Aplikasi Chatbot Speak English Media Pembelajaran Bahasa Inggris Berbasis Android

Irawan Afrianto; Muhammad Fahmi Irfan; Sufa Atin

doi:10.34010/komputika.v8i2.2273

Aplikasi Chatbot Speak English Media Pembelajaran Bahasa Inggris Berbasis Android

Komputika : Jurnal Sistem Komputer ◽

10.34010/komputika.v8i2.2273 ◽

2019 ◽

Vol 8 (2) ◽

pp. 99-109

Author(s):

Irawan Afrianto ◽

Muhammad Fahmi Irfan ◽

Sufa Atin

Keyword(s):

Text To Speech ◽

Voice Chat

Kemampuan Bahasa Inggris yang baik tentu akan menjadi modal kompetitif, baik dalam bidang pendidikan maupun pekerjaan.Teknologi chatbot adalah merupakan teknologi yang dapat dimanfaatkaan sebagai solusi untuk permasalahan dalam lingkup edukasi. Beberapa penelitian telah membuktikan pemanfaatan chatbot sebagai media belajar, terutama pembelajaran Bahasa Inggris. Disamping itu, sumber daya untuk mendukung pengembangan teknologi chatbot sudah cukup banyak tersedia, seperti API yang mempermudah untuk pembuatan chatbot seperti Dialogflow API yang digunakan pada penelitian ini. Metode yang digunakan pada penelitian ini ialah berlatih dan evaluasi mandiri. Adapun tools pendukung ialah Languagetool API untuk koreksi grammar, Google Speech Recogntion dan Google Text-to-Speech menyediakan antarmuka voice chat. Penelitian ini membahas tentang pemanfaatan teknologi chatbot, tepatnya dengan membangun sebuah aplikasi android sebagai media latihan percakapan Bahasa Inggris. Adapun hasil pengujian menyimpulkan bahwa 63,96% pengguna menyatakan setuju dengan fitur yang dikembangkan pada aplikasi tersebut. Pengguna setuju, sistem yang telah dibangun dapat menjadi solusi untuk permasalahan tersebut, yakni sebagai media latihan percakapan Bahasa Inggris, selain itu pengguna juga terbantu dengan adanya fitur-fitur seperti voice chat, koreksi kesalahan, log harian untuk berlatih dan evaluasi mandiri. Kata Kunci – Chatbot; Pembelajaran Bahasa Inggris; Dialogflow API; Languangetool API; Google Text-To-Speech.

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A Review of 21 iPad Applications for Augmentative and Alternative Communication Purposes

Perspectives on Augmentative and Alternative Communication ◽

10.1044/aac21.2.60 ◽

2012 ◽

Vol 21 (2) ◽

pp. 60-71 ◽

Cited By ~ 24

Author(s):

Ashley Alliano ◽

Kimberly Herriger ◽

Anthony D. Koutsoftas ◽

Theresa E. Bartolotta

Keyword(s):

Augmentative And Alternative Communication ◽

Cost Effective ◽

Alternative Form ◽

Alternative Communication ◽

Text To Speech ◽

Reference Guide ◽

Expressive Communication ◽

Communication Needs ◽

User Friendly ◽

Ipad Applications

Abstract Using the iPad tablet for Augmentative and Alternative Communication (AAC) purposes can facilitate many communicative needs, is cost-effective, and is socially acceptable. Many individuals with communication difficulties can use iPad applications (apps) to augment communication, provide an alternative form of communication, or target receptive and expressive language goals. In this paper, we will review a collection of iPad apps that can be used to address a variety of receptive and expressive communication needs. Based on recommendations from Gosnell, Costello, and Shane (2011), we describe the features of 21 apps that can serve as a reference guide for speech-language pathologists. We systematically identified 21 apps that use symbols only, symbols and text-to-speech, and text-to-speech only. We provide descriptions of the purpose of each app, along with the following feature descriptions: speech settings, representation, display, feedback features, rate enhancement, access, motor competencies, and cost. In this review, we describe these apps and how individuals with complex communication needs can use them for a variety of communication purposes and to target a variety of treatment goals. We present information in a user-friendly table format that clinicians can use as a reference guide.

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Design of English text-to-speech conversion algorithm based on machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189238 ◽

2020 ◽

pp. 1-12

Author(s):

Li Dongmei

Keyword(s):

Machine Learning ◽

Speech Synthesis ◽

Feature Recognition ◽

Learning Algorithm ◽

Morphological Structure ◽

English Text ◽

Text To Speech ◽

Part Of Speech ◽

Modern Computer ◽

Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

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