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6 results on '"Andi Djalal Latief"'

3. Experimentation Of Various Preprocessing Pipelines For Sentiment Analysis On Twitter Data About New Indonesia’s Capital City Using SVM And CNN

Author

Siska Pebiana, Nuraisa Novia Hidayati, Dian Isnaeni Nurul Afra, Elvira Nurfadhilah, Harnum Annisa Prafitia, Junanto Prihantoro, Radhiyatul Fajri, M. Teduh Uliniansyah, Agung Santosa, Lyla Ruslana Aini, Yosi Sahreza, Aulia Haritsuddin Karisma Muhammad Subekti, Josua Geovani Pinem, Muhammad Reza Alfin, Agung Septadi, Siti Shaleha, Gembong Satrio Wibowanto, Asril Jarin, null Gunarso, Andi Djalal Latief, and Hammam Riza

Published
2022

5. The Architecture of Speech-to-Speech Translator for Mobile Conversation

Author

Made Gunawan, Gita Citra Puspita, Lyla Ruslana Aini, Agung Santosa, Elvira Nurfadhilah, Andi Djalal Latief, Asril Jarin, Hammam Riza, Muhammad Teduh Uliniansyah, Harnum Annisa Prafitia, and Gunarso

Subjects
Computer science, media_common.quotation_subject, JSON, WebSocket, Human–computer interaction, Conversation, Architecture, Android (operating system), Communications protocol, computer, Mobile device, Utterance, computer.programming_language, media_common
Abstract

With competencies and the results of the engineering of natural language processing technology owned by BPPT since 1987, BPPT develops an English-Bahasa Indonesia speech-to-speech translation system (S2ST). In this paper, we propose an architecture of speech-to-speech translation system for Android-based mobile conversation using separate mobile devices for each language. This architecture applies three leading technologies, namely: WebSocket, REST, and JSON. The system utilizes a two-way communication protocol between two users and a simple voice activation detector that can detect a boundary of user's utterance.

Published
2019

6. A Tool for Making Segmented Speech Corpus for ASR and TTS Modeling

Author

Agung Santosa, Mohammad Teduh Uliniansyah, Andi Djalal Latief, Made Gunawan, Lyla Ruslana Aini, Elvira Nurfadhilah, Asril Jarin, Fara Ayuningtyas, Gunarso, and Harnum Annisa

Subjects
Training set, Computer science, media_common.quotation_subject, Speech recognition, SIGNAL (programming language), Personal computer, Speech corpus, Speech synthesis, Function (engineering), Sentence segmentation, computer.software_genre, computer, media_common
Abstract

To develop models of Natural Language Processing (NLP), such as speech recognition and speech synthesis, require the provision of a speech corpus that has been segmented and useful as training data. On the other hand, making a speech corpus costs a lot because it can include studio rent and payment of recorded speaker fees. Therefore, in this paper, we develop a tool for making speech corpus equipped with a function that guides the speaker to record their speech in sentences. This tool can be run independently on a personal computer so that we can do recording anytime and anywhere. To produce a better speech corpus, the recording results of this tool still require to be checked because they could potentially have a signal clip or low amplitudes.

Published
2019

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