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The performance of language recognition system is mainly determined by feature extraction and model training. In this paper, a robust equalization feature for language recognition is proposed, which utilizes the common features of the speech spectrum mean vector to calculate a global mean vector. The spectrum mean vector of each segment is equalized on the global mean vector, and the equalization features are obtained. In model training, Gated Recurrent Unit (GRU) of Recurrent Neural Network (RNN) is applied to language recognition, in which GRU can reduce the amount of computation and shorten the training time. The experimental results show that the proposed method outperforms the baseline system on the NIST LRE 2007 corpus. [ABSTRACT FROM AUTHOR]

Abstract We combine Total Variability algorithm with Deep Learning theory to complete the language recognition task. The Total Variability algorithm can compensate for the influence of differences in channels and speakers among various languages, while deep learning methods have a stronger ability of nonlinear modeling compared with traditional statistical models. In this paper, I-vector feature is extracted using Total Variability algorithm, and model training is established using fully connected neural network. Meanwhile, the dropout strategy is also used to suppress overfitting. The experimental results show that the new system outperforms the baseline system on the NIST LRE 2007 corpus. [ABSTRACT FROM AUTHOR]

Across Australia, language contact has shaped the languages that many Aboriginal and Torres Strait Islander people speak today. While in some communities Traditional Languages have been spoken continuously from pre-invasion through to the present day, the language use of many First Nations Australians has shifted to "New Languages", which are contact languages such as creoles and mixed languages, or otherwise to a variety of English. Despite the extent of language contact and its effects on the day-to-day language experiences of many Indigenous Australians, there is little discourse in the public domain or in educational settings about language contact or contact languages. In public data sets they are not reliably differentiated from Traditional Languages on the one hand, nor from English(es) on the other (Angelo et al., 2019). This has profound implications for Indigenous Australians who speak these New Languages, some of which constitute the largest languages that are spoken almost exclusively by First Nations peoples today. There is no systematic policy effort underway to work with communities to describe, recognise and name these New Languages, and consequently little tailoring of services to speakers on account of these languages. This paper presents a grounded translational research methodology, Community Vernacular Language Posters (Department of Education [Qld], 2018), that has been developed for working with Aboriginal and Torres Strait Islander community members to raise awareness about their local contact language and about the ramifications for education and other service provision. The outcome of this linguistically-informed process is an accessible poster depicting local everyday ways of talking. Underpinning this document is the shared body of knowledge amongst communityparticipants that has been generated by the many consultations and inclusive discussions that have taken place due to the collaborative language research process. Often, a Community Vernacular Language Poster project has resulted in a community initiative to name their local contact language. The posters have proven a popular initiative, as they make tangible a community's speech variety, foster a sense of pride and ownership in the local vernacular and provide the community with tools for informing incomers about the local language situation. [ABSTRACT FROM AUTHOR]

For almost 30 years, digital nonlinear editing systems (DNLEs) have functioned as a practical replacement for film and videotape editing. While DNLEs have progressed in their capabilities by offering increased video resolutions and visual effects, they have not fundamentally changed their operational constructs. Editors must still choose in and outpoints of shots and then methodically edit those shots into a cohesive sequencing. Technology improvements in artificial intelligence and machine learning have the potential to profoundly impact how DNLE systems operate, and, in turn, content creation methodologies will dramatically change. This paper examines the effects of image recognition, natural speech processing, language recognition, cognitive metadata extraction, tonal analysis, and data and statistical integration on the creation of content. [ABSTRACT FROM AUTHOR]

In the past several decades, much research has been carried out on the wireless sensor network which is widely used in the fields of national defense and national economy within China. The main function of the language sensor is to transfer the voice signal into an electrical signal so as to facilitate the subsequent analysis and processing. Combined with the wireless network signal, it is widely used in banks, shopping malls, examination rooms, prisons, important places of military affairs and other places. This paper makes a detailed introduction of some theoretical knowledge of language recognition and puts forward the recognition algorithm in which the language signal is abstracted by language features. Afterwards, Matlab is used to make simulation in-depth research and classification based on Support Vector Machine. Finally, a large number of samples are collected for the experiment so as to research the effect of weighted feature value and structure of classification on speech recognition rate. The conclusion of the paper provides a basis for further subsequent theoretical study. [ABSTRACT FROM AUTHOR]

Tibetan websites are important for studying the Tibetan language and Tibetan culture. The domain names and character encodings of Tibetan websites are complex. Three key technologies of collecting Tibetan websites are discussed in this paper. Firstly, using high frequency syllables in different Tibetan character encoding to searcher the web pages may be in Tibetan as many as possible. Then, using Tibetan syllable dot and the properties of HTML to judge the language of web pages is in Tibetan or not. Thirdly, according the relationships between the URL of Tibetan web page and the URL of Tibetan website, the URLs of Tibetan websites are gotten. About 100 Tibetan websites in three Tibetan character encodings (Unicode, BZD and Tongyuan) are collected by these methods. These key technologies are also useful for collecting websites in other minority language. [ABSTRACT FROM AUTHOR]