In recent years, globalization has highlighted the importance of having machines that can truly provide customized communication for different languages. Majority of the research in the field focus on developing technologies for widely used languages such as English. In this study, we apply HMM-based speech synthesis (HTS) technology for Indonesian language. The proposed hybrid HTS-based framework, PFHTS-IDSS, uses phoneme and full-context lab to synthesize Indonesian with higher accuracy. First, we identify a list of Indonesian phonemes according to the initial-final structure of Chinese language. Based on this, we add zero-initials that match the Indonesian acoustic performance and HTS, which can make the synthesized speech natural and smooth. Second, we consider Indonesian phonemes as synthetic units to synthesize speech through the triphone and full-context lab. In addition, we design context properties of the full-context lab and the corresponding question set to train the acoustic model, which can eliminate machine sounds. Experimental results suggest that the accuracy of phoneme segmentation (PSA) and the naturalness of speech synthesis (SSN) are significantly improved via PFHTS-IDSS. Especially, the PSA of selecting phonemes as synthetic units reaches 88.3% and the corresponding SSN based on full-context lab is 4.1. The results demonstrated by PFHTS-IDSS presented in this paper may be used in multilingual free interactive system to promote better communication in terms of voice navigation, intelligent speaker and question-answering system.
In view of it is not only time-consuming but also costly to determine the locations of the protein subcellular by biological experiment, developing fast and effective calculation methods for subcellular localization prediction has become one of the important research contents in the field of bioinformatics. Since the SVM-RFE algorithm can select the optimal feature subset according to the correlation between each feature and protein subcellular localization, and it can reduce the computational complexity while keeping the result steady and having a high degree of generalization in the progress of using the RFE part of SVM-RFE method, therefore, this algorithm is applied to predict protein subcellular localization. First of all, we extract amino acid components, dipeptide component and entropy density from Position Specific Scoring Matrix to construct the feature expression model of protein sequence. Then we use the recursion feature elimination to conduct feature selection. Finally, the support vector machine classifier was used to conduct Jackknife verification on two data sets of Gram Positive and Negative. The experimental results show that the application of SVM-RFE algorithm to protein subcellular localization has a good predictive accuracy.
As the style of language expression become liberalized and diversified increasingly, the advantages of using deep learning models in the field of speech synthesis are gradually highlighted. However, most of the current studies are based on those popular languages such as Chinese and English, and there is a little research on minority languages. To this end, the speech parameter generation and emotional speech synthesis for Malay are studied in this paper. We first used recurrent neural network (RNN) to capture the features of dependencies in Malay, and the parametric model was established through multivariate feature matrices for Malay texts using long short-term (LSTM). Most of the inputs are audio and corresponding triphone models which are obtained after a series of segmentation in the process of speech synthesis. There are few emotional components remained in the segmented results. This paper used LSTM RNN to directly model on the waveform of Malay speech and to keep emotions as much as possible. Experimental results on real-life data showed that the synthesis of Malay speech parameter based on LSTM RNN model achieved satisfying performance which are 1.16 and 0.25 improvements in two indexes respectively and applying that model in Malay emotional speech synthesis reached the precision of 85.46%.
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