谁帮我翻译下这段话，感激不尽啊

Question

Accuracy
The performance of a speech recognition system is measurable. Perhaps the most widely used measurement is accuracy. It is typically a quantitative measurement and can be calculated in several ways. Arguably the most important measurement of accuracy is whether the desired end result occurred. This measurement is useful in validating application design. For example, if the user said "yes," the engine returned "yes," and the "YES" action was executed, it is clear that the desired end result was achieved. But what happens if the engine returns text that does not exactly match the utterance? For example, what if the user said "nope," the engine returned "no," yet the "NO" action was executed? Should that be considered a successful dialog? The answer to that question is yes because the desired end result was acheived.
Another measurement of recognition accuracy is whether the engine recognized the utterance exactly as spoken. This measure of recognition accuracy is expressed as a percentage and represents the number of utterances recognized correctly out of the total number of utterances spoken. It is a useful measurement when validating grammar design. Using the previous example, if the engine returned "nope" when the user said "no," this would be considered a recognition error. Based on the accuracy measurement, you may want to analyze your grammar to determine if there is anything you can do to improve accuracy. For instance, you might need to add "nope" as a valid word to your grammar. You may also want to check your grammar to see if it allows words that are acoustically similar (for example, "repeat/delete," "Austin/Boston," and "Addison/Madison"), and determine if there is any way you can make the allowable words more distinctive to the engine.
Recognition accuracy is an important measure for all speech recognition applications. It is tied to grammar design and to the acoustic environment of the user. You need to measure the recognition accuracy for your application, and may want to adjust your application and its grammars based on the results obtained when you test your application with typical users.

Answer 1

准确性
在一个语音识别系统性能的舒适度。也许最广泛使用的测量精度。这是一个典型的定量测量,就可以计算出有几个方面。可以说是最重要的测量精度,是理想的最终结果是否发生过的事。这个测试是验证程序设计中有用。举例来说,如果用户说“是的”,引擎返回“是的,”和“是的”行动被处死,很明显的是,理想的最终结果,取得了较好的应用效果。但是如果引擎返回文本,并不完全匹配的话语吗?举个例子,如果用户说“不”,这个引擎返回"没有",但"不"行动被处死吗?那应该被认为是一个成功的对话吗?这个问题的答案是肯定的,因为理想的最终结果是目的。
另一个测量的准确度的发动机是否认识到底是公认的话语的。这个度量的识别精度是以百分比表示,代表数助词公认的正确的总数表达口语。这是一个很有用的测量时,确认语法的设计。利用前面的例子,如引擎返回“不”,当用户说“不”,这也被认为是一个错误。基于精度测量时,你可能想要分析你的语法决定是否有任何你能提高准确度。例如,你可能需要添加“不”作为一种有效的话,你的语法。你可能也想看看你的语法,看看它是否允许的话,从声学角度类似的(例如,“重复/删除”、“奥斯汀/波士顿,”和“艾迪/麦迪逊”),并决定是否有任何方法可以让你的允许的话,更与众不同的引擎。
识别精度的重要举措,是所有语音识别中的应用。它与语法的设计和声学环境的用户。你需要测量精度的识别你的申请,并想调整你的应用程序,它的语法基于获得的结果,当你测试你的应用程序和典型的用户。

能把它打下来..你也真是厉害啊！！