PERSIDANGAN LINGUISTIK ASEAN KETIGA-PLA III

Indonesian text to speech has already available for 5 years. Current Indonesia TTS still used Manual Prosody Modeling that drive by parameters that extract manually from speech sample and inserted manually to the prosody model. Currently, we are trying to replace the current model by automatic prosody model using artificial neural network (ANN). The ANN in the model will learn from the speech sample and determine the prosody curve automatically. The interesting thing from the linguistinc view is the list of parameters from the speech signal that need to define as an input for ANN, so it can learn properly. In this prelemenary research, the ANN can mimic several prosody event come from sample sentences. Penerjemahan bahasa Indonesia dari teks ke pengucapan sudah tersedia selama 5 tahun. TTS bahasa Indonesia saat ini masih menggunakan pemodelan prosodi secara manual yang berasal dari pengukuran yang di sarikan secara manual melalui sampel ucapan dan dimasukan secara manual ke dalam model prosodi. Baru-baru ini kami mencoba untuk mengganti model yang ada dengan model prosodi otomatis menggunakan Jaringan Pemrosesan Saraf Tiruan. Jaringan pemrosesan saraf tiruan dalam model akan mempelajari sampel pengucapan dan menentukan kurva prosodi secara otomatis. Hal yang menarik dari sudut pandang linguistik adalah bahwa daftar parameter dari sinyal ucapan yang harus di artikan sebagai masukan untuk Jaringan pemrosesan saraf tiruan sehingga alat tersebut bisa mempelajarinya secara tepat. Dalam penelitian awal ini, Jaringan pemrosesan saraf tiruan dapat menirukan beberapa prosodi bahkan yang berasal dari contoh kalimat

Generic Solution Architecture Design of Regulatory Technology (RegTech)

Regulatory Technology, or RegTech, uses new technology that assists the financial industry, such as FinTech and banks, in meeting regulatory compliance. RegTech automates various regulatory compliance activities that were previously manual, such as regulatory interpretation and regulatory reporting, amidst the challenges of the increasing volume of regulations and operational data. Some cutting-edge technologies discovered at RegTech include big data analytics, artificial intelligence, machine learning, robotic process automation, and cloud computing. Although very dominant in the financial industry, RegTech solutions have the potential to be applied in other regulated industries besides finance. Several studies have explored the potential for applying RegTech in industries other than finance, such as charitable organizations, real estate marketplace, pharmaceuticals, and healthcare. Therefore, this study aims to design a generic RegTech solution architecture so that it can be adopted and applied in various regulated industries achieve regulatory compliance more efficiently. Based on the evaluation results, the proposed architecture can be applied in an industrial environment other than financial to be considered generic. Furthermore, an evaluation of the comparison of regulatory compliance business processes without and by implementing RegTech can produce a time efficiency of 95.16%. These results show that RegTech solutions can achieve regulatory compliance more efficiently

A Novel Part-of-Speech Set Developing Method for Statistical Machine Translation

Part of speech (PoS) is one of the features that can be used to improve the quality of statistical-based machine translation. Typically, the language PoS determined based grammar of the language or adopt from other languages PoS. This work aims to formulate a model to developing PoS as linguistic factors to improve the quality of machine translation automatically. The research method using word similarity approach, where we perform clustering of the words contained in a corpus. Further classes will be defined as PoS set obtained for a given language.We evaluated the results of the PoS that defined computational results using machine translation system MOSES as the system by comparing the results of the SMT are using PoS sets generated manually, while the assessment of the system using BLEU method. Language that will be used for evaluation is English as the source language and Indonesian as the target language

Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients

To improve the performance of phoneme based Automatic Speech Recognition (ASR) in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT) coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited) and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA). These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4) from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments

Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients

To improve the performance of phoneme based Automatic Speech Recognition (ASR) in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT) coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited) and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA). These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4) from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments

Formulating a Conceptual Model of Digital Service Transformation Based on a Systematic Literature Review

Digital service transformation study is a part of research in the field of digital transformation, which is devoted to exploring the transformations that occur in digital service products, which have been intensely explored in recent years to address digital disruption. Several concepts and definitions of digital service transformation have emerged as a result of an approach from the point of view of digital transformation and digital services concepts. This paper is organized to provide a foundational understanding of digital service transformation terminology. This paper uses the systematic literature review method to compile 52 qualified articles from previous studies. We conduct an analysis and synthesis of articles to answer research questions. The results of this study are a descriptive summary of research in the digital service transformation field, determining digital service transformation terminology and components, and also a proposed digital service transformation model to explain the position of transformation in digital service products in the overall transformation process. We construct this model using the findings of previously determined components synthesis