我在做一个应用程序，允许人们说话，几个选项（弦乐）之间进行选择。我有一个小问题，使得Android的语音识别适合我的想法。

I'm making an app that allows people to speak and select between a few options (Strings). I'm having a little problem making the Android Speech Recognizer fit my idea.

有只传递给SpeechRecognizer是有效的，并有它的之间的最佳的比赛？

Is there a way to just pass to the SpeechRecognizer the parameters that are "valid" and having it select between those the "best" match?

我不需要code，我只是需要一些指导，我的谷歌福看来今天要失败的我。

I don't need the code, I just need some guidance as my google-fu seems to be failing me today.

推荐答案

我们的解决这个问题，在 HTTP描述：//kaljurand.github.io/Grammars/ ，例如：看看从这个页面链接的文件：

Our solution to this problem is described at http://kaljurand.github.io/Grammars/, e.g. check out the paper linked from this page:

Kaarel Kaljurand，TanelAlumäe。在语音控制自然语言   基于识别用户界面（CNL 2012）

Kaarel Kaljurand, Tanel Alumäe. Controlled Natural Language in Speech Recognition Based User Interfaces (CNL 2012)

的基本思想是：

请不要使用谷歌的语音识别，因为你不能（目前）通过语言模型（如语法）把它（在我们的情况下，它也不支持输入语言，我们想用）; ，所以你需要实现自己的语音识别（例如，基于狮身人面像），并使其接受语法为输入的一部分; 执行语法。如果它是可以接受的词组一个简单的列表，然后JSGF会做的语法描述语言，对于较复杂的语法，我建议语法框架（可以自动编译到JSGF或有限状态自动机）; 在实现一个Android应用程序，它通过添加方式对语法传递给识别器扩展了RecognizerIntent API。您可以如碱它在Kõnele。 don't use Google's speech recognizer because you cannot (currently) pass the language model (e.g. a grammar) to it (in our case it also didn't support the input language that we wanted to use); so you need to implement your own speech recognizer (e.g. based on Sphinx) and make it accept grammars as part of the input; implement the grammar. If it's a simple list of acceptable phrases then JSGF will do as the grammar description language, for more complex grammars I recommend Grammatical Framework (which you can automatically compile to JSGF or finite-state automata); implement an Android app that extends the RecognizerIntent API by adding a way to pass the grammar to the recognizer. You can base it e.g. on Kõnele.

这一切都可能是你的情况矫枉过正。后处理谷歌的结果（如@gregm建议）肯定是更容易实现。但是，如果你想扩展到更复杂和/或多种语言的语言模型，那么我们的做法无疑提供了所需的模块化和EX pressive力量。

All this might be an overkill in your case. Post-processing of Google's results (as @gregm suggests) is certainly easier to implement. But if you want to scale to more complex and/or multilingual language models then our approach certainly provides the required modularity and expressive power.