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Q:How do I improve speech recognition on startup with PocketSphinx Android?

Q:如何提高对Android启动PocketSphinx语音识别?

I'm using PocketSphinx on Android. After the recognizer initializes, I start a keyword listener. At first, the recognizer will not match anything. But, after a few seconds, the recognizer starts matching keywords with excellent performance (about a 3% WER in initial testing). The time it takes to start matching depends on the word/phrase. It also seems to depend on how many times you say the word. For instance, "plus" is matched very quickly, usually on the first or second utterance, taking an average of 2 seconds to match. "A little help please", on the other hand takes around 10 seconds, or about 8-10 utterances. Once any keyword is matched, Sphinx enters its high-performance mode for all keywords. So, one workaround (although not a very good one) is to say "plus" immediately after initialization completes. During the time that no matching occurs, onBeginningOfSpeech() and onEndOfSpeech() are called by Sphinx, corresponding to the utterances of the key phrase or keyword.

Keyword file:

cancel last
a little help please
add new cut/1e-35/
set material
set quantity
plus/5e-2/
minus/5e-2/

I'm using pocketsphinx-android-5prealpha-nolib.jar, and (if it makes a difference) have tested on a Samsung Galaxy-S3 and a Motorola Moto E (2nd Gen). The problem is the same whether or not I use a headset.

我用PocketSphinx Android。系统初始化后,我开始一个关键词的倾听者。首先,识别器将没有任何比赛。但是,几秒钟后,开始的识别关键词性能优良的匹配(在初始测试3%的答案)。开始匹配的时间取决于单词/短语。它似乎也取决于多少次你说的话。例如,“加”匹配非常快,通常在第一次或第二次发言,平均2秒匹配。一点帮助请”,另一方面需要大约10秒,或约8-10话语。一旦任何关键字匹配,狮身人面像进入其高性能模式的所有关键字。因此,一种解决方法(虽然不是一个很好的人)是说“加”后立即初始化完成。没有发生匹配时,onbeginningofspeech()和onendofspeech()称为狮身人面像,对应的关键词或关键词的话语。

关键词的文件:

cancel last
a little help please
add new cut/1e-35/
set material
set quantity
plus/5e-2/
minus/5e-2/

我用pocketsphinx-android-5prealpha-nolib.jar,和(如果它有差别)已经测试了三星galaxy-s3和摩托罗拉Moto E(第二代)。无论我是否使用耳机,问题都一样。

answer1: 回答1:

Use the standard model that ships with the PocketSphinx demo, en-us-ptm. It's a lightweight* model, and has default CMN values set in the feat.params file. Since CMN values are set, Sphinx doesn't have to take time to set them on startup, which means there is no delay in getting to quality recognition results on startup. The overall recognition results with the default model compared to the others I've tested on is very similar with my command-and-control grammars.

* less than 7MB vs. some others like Voxforge that are more than double that

使用用PocketSphinx演示船舶标准模型,我们可。这是一个轻量级的模型,具有默认值设置在feat.params文件CMN。自从CMN的值设置,狮身人面像没有花时间把他们在启动时,这意味着要在启动质量识别结果没有延迟。与我测试过的其他人相比,默认模型的整体识别结果与我的命令和控制语法非常相似。

*小于7MB和其他一些像voxforge,倍不止

speech-recognition  pocketsphinx-android