Impact of Community Radio on Community Development in the United States

Community radio is a community-led broadcasting service that serves as a tool for development for communities around the world. This research study explores the development benefits of community radio in the United States using a case study of Federal Communications Commission (FCC)-licensed FM community radio stations to evaluate the role of community radio in communication and information sharing and promoting sustainable social change and development. To fulfill the objectives of the case study, data was collected from 55 community radio stations in the United States using mixed-questionnaire digital surveys and supplementary virtual interviews. Findings indicated that community radio stations in the United States serve as a communication and information-sharing resource to aid communities in overcoming challenges arising from the digital divide. The findings also demonstrated that community radio provides direct development benefits to communities such as aiding in grassroots initiatives, community building, empowering community members, and promoting diversity and inclusion, among others. If provided proper support to overcome barriers and ensure the sustainability of the field, community radio could prove a valuable tool for communities to achieve sustainable social change and development.Keywords: community radio, community development, digital divide, development benefits, sustainabilit

Las fortalezas castellanas de la Orden de Calatrava en el siglo XII

In this paper, we present a spectro-temporal feature extraction technique using sub-band Hilbert envelopes of relatively long segments of speech signal. Hilbert envelopes of the sub-bands are estimated using Frequency Domain Linear Prediction (FDLP). Spectral features are derived by integrating the sub-band Hilbert envelopes in short-term frames and the temporal features are formed by converting the FDLP envelopes into modulation frequency components. These are then combined at the phoneme posterior level and are used as the input features for a phoneme recognition system. In order to improve the robustness of the proposed features to telephone speech, the sub-band temporal envelopes are gain normalized prior to feature extraction. Phoneme recognition experiments on telephone speech in the HTIMIT database show significant performance improvements for the proposed features when compared to other robust feature techniques (average relative reduction of $11\%$ in phoneme error rate)

Stochastic techniques in deriving perceptual knowledge

The paper argues on examples of selected past works that stochastic and knowledge-based approaches do not contradict each other. Frequency resolution of human hearing is decreasing with increasing frequency. Spectral basis designed for optimal discrimination among different phonemes of speech have similar property. Further, human hearing is most sensitive to modulations with frequency around 4 Hz. Filters on feature trajectories, designed for optimal discrimination among phonemes of speech are bandpass with central frequency around 4 Hz

Hierarchical multi-stream posterior based speech secognition system

Abstract. In this paper, we present initial results towards boosting posterior based speech recognition systems by estimating more informative posteriors using multiple streams of features and taking into account acoustic context (e.g., as available in the whole utterance), as well as possible prior information (such as topological constraints). These posteriors are estimated based on “state gamma posterior ” definition (typically used in standard HMMs training) extended to the case of multi-stream HMMs.This approach provides a new, principled, theoretical framework for hierarchical estimation/use of posteriors, multi-stream feature combination, and integrating appropriate context and prior knowledge in posterior estimates. In the present work, we used the resulting gamma posteriors as features for a standard HMM/GMM layer. On the OGI Digits database and on a reduced vocabulary version (1000 words) of the DARPA Conversational Telephone Speech-to-text (CTS) task, this resulted in significant performance improvement, compared to the stateof-the-art Tandem systems.