). SMART-In English can be used as an alternative to English learning, especially in the pronunciation of a word. Using speech recognition can display the score of the pronunciation spoken by the user, recorded, show a level the pronunciation of the word and display the correct pronunciation.
Full Text:
PDFReferences
J. Monte-Ordoño and J. M. Toro, “Different ERP profiles for learning rules over consonants and vowels,” Neuropsychologia, vol. 97, no. February, pp. 104–111, Mar. 2017.
T. Nazzi and L. Polka, “The consonant bias in word learning is not determined by position within the word: Evidence from vowel-initial words,” J. Exp. Child Psychol., vol. 174, pp. 103–111, Oct. 2018.
T. D. Wewalaarachchi and L. Singh, “Vowel, consonant, and tone variation exert asymmetrical effects on spoken word recognition: Evidence from 6-year-old monolingual and bilingual learners of Mandarin,” J. Exp. Child Psychol., vol. 189, p. 104698, Jan. 2020.
Z. Shirzhiyan, E. Shamsi, A. S. Jafarpisheh, and A. H. Jafari, “Objective classification of auditory brainstem responses to consonant-vowel syllables using local discriminant bases,” Speech Commun., vol. 114, no. September, pp. 36–48, Nov. 2019.
J. Feng et al., “Effect of blindness on mismatch responses to Mandarin lexical tones, consonants, and vowels,” Hear. Res., vol. 371, pp. 87–97, Jan. 2019.
V. M. Cáceres et al., “Daily zero-reporting for suspect Ebola using short message service (SMS) in Guinea-Bissau,” Public Health, vol. 138, pp. 69–73, Sep. 2016.
S. J. Iribarren et al., “Scoping review and evaluation of SMS/text messaging platforms for mHealth projects or clinical interventions,” Int. J. Med. Inform., vol. 101, pp. 28–40, May 2017.
S. Zhou and B. D. Solomon, “Do renewable portfolio standards in the United States stunt renewable electricity development beyond mandatory targets?,” Energy Policy, vol. 140, no. February, p. 111377, May 2020.
J. Peng, X. Zhang, Z. Lei, B. Zhang, W. Zhang, and Q. Li, “Comparison of several cloud computing platforms,” in 2009 Second international symposium on information science and engineering, 2009, pp. 23–27.
D. I. S. Saputra, S. W. Handani, and G. A. Diniary, “Pemanfaatan Cloud Speech Api Untuk Pengembangan Media Pembelajaran Bahasa Inggris Menggunakan Teknologi Speech Recognition,” Telematika, vol. 10, no. 2, pp. 92–105, 2017.
S. P. T. Krishnan and J. L. U. Gonzalez, Building Your Next Big Thing with Google Cloud Platform: A Guide for Developers and Enterprise Architects. Springer, 2015.
C. Ratcliff, “What are the top 10 most popular search engines,” Retrieved from Search Engine Watch https//searchenginewatch. com/2016/08/08/what-are-the-top-10-mostpopular-search-engines, 2016.
R. Arulmurugan, K. R. Sabarmathi, and H. Anandakumar, “Classification of sentence level sentiment analysis using cloud machine learning techniques,” Cluster Comput., pp. 1–11, 2017.
N. Mithapelli, S. Chavan, and J. Kumari, “Alumni Tracking Using Google Map API and Social Media based on GPS and LBS,” Int. J. Eng. Sci., vol. 25, no. 11, 2016.
D. Petcu, C. Craciun, and M. Rak, “Towards a cross platform cloud API,” in 1st International Conference on Cloud Computing and Services Science, 2011, pp. 166–169.
D. I. S. Saputra, E. Utami, and A. Sunyoto, “Penerapan Mobile Augmented Reality Berbasis Cloud Computing Pada Harian Umum Radar Banyumas,” in Seminar Nasional Informatika (SEMNASIF) 2015, 2015.
R. H. Rizzardini, C. Gütl, and H. R. Amado-Salvatierra, “Using Cloud-Based Applications for Education, a Technical Interoperability Exploration for Online Document Editors,” in International Workshop on Learning Technology for Education in Cloud, 2015, pp. 219–231.
S. W. Handani, M. Suyanto, and A. F. Sofyan, “Penerapan konsep gamifikasi pada e-learning untuk pembelajaran animasi 3 dimensi,” Telematika, vol. 9, no. 1, 2016.
M. E. Brown and D. L. Hocutt, “Learning to use, useful for learning: a usability study of Google apps for education,” J. Usability Stud., vol. 10, no. 4, pp. 160–181, 2015.
D. Povey et al., “The Kaldi speech recognition toolkit,” in IEEE 2011 workshop on automatic speech recognition and understanding, 2011, no. CONF.
E. (Betsy) A. Baker, “Apps, iP ads, and Literacy: Examining the Feasibility of Speech Recognition in a First-Grade Classroom,” Read. Res. Q., vol. 52, no. 3, pp. 291–310, 2017.
B. R. Reddy and E. Mahender, “Speech to text conversion using android platform,” Int. J. Eng. Res. Appl., vol. 3, no. 1, pp. 253–258, 2013.
W. Diao, X. Liu, Z. Zhou, and K. Zhang, “Your voice assistant is mine: How to abuse speakers to steal information and control your phone,” in Proceedings of the 4th ACM Workshop on Security and Privacy in Smartphones & Mobile Devices, 2014, pp. 63–74.
Y. Xue, Y. Hamada, and M. Akagi, “Voice conversion for emotional speech: Rule-based synthesis with degree of emotion controllable in dimensional space,” Speech Commun., vol. 102, no. June, pp. 54–67, Sep. 2018.
S. H. Mohammadi and A. Kain, “An overview of voice conversion systems,” Speech Commun., vol. 88, pp. 65–82, Apr. 2017.
K. Kobayashi, T. Toda, and S. Nakamura, “Intra-gender statistical singing voice conversion with direct waveform modification using log-spectral differential,” Speech Commun., vol. 99, no. March, pp. 211–220, May 2018.
N. J. Shah and H. A. Patil, “A novel approach to remove outliers for parallel voice conversion,” Comput. Speech Lang., vol. 58, pp. 127–152, Nov. 2019.
F.-L. Xie, F. K. Soong, and H. Li, “Voice conversion with SI-DNN and KL divergence based mapping without parallel training data,” Speech Commun., vol. 106, no. November 2018, pp. 57–67, Jan. 2019.
J. Grudin, “Human-computer interaction,” Annu. Rev. Inf. Sci. Technol., vol. 45, no. 1, pp. 367–430, 2011.
R. Aihara, T. Nakashika, T. Takiguchi, and Y. Ariki, “Voice conversion based on non-negative matrix factorization using phoneme-categorized dictionary,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, pp. 7894–7898.
DOI: https://doi.org/10.18196/jrc.1423
Refbacks
- There are currently no refbacks.
Copyright (c) 2020 Journal of Robotics and Control (JRC)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Journal of Robotics and Control (JRC)
P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Peneliti Teknologi Teknik Indonesia
Published by Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia, Indonesia and the Department of Electrical Engineering
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrcofumy@gmail.com