blocks|key|878516|text|我想，如果你能限制自己的发音，在英语中，你可能会想到类似的事情。对我来说(我是法国人)，像szczepan或wawrzyniec这样的词是无法发音的，当然也有一定的随机性。|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|878517|但他们实际上是波兰名+(意思是史蒂文和劳伦斯).|878518|entityMap|0|LINK|mutability|MUTABLE|url|http://www.rootsweb.ancestry.com/~polwgw/namelist.html^0|F|3|0|7|3|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@$9|Q|A|R|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|S|8|@]|D|@$9|T|A|U|1|V]]|E|$]]|$1|H|3|-4|5|6|7|W|8|@]|D|@]|E|$]]]|I|$J|$5|K|L|M|E|$N|O]]]]

I guess you could think of something like that if you could restrict yourself to pronounceable sounds in english. For me (I am French), words like szczepan or wawrzyniec are unpronounceable and certainly have a certain randomness.

But they are actually <a href="http://www.rootsweb.ancestry.com/~polwgw/namelist.html" rel="nofollow noreferrer">Polish first names</a> (meaning steven and lawrence)...

blocks|key|970842|text|我同意麦克的看法。但更重要的是，人们有时会有一些无法宣布的用户名，比如qwerty或rtfmorleave。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|970843|为什么要费心呢？|970844|<过时和错误，但我不删除，因为评论>|970845|更重要的是，没有机器人使用'zetztzgsd‘作为用户名，它们没有真实名称、可能的昵称等等，所以我认为这对你来说是浪费时间的。|offset|length|style|BOLD|970846|970847|entityMap^0|0|0|0|6|N|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|Q|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|R|8|@$H|S|I|T|J|K]]|9|@]|A|$]]|$1|L|3|-4|5|6|7|U|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|V|8|@]|9|@]|A|$]]]|N|$]]

I agree with Mac. But more than that, people sometimes have user name that aren't pronouncable, like qwerty or rtfmorleave. 

Why bother with that ?

&lt; obsolete and false, but i don't delete because of comments >

But more than that, no bots use 'zetztzgsd' as user name, they have dictionnary of realname, possible nick name, etc. so I think this would be a waster of time for you

&lt; / obsolete and false, but i don't delete because of comments>

blocks|key|878779|text|查一查n格分析。它被成功地用于自动检测文本语言，甚至在非常短的文本上也能很好地工作。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|878780|该在线演示(不再在线)承认‘双语点’为英语和'sdfgbhm342r3f‘为尼泊尔语。即使这是一场非常糟糕的比赛，它也可能总是返回最好的比赛。我想你可以把它训练成区分“可发音”和“随机”。|878781|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

Look up n-gram analysis. It is successfully used to automatically detect text language and works surprisingly well even on very short texts.

The online demo (no longer online) recognized 'bilbomoothof' as English and 'sdfgbhm342r3f' as Nepali. It probably always returns the best match, even if it's a very poor one. I think you could train it to discern between 'pronounceable' and 'random'.

blocks|key|2404670|text|只需使用卡普查作为注册过程的一部分。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2404671|你永远无法区分真实的用户名和由机器人创建的用户名，而不会对你的用户造成严重的困扰。|2404672|您将阻止使用bizzare或非英语名称的用户，这会激怒他们，而机器人只会继续尝试，直到找到一个好的用户名(顺便说一下，从字典或其他来源--+这是一个非常好的！)。|2404673|编辑:寻找预防而不是事后分析？|style|BOLD|2404674|解决方案是让其他人为您管理用户身份。例如，您可以使用一个小的OpenID提供程序列表(就像这样)，或者使用facebook连接，或者两者都使用。您将确信用户是真实的，并且他们已经解决了至少一个CAPTCHA。|2404675|编辑：的另一个想法|2404676|搜索Google中的字符串，并检查找到的匹配数。不应该是你唯一的工具，但它也是一个很好的指标。当然，随机字符串应该很少或没有匹配。|2404677|entityMap|0|LINK|mutability|MUTABLE|url|http://recaptcha.net/|1|https://stackoverflow.com/users|2|https://stackoverflow.com/questions/1164511/can-i-use-the-facebook-credentials-to-for-users-to-access-my-site^0|4|3|0|0|0|1Y|8|1|0|0|F|0|1H|A|2|0|0|3|0|0^^$0|@$1|2|3|4|5|6|7|13|8|@]|9|@$A|14|B|15|1|16]]|C|$]]|$1|D|3|E|5|6|7|17|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|18|8|@]|9|@$A|19|B|1A|1|1B]]|C|$]]|$1|H|3|I|5|6|7|1C|8|@$A|1D|B|1E|J|K]]|9|@]|C|$]]|$1|L|3|M|5|6|7|1F|8|@]|9|@$A|1G|B|1H|1|1I]]|C|$]]|$1|N|3|O|5|6|7|1J|8|@$A|1K|B|1L|J|K]]|9|@]|C|$]]|$1|P|3|Q|5|6|7|1M|8|@]|9|@]|C|$]]|$1|R|3|-4|5|6|7|1N|8|@]|9|@]|C|$]]]|S|$T|$5|U|V|W|C|$X|Y]]|Z|$5|U|V|W|C|$X|10]]|11|$5|U|V|W|C|$X|12]]]]

Just use <a href="http://recaptcha.net/" rel="nofollow noreferrer">CAPTCHA</a> as a part of the registration process.

You can never distinguish real uesrnames from bot-created usernames, without severely annoying your users.

You will block users with bizzare, or non-English names, which will irritate them, and the bots will just keep trying until they catch a good username (from dictionary, or other sources - <a href="https://stackoverflow.com/users">this is a very nice one</a>, by the way!).

EDIT : Looking for prevention rather than after-the-fact analysis?

The solution is letting somebody else manage user's identities for you. For instance, you can use a small list of OpenID providers (like SO), or <a href="https://stackoverflow.com/questions/1164511/can-i-use-the-facebook-credentials-to-for-users-to-access-my-site">facebook connect</a>, or both.
You'll know for sure that the users are real, and that they have been solving at least one CAPTCHA.

EDIT: Another Idea

Search the string in Google, and check the number of matches found. Shouldn't be your only tool, but it is a good indicator, too. Randomized strings, of course, should have little or no matches.

blocks|key|970103|text|对问题1的答复：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|970104|​|970105|不幸的是，由于Kolmogorov复杂性函数是不可计算的，因此您无法生成这样的算法，除非您将一些规则应用于可能的用户名域，然后您将能够执行启发式分析和决定，但即使这样做也非常困难。|offset|length|970106|PS:在发了这个答案后，我遇到了一些服务，它给用户提供了用户名域限制的概念，让用户使用众所周知的公共域的邮箱作为用户名。|970107|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Kolmogorov_complexity^0|0|0|7|D|0|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|S|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$F|U|G|V|1|W]]|A|$]]|$1|H|3|I|5|6|7|X|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]]]

<h2>Reply for question #1:</h2>

Unfortunately this cannot be done, since <a href="http://en.wikipedia.org/wiki/Kolmogorov_complexity" rel="nofollow noreferrer">Kolmogorov complexity</a> function is not computable, therefore you cannot generate such algorithm unless you will apply some rules to domain of possible user names, then you will be able to perform heuristic analysis and decide, but even then it's really hard to do.

PS: After posted this answer, I bumped into some service which gave an idea of example for user name domain restriction, let to the users use the mail box of well known public domain as they user names.

blocks|key|2404458|text|从我的头顶上，你可以寻找音节，利用soundex。这就是我要探索的方向，基于一个可发音的单词至少有一个音节的假设。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2404459|编辑:这是一个计算音节的函数：|2404460|function+count_syllables($word)+{
+
$subsyl+=+Array(
'cial'
,'tia'
+,'cius'
+,'cious'
+,'giu'
+,'ion'
+,'iou'
+,'sia$'
+,'.ely$'
+);
++
+$addsyl+=+Array(
+'ia'
+,'riet'
+,'dien'
+,'iu'
+,'io'
+,'ii'
+,'[aeiouym]bl$'
+,'[aeiou]{3}'
+,'%5Emc'
+,'ism$'
+,'([%5Eaeiouy])\1l$'
+,'[%5El]lien'
+,'%5Ecoa[dglx].'
+,'[%5Egq]ua[%5Eauieo]'
+,'dnt$'
+);
++
+//+Based+on+Greg+Fast's+Perl+module+Lingua::EN::Syllables
+$word+=+preg_replace('/[%5Ea-z]/is',+'',+strtolower($word));
+$word_parts+=+preg_split('/[%5Eaeiouy]%2B/',+$word);
+foreach+($word_parts+as+$key+=>+$value)+{
+if+($value+<>+'')+{
+$valid_word_parts[]+=+$value;
+}
+}
++
+$syllables+=+0;
+//+Thanks+to+Joe+Kovar+for+correcting+a+bug+in+the+following+lines
+foreach+($subsyl+as+$syl)+{
+$syllables+-=+preg_match('~'.$syl.'~',+$word);
+}
+foreach+($addsyl+as+$syl)+{
+$syllables+%2B=+preg_match('~'.$syl.'~',+$word);
+}
+if+(strlen($word)+==+1)+{
+$syllables%2B%2B;
+}
+$syllables+%2B=+count($valid_word_parts);
+$syllables+=+($syllables+==+0)+?+1+:+$syllables;
+return+$syllables;
+}|code-block|syntax|javascript|2404461|从这个非常有趣的链接：|2404462|http://www.addedbytes.com/php/flesch-kincaid-function/|2404463|entityMap|0|LINK|mutability|MUTABLE|url|http://www.php.net/soundex|1^0|H|7|0|0|0|0|0|0|1I|1|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@$A|Y|B|Z|1|10]]|C|$]]|$1|D|3|E|5|6|7|11|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|12|8|@]|9|@]|C|$I|J]]|$1|K|3|L|5|6|7|13|8|@]|9|@]|C|$]]|$1|M|3|N|5|6|7|14|8|@]|9|@$A|15|B|16|1|17]]|C|$]]|$1|O|3|-4|5|6|7|18|8|@]|9|@]|C|$]]]|P|$Q|$5|R|S|T|C|$U|V]]|W|$5|R|S|T|C|$U|N]]]]

Off the top of my head, you could look for syllables, making use of <a href="http://www.php.net/soundex" rel="nofollow noreferrer">soundex</a>. That's the direction I would explore, based on the assumption that a pronounceable word has at least one syllable.
EDIT: Here's a function for counting syllables:
<pre><code>function count_syllables($word) {
 
$subsyl = Array(
'cial'
,'tia'
 ,'cius'
 ,'cious'
 ,'giu'
 ,'ion'
 ,'iou'
 ,'sia$'
 ,'.ely$'
 );
 
 $addsyl = Array(
 'ia'
 ,'riet'
 ,'dien'
 ,'iu'
 ,'io'
 ,'ii'
 ,'[aeiouym]bl$'
 ,'[aeiou]{3}'
 ,'^mc'
 ,'ism$'
 ,'([^aeiouy])\1l$'
 ,'[^l]lien'
 ,'^coa[dglx].'
 ,'[^gq]ua[^auieo]'
 ,'dnt$'
 );
 
 // Based on Greg Fast's Perl module Lingua::EN::Syllables
 $word = preg_replace('/[^a-z]/is', '', strtolower($word));
 $word_parts = preg_split('/[^aeiouy]+/', $word);
 foreach ($word_parts as $key =&gt; $value) {
 if ($value &lt;&gt; '') {
 $valid_word_parts[] = $value;
 }
 }
 
 $syllables = 0;
 // Thanks to Joe Kovar for correcting a bug in the following lines
 foreach ($subsyl as $syl) {
 $syllables -= preg_match('~'.$syl.'~', $word);
 }
 foreach ($addsyl as $syl) {
 $syllables += preg_match('~'.$syl.'~', $word);
 }
 if (strlen($word) == 1) {
 $syllables++;
 }
 $syllables += count($valid_word_parts);
 $syllables = ($syllables == 0) ? 1 : $syllables;
 return $syllables;
 }
</code></pre>
From this very interesting link:
<a href="http://www.addedbytes.com/php/flesch-kincaid-function/" rel="nofollow noreferrer">http://www.addedbytes.com/php/flesch-kincaid-function/</a>

blocks|key|2404696|text|你可以用神经网络来评估这个昵称是否看起来像一个自然语言的昵称。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2404697|组装两个数据集:一个是有效的昵称，另一个是假的。以字符值为输入，训练简单的后向递进单层隐层神经网络。神经网络将学习区分像"zrgssgbt“和"zargbyt”这样的字符串，因为zargbyt的辅音和元音混合在一起。|2404698|使用现实世界的例子来获得一个好的鉴别器是很重要的。|2404699|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

You could use a neural network to evaluate whether the nickname looks like a natural-language nickname.

Assemble two data-sets: one of valid nicknames, and one of bogus-generated ones. Train a simple back-progating single hidden layer neural network with the character values as inputs. The neural network will learn to discriminate between strings like "zrgssgbt" and "zargbyt", since the latter has consonants and vowels intermingled . 

It is important to use real-world examples to get a good discriminator.

blocks|key|2404508|text|我不知道这个问题的现有算法，但我认为可以通过以下任何一种方式攻击它：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2404509|你的机器人可能是垃圾，但你可以保留一个音节列表，或者更具体地说，音素，你可以尝试在你的给定字符串中找到。但是这听起来有点困难，因为你需要在不同的地方分割字符串等等。|unordered-list-item|2404510|英语字母中有5个元音，另外21个元音。你可以假设，如果它们是随机产生的，那么大约你会期望5/26*W，(其中W是字长)字母是元音，与此有很大的偏差可能是可疑的。(如果包括信函，则5/31等等)你可以尝试在这个想法的基础上，通过搜索双子，并试图确保每一个双例发生的概率相同等等。|2404511|此外，你可以试着在元音前后分割你的输入字符串，例如元音之前的三个字母和元音后面的三个字母，并试图通过与音素的比较来找出它是否能发出可识别的声音。|2404512|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$]]|$1|E|3|F|5|D|7|M|8|@]|9|@]|A|$]]|$1|G|3|H|5|D|7|N|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|O|8|@]|9|@]|A|$]]]|J|$]]

I dont know of existing algorithms for this problem, but I think it can be attacked in any one of the following ways:

<ul>
<li>your bot may be rubbish, but you can keep a list of syllables, or more specifically, phonemes, that you can try finding in your given string. But this sounds a bit difficult becasuse you would need to segment the string in different places etc.</li>
<li>there are 5 vowels in the english alphabet, and 21 others. You could assume that if they were randomly generated, then approximately you would expect 5/26*W, (where W is word length) letters that are vowels, and significant deviations from this could be suspicious. (If letter are included then 5/31 and so on..) You can try building on this idea by searching for doubletons, and trying to make sure that each doubleton occurs with same probability etc. </li>
<li>further, you can try to segment your input string around vowels, example three lettters before a vowel and three letters after a vowel, and try to find out if it make a recognizable sound by comparing with phonemes.</li>
</ul>

blocks|key|2404598|text|在俄语中，我们有禁止音节，如ГЙ，а，Ъ或Ь，在元音后面等等。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2404599|然而，垃圾邮件机器人只使用名称数据库，这就是为什么我的垃圾邮件收件箱里充斥着奇怪的名字，你只能在历史书中看到。|2404600|我希望英语也有音节分布直方图(比如ETAOIN+SHRDLU，但是对于两个字母甚至三个字母的音节)，在一个名字中有一个低频音节的临界密度当然是一个标志。|2404601|entityMap^0|E|2|J|1|L|1|0|0|H|D|0^^$0|@$1|2|3|4|5|6|7|L|8|@$9|M|A|N|B|C]|$9|O|A|P|B|C]|$9|Q|A|R|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|S|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|T|8|@$9|U|A|V|B|C]]|D|@]|E|$]]|$1|J|3|-4|5|6|7|W|8|@]|D|@]|E|$]]]|K|$]]

In Russian, we have forbidden syllables, like <code>ГЙ</code>, а <code>Ъ</code> or <code>Ь</code> after a vowel and so on.

However, spam bots just use the names database, that's why my spam inbox is full of strange names you can only meet in history books.

I expect English to have syllable distribution histograms too (like <code>ETAOIN SHRDLU</code>, but for two-letter or even three-letter syllables), and having critical density of low frequency syllables in one name is certainly a sign.

blocks|key|878795|text|请注意，许多大型站点都建议使用用户名，比如姓首。然后，用户将这些用户名传递到其他站点，并且前三个字母绝对不可读。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|878796|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Note that many large sites suggest usernames like [first init][middle init][last name][number]. The users then carry these usernames over to other sites, and the first three letters are definitely not pronounceable.

blocks|key|2404792|text|我见过机器人注册，用户名和全名都是随机大小写字母的字符串。它们通常至少有10个字母长，所以在这种情况下，不可能100%25准确，但是您可以通过首先传递任何具有非a字符的字符(例如空格、数字或特殊字符)就可以得到非常接近的结果。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2404793|然后，对于少数没有通过上述测试的人来说，如果有大写字母和小写字母，那么全名大写字母太多的字母就会失败，而大写字母通常不会超过三个或四个。如果用户名和全名都是JoHnDoE，或者是JohnSmithIII，您会犯一个错误，但这是非常罕见的情况。|2404794|您可以通过对一组已知的有效注册运行该算法来改进该算法。|2404795|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

I've seen bot registrations where both the username and full name are strings of random Upper- and lowercase letters. They tend to be at least 10 letters long, so in this case, it's not possible to be 100% accurate, but you can get pretty close by first passing any that have a non [a-zA-Z] character (e.g., space, number, or special character).
Then, for the few that haven't passed the test above, if there are both upper-and lowercase letters, failing those with too many uppercase letters in the full name, which normally wouldn't have more than three or four. You'll make an error with names like JoHnDoE for both the username and full name, or JohnSmithIII, but those are a pretty rare cases.
You can refine the algorithm by running it against a group of known valid registrations.

For the purpose of identifying [possible] bot-generated usernames.

Suppose you have a username like "bilbomoothof" .. it may be nonsense, but it still contains pronouncable sounds and so appears human-generated.

I accept that it could have been randomly generated from a dictionary of syllables, or word parts, but let's assume for a moment that the bot in question is a bit rubbish.

<ol>
<li>Suppose you have a username like
"sdfgbhm342r3f", to a human this is
clearly a random string. But can
this be identified programatically?</li>
<li>Are there any algorithms available
(similar to Soundex, etc..) that can
identify pronounceable sounds within
a string like this?</li>
</ol>

Solutions applicable in PHP/MySQL most appreciated.

how to check if a string looks randomized, or human generated and pronouncable?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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功能1上新10个字符

功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符。

功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符。

功能5描述100个字符功能5描述100个字符功能5描述100个字符功能5描述100个字符功能5描述100个字符功能5描述100个字符

功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符

功能4上新

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问如何检查字符串是否看起来是随机的，还是人为生成的和可发音的？
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问如何检查字符串是否看起来是随机的，还是人为生成的和可发音的？
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