Establishing a Methodology for Benchmarking Speech Synthesis for Computer-Assisted Language Learning (CALL).
Establishing a Methodology for Benchmarking Speech Synthesis for Computer-Assisted Language Learning (CALL).
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建立计算机辅助语言学习 (CALL) 语音合成基准测试方法。
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
Marie
中科院分区：
文献类型：
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作者：
Zöe Handley;Marie
尽管语音合成带来了新的可能性，但很少有集成语音合成的计算机辅助语言学习（CALL）应用程序已经进入市场。一个潜在的原因是，在CALL中使用语音合成的适用性和好处尚未得到证明。一种方法是通过评估。然而，很少有正式的评估语音合成CALL的目的已经进行。在这方面忽视评价的一个可能原因是，评价在时间和资源方面都很昂贵。这是一个重要的问题，因为有几个层次的评价，这些应用程序将从中受益。基准测试，由一个系统获得的分数与一个已知的，以保证用户满意度在一个标准的任务或一组任务的比较，被引入作为一个潜在的解决方案，这个问题。在这篇文章中，我们报告了我们的进展，对这些基准之一，即一个基准，用于确定充分的语音合成系统中使用的CALL。我们通过展示一个案例研究的结果来做到这一点，该研究旨在确定确定语音合成系统输出是否足以用于CALL中的各种角色的标准，以期选择能够解决这些问题的基准测试标准。这些角色（阅读机器、发音模型和会话伙伴）也在这里讨论。结论部分提出了进一步研究和评价的议程。
Despite the new possibilities that speech synthesis brings about, few Computer-Assisted Language Learning (CALL) applications integrating speech synthesis have found their way onto the market. One potential reason is that the suitability and benefits of the use of speech synthesis in CALL have not been proven. One way to do this is through evaluation. Yet, very few formal evaluations of speech synthesis for CALL purposes have been conducted. One possible reason for the neglect of evaluation in this context is the fact that it is expensive in terms of time and resources. An important concern given that there are several levels of evaluation from which such applications would benefit. Benchmarking, the comparison of the score obtained by a system with that obtained by one which is known, to guarantee user satisfaction in a standard task or set of tasks, is introduced as a potential solution to this problem. In this article, we report on our progress towards the development of one of these benchmarks, namely a benchmark for determining the adequacy of speech synthesis systems for use in CALL. We do so by presenting the results of a case study which aimed to identify the criteria which determine the adequacy of the output of speech synthesis systems for use in its various roles in CALL with a view to the selection of benchmark tests which will address these criteria. These roles (reading machine, pronunciation model, and conversational partner) are also discussed here. An agenda for further research and evaluation is proposed in the conclusion.
DOI：
10.1007/978-3-319-03068-5_10
发表时间：
2013
期刊：
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影响因子：
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作者：
Roast C
通讯作者：
Roast C