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Several members of the genus Sterculia have a great potential as a candidate for the identification of new drug lead molecules, but lack of their genomic information can be a hindrance for the verification of the genetic background for future use. To obtain genomic resources, RNA-seq transcriptome analysis was conducted using leaves of Sterculia lanceolata, a member of the genus Sterculia, resulting...
To broaden and delve into the genomic information of Clausena excavata, an important medicinal plant in many Asian countries, RNA sequencing (RNA-seq) analysis was performed and a total of 16,638 non-redundant unigenes (≥ 300 bp) with an average length of 755 bp were generated by de novo assembly from 17,580,456 trimmed clear reads. The functional categorization of the identified unigenes by a gene...
Mismatched crowdsourcing is a technique to derive speech transcriptions using crowd-workers unfamiliar with the language being spoken. This technique is especially useful for under-resourced languages since it is hard to hire native transcribers. In this paper, we demonstrate that using mismatched transcription for adaptation improves performance of speech recognition under limited matched training...
This paper presents a novel method for acoustic modeling of an under-resourced language by “mapping” from acoustic models of well-resourced languages. The proposed method can be considered as a “many-to-one mapping” method where one speech unit in the target language is built as a linear combination of the source speech unit models and hence we can explicitly observe the relationship of the source...
Dynamic Time Warping (DTW) is widely used in language independent query-by-example (QbE) spoken term detection (STD) tasks due to its high performance. However, there are two limitations of DTW based template matching, 1) it is not straightforward to perform approximate match of audio queries; 2) DTW is sensitive to the mismatch of signal conditions between the query and the speech search data. To...
We present exemplar-inspired low-resource spoken keyword search strategies for acoustic modeling, keyword verification, and system combination. This state-of-the-art system was developed by the SINGA team in the context of the 2015 NIST Open Keyword Search Evaluation (OpenKWS15) using conversational Swahili provided by the IARPA Babel program. In this work, we elaborate on the following: (1) exploiting...
Kernel density model works well for limited training data in acoustic modeling. In this paper, we improve the kernel density-based acoustic model for low resource language speech recognition. In our previous study, we demonstrated the effectiveness of the kernel density-based acoustic model on discriminative features such as cross-lingual bottleneck features. In this paper, we propose to learn a Mahalanobis-based...
In this paper we report our approaches to accomplishing the very limited resource keyword search (KWS) task in the NIST Open Keyword Search 2015 (OpenKWS15) Evaluation. We devised the methods, first, to attain better acoustic modeling, multilingual and semi-supervised acoustic model training as well as the examplar-based acoustic model training; second, to address the overwhelming out-of-vocabulary...
In this paper, we investigate the use of the proposed non-parametric exemplar-based acoustic modeling for the NIST Open Keyword Search 2015 Evaluation. Specifically, kernel-density model is used to replace GMM in HMM/GMM (Hidden Markov Model / Gaussian Mixture Model) or DNN in HMM/DNN (Hidden Markov Model / Deep Neural Network) acoustic model to predict the emission probability of HMM states. To get...
To improve speech recognition performance, a combination between TANDEM and bottleneck Deep Neural Networks (DNN) is investigated. In particular, exploiting a feature combination performed by means of a multi-stream hierarchical processing, we show a performance improvement by combining the same input features processed by different neural networks. The experiments are based on the spontaneous telephone...
This paper presents a novel method for acoustic modeling with limited training data. The idea is to leverage on a well-trained acoustic model of a source language. In this paper, a conventional HMM/GMM triphone acoustic model of the source language is used to derive likelihood scores for each feature vector of the target language. These scores are then mapped to triphones of the target language using...
This paper presents a novel method for acoustic modeling of a new language with a limited amount of training data. In this approach, we use well-trained acoustic models of a foreign language to generate acoustic scores for each feature vector of the target language. These scores are then used as the input for mapping to context dependent triphones of the target language using a limited amount of training...
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