狮身人面像无法识别字母精度很低 [英] sphinx to recognize alphabet accuracy is very low

问题描述

我正在使用CMU狮身人面像来识别字母，但是我注意到
的准确性非常低。（< = 20％）。例如
：当我拼写字母：APPLE时，它就出来了ABBLE。
的准确性太低而无用。

i'm using CMU sphinx to recognize alphabet letters, but i'm noticing very low accuracy.( <=20%). for example : when I spelling letters: A-P-P-L-E, it come out A B B L E. the accuracy is too low to be useful.

我希望不必像提到的某些帖子那样实现它，使用
alpha beta等来提高识别率。

I hope don't have to implement it like some posts mentioned, using "alpha""beta" etc. for improving the recognition rates.

dict文件和lm文件在在线lmtools 中生成
BTW：准确性当我限制字典并用麦克风对着麦克风讲话时，我的回答率超过80％。
所以有人解决过这个问题吗？或任何想法是赞赏。谢谢 。

the dict file and lm file in generate in online lmtools BTW: the accuracy rate is above 80% when i limit the dict and speak to microphone with words . so does anyone solve the problem before ? or any idea is appreciate. thx .

推荐答案

是的，由于字母名称容易混淆，准确性会很低。众所周知，识别E，D，P，B，C，Z的集合是最难的识别任务之一。正是由于这个原因，其他人使用alpha，bravo等。

Yes, accuracy is going to be low because letter names are confusable. The set to recognize E,D,P,B,C,Z is well known to be one of the hardest recognition tasks. Exactly for that reason others use alpha, bravo and so on.

更好的解决方案是设计应用程序，因此不需要拼写。您只需输入单词，它就是可靠且准确的。

The better solution would be to design your application so it will not require spelling. You can just input words, it's reliable and accurate.

您始终可以通过为自己的词汇量训练自己的模型或使现有模型适应语音来提高准确性。

You can always improve accuracy by training your own model for the vocabulary you have or by adapting existing model to your voice.

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