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This paper aims to design and validate a phonetically balanced speech corpus for Arabic language. Designing and developing a rich and phonetically balanced corpus in optimal context is one of the key issues in building high quality of text-to-speech synthesis systems. The rich characteristic is in the sense that it must contain all the possible phonemes on the right and left context, whereas the balanced characteristic is in the sense that it respects the phonetic distribution in the language. We propose a new methodology for designing and implementing such corpus for speech synthesis purposes. The paper explains the whole creation process of this corpus, beginning with the design stage, corpus creation, recording phases, and finally the segmentation of the speech corpus. The speech corpus contains 202 sentences with 6174 phonemes. In order to validate the speech corpus, an Arabic speech synthesis system using Hidden Markov Models has been developed. [ABSTRACT FROM AUTHOR]

Compared with English, Arabic is a poorly-resourced language within the field of corpus linguistics. A lack of sufficient data and research has negatively affected Arabic corpus-based researchers and natural language processing practitioners. Although a number of Arabic corpora have been developed in recent years, the overall situation has improved little. The aim of this paper is twofold. First, it reviews 14 Arabic corpora categorized by their designated purpose, target language, mode of text, size, text date, location, text type/medium, text domain, representativeness, and balance. The review also describes the availability of the reviewed corpora, the presence of tokenization, lemmatization and tagging, and whether there are any tools available to search and explore them. Second, it introduces the King Abdulaziz City for Science and Technology (KACST) Arabic corpus, which was designed and created to overcome the limitations of existing Arabic corpora. The KACST Arabic corpus is a large and diverse Arabic corpus with clearly defined design criteria. It is carefully sampled, and its contents are classified based on time, region, medium, domain, and topic, and it can be searched and explored using these classifications. The KACST Arabic corpus comprises more than 700 million words from the pre-Islamic era to the present day (a period covering more than 1,500 years), collected from 10 diverse mediums. Each text has been further classified more specifically into domains and topics. The KACST Arabic corpus is freely available to explore on the Internet () using a variety of tools. [ABSTRACT FROM AUTHOR]

Content-based analysis to find where violence appears in multimedia content has several applications, from parental control and children protection to surveillance. This paper presents the design and annotation of the Violent Scene Detection dataset, a corpus targeting the detection of physical violence in Hollywood movies. We discuss definitions of physical violence and provide a simple and objective definition which was used to annotate a set of 18 movies, thus resulting in the largest freely-available dataset for such a task. We discuss borderline cases and compare with annotations based on a subjective definition which requires multiple annotators. We provide a detailed analysis of the corpus, in particular regarding the relationship between violence and a set of key audio and visual concepts which were also annotated. The VSD dataset results from two years of benchmarking in the framework of the MediaEval initiative. We provide results from the 2011 and 2012 benchmarks as a validation of the dataset and as a state-of-the-art baseline. The VSD dataset is freely available at the address: . [ABSTRACT FROM AUTHOR]

Automatic speech recognition (ASR) technology has matured over the past few decades and has made significant impacts in a variety of fields, from assistive technologies to commercial products. However, ASR system development is a resource intensive activity and requires language resources in the form of text annotated audio recordings and pronunciation dictionaries. Unfortunately, many languages found in the developing world fall into the resource-scarce category and due to this resource scarcity the deployment of ASR systems in the developing world is severely inhibited. One approach to assist with resource-scarce ASR system development, is to select 'useful' training samples which could reduce the resources needed to collect new corpora. In this work, we propose a new data selection framework which can be used to design a speech recognition corpus. We show for limited data sets, independent of language and bandwidth, the most effective strategy for data selection is frequency-matched selection and that the widely-used maximum entropy methods generally produced the least promising results. In our model, the frequency-matched selection method corresponds to a logarithmic relationship between accuracy and corpus size; we also investigated other model relationships, and found that a hyperbolic relationship (as suggested from simple asymptotic arguments in learning theory) may lead to somewhat better performance under certain conditions. [ABSTRACT FROM AUTHOR]

Automatic Speaker Recognition (ASR) refers to the task of identifying a person based on his or her voice with the help of machines. ASR finds its potential applications in telephone based financial transactions, purchase of credit card and in forensic science and social anthropology for the study of different cultures and languages. Results of ASR are highly dependent on database, i.e., the results obtained in ASR are meaningless if recording conditions are not known. In this paper, a methodology and a typical experimental setup used for development of corpora for various tasks in the text-independent speaker identification in different Indian languages, viz., Marathi, Hindi, Urdu and Oriya have been described. Finally, an ASR system is presented to evaluate the corpora. [ABSTRACT FROM AUTHOR]