Risk Governance of The People's Palm Plantation Partnership Program in Bangka Regency

Nowadays, partnership has become one of new forms of governance. It is noteffective for the government to handle all public services on its own because thesociety’s current situation turns to be more complex and complicated. However,there are risks inherent in every partnership. Every party involved in apartnership has each own potential risk. In the context of palm plantationmanagement partnerships, uncertainty in managing risks will lead to a numberof conflicts. Thus, it is important to pay attention to risk governance in apartnership. This study aims at describing how the risk governance is done inKKSR (Kebun Kelapa Sawit Rakyat/People’s Palm Plantation) program inBangka Regency, Indonesia. There are 3 main focuses of this study, namely theprocess, the strategy and the result of risk governance in KKSR program.Theresults show that the there are different interests among the parties involved inthe partnership which are potential to be the cause of conflicts. Furthermore, thepartnership achievement indicates that the strategy of risk governance appliedin KKSR Program initiated by the Government of Bangka Regency has givenpositive impact on each party involved in the partnership. The result of thisstudy also implies the importance of government role in the risk governanceprocess of the partnership program for public services. The government isresponsible for protecting the stakeholders’ interests, especially public interests,and also responsible for assuring that the goals of the partnership are achieved

Robust Phoneme Recognition with Little Data

A common belief in the community is that deep learning requires large datasets to be effective. We show that with careful parameter selection, deep feature extraction can be applied even to small datasets.We also explore exactly how much data is necessary to guarantee learning by convergence analysis and calculating the shattering coefficient for the algorithms used. Another problem is that state-of-the-art results are rarely reproducible because they use proprietary datasets, pretrained networks and/or weight initializations from other larger networks. We present a two-fold novelty for this situation where a carefully designed CNN architecture, together with a knowledge-driven classifier achieves nearly state-of-the-art phoneme recognition results with absolutely no pretraining or external weight initialization. We also beat the best replication study of the state of the art with a 28% FER. More importantly, we are able to achieve transparent, reproducible frame-level accuracy and, additionally, perform a convergence analysis to show the generalization capacity of the model providing statistical evidence that our results are not obtained by chance. Furthermore, we show how algorithms with strong learning guarantees can not only benefit from raw data extraction but contribute with more robust results

A single speaker is almost all you need for automatic speech recognition

We explore the use of speech synthesis and voice conversion applied to augment datasets for automatic speech recognition (ASR) systems, in scenarios with only one speaker available for the target language. Through extensive experiments, we show that our approach achieves results compared to the state-of-the-art (SOTA) and requires only one speaker in the target language during speech synthesis/voice conversion model training. Finally, we show that it is possible to obtain promising results in the training of an ASR model with our data augmentation method and only a single real speaker in different target languages.Comment: Submitted to INTERSPEECH 202

End-To-End Speech Synthesis Applied to Brazilian Portuguese

Voice synthesis systems are popular in different applications, such as personal assistants, GPS applications, screen readers and accessibility tools.Voice provides a natural way for human-computer interaction. However, not all languages are on the same level when in terms of resources and systems for voice synthesis. This work consists of the creation of publicly available resources for Brazilian Portuguese in the form of a dataset and deep learning models for end-to-end voice synthesis. The dataset has 10.5 hours from a single speaker. We investigated three different architectures to perform end-to-end speech synthesis: Tacotron 1, DCTTS and Mozilla TTS. We also analysed the performance of models according to different vocoders (RTISI-LA, WaveRNN and Universal WaveRNN), phonetic transcriptions usage, transfer learning (from English) and denoising. In the proposed scenario, a model based on Mozilla TTS and RTISI-LA vocoder presented the best performance, achieving a 4.03 MOS value. We also verified that transfer learning, phonetic transcriptions and denoising are useful to train the models over the presented dataset. The obtained results are comparable to related works covering English, even while using a smaller datasetComment: This paper is under consideration at COLING'2020 - The 28th International Conference on Computational Linguistic