Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language

We address the problem of efficient acoustic-model refinement (continuous retraining) using semi-supervised and active learning for a low resource Indian language, wherein the low resource constraints are having i) a small labeled corpus from which to train a baseline `seed' acoustic model and ii) a large training corpus without orthographic labeling or from which to perform a data selection for manual labeling at low costs. The proposed semi-supervised learning decodes the unlabeled large training corpus using the seed model and through various protocols, selects the decoded utterances with high reliability using confidence levels (that correlate to the WER of the decoded utterances) and iterative bootstrapping. The proposed active learning protocol uses confidence level based metric to select the decoded utterances from the large unlabeled corpus for further labeling. The semi-supervised learning protocols can offer a WER reduction, from a poorly trained seed model, by as much as 50% of the best WER-reduction realizable from the seed model's WER, if the large corpus were labeled and used for acoustic-model training. The active learning protocols allow that only 60% of the entire training corpus be manually labeled, to reach the same performance as the entire data

A framework for enhancing trust for improved participation in electronic marketplaces accessed from mobile platforms

Information and communication technologies (ICTs) have been widely researched as a mechanism for improving the socio-economic status of disadvantaged, rural communities. In order to do this numerous technology-based initiatives have been introduced into disadvantaged, rural communities to assist them in various aspects of their lives. Unfortunately, even when the proposed benefit of a particular technology is clearly evident to its initiators, the adoption by the target users is often uncertain. This has also been the case with e-commerce in agriculture. Despite the numerous benefits of e-commerce for agricultural producers, the uptake has been low. Trust is a critical pre-condition for the adoption of e-marketplaces. E-marketplaces expose consumers to the risk of non-delivery or misrepresentation of goods ordered and the misuse of personal information by external parties. Additionally, the time investment needed to make a shift to e-marketplaces and the opinions of important reference groups affects the user’s willingness to trust and depend on an e-marketplace. This study was undertaken to assess the extent to which rural users with limited ICT experience would trust and, consequently, adopt an e-marketplace to support agricultural trade. A pragmatic philosophy was adopted in this study, indicating that the researcher’s view of reality is founded on the practical implications and outcomes that are observed. This study used a Canonical Action Research strategy to design, develop and deploy a voice based e-marketplace to assist the trading activities of a Western Cape based aloe community. The community was allowed to utilise thee-marketplace over a period of eight weeks. Thereafter, interviews were held with the participants to investigate their perceptions of the technology. As a result, a model proposing the factors that must be in place for trust to be achieved in a voice based e-marketplace was proposed. The study found that the trustworthiness of a technology results from the technology’s technical capability to satisfy the needs of its users reliably. Usability and security were found to be important determinants of the trustworthiness of a technology. Furthermore, the requirements elicitation process was found to be central to achieving trust as it defines the necessary criteria for developing secure, usable, functional, and reliable technologies that meet the needs of their users