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Siri背后的技术

2011年10月23日 发表评论 阅读评论

今年10月,Apple发布了iphone 4S with IOS 5,其中最大的亮点就是一个语音搜索软件-Siri。一时间,各种geek,伪geek,码农,非码农都流行起调戏siri,各种调戏视频,音频大量出现。不过,常言道“外行看热闹,内行看门道”,作为一个“伪内行”,或者“欲做内行而不得”的人,根据自己的知识,以及一些搜索工具,尝试了解了一下Siri的“门道”,在这里做个总结,列出siri所可能用到的技术(所谓可能,是因为很多是我猜测,或者没有准确的来源的资料)。

Siri是IOS上的个人助理应用:此软件使用到自然语言处理技术,使用者可以使用自然的对话与手机进行互动,完成搜寻资料、查询天气、设定手机日历、设定闹铃等服务。(来自维基百科

Siri所用到的技术,很多人会回答,人工智能以及云计算,的确,总体来说,是这两样技术,不过,这种概述感觉几乎没有任何意义,和不直接说“计算技术”(注意,不是计算机技术)呢。因此,在本文,我将介绍下我了解Siri可能采用的技术(由于有个人猜测,不一定准确)。

首先,在前端方面,即面向用户,和用户交互(User Interface,UI)的技术,主要是语音识别以及语音合成技术。语音识别技术是把用户的口语转化成文字,其中需要强大的语音知识库,因此需要用到所谓的“云计算”技术。而语音合成则是把返回的文字结果转化成语音输出,这个技术理论上本地就能完成(以前用过科大讯飞的在windows mobile上的本地语音阅读软件,软件很小,但能读的很好,还支持方言),但不知道Siri是否如此,当然,在云端完成也并无不可,在当前无线带宽下,那点语音流量根本不算什么。

其次,后台技术,这些其实才是真正的大角色(当然,普通用户是不会在意的,他们只会觉得前端很炫,哎,这就是做后端的悲哀,小小感叹一下)。这些技术的目的就是处理用户的请求,并返回最匹配的结果,这些请求类型很多,千奇百怪,要处理好并不简单。基本的结构猜测可能是分析用户的输入(已经通过语音转化),根据输入类型,分别采用合适的技术(合适的技术后面)进行处理。这些合适的后台技术包括,①以Google为代表的网页搜索技术;②以Wolfram Alpha为代表的知识搜索技术(或者知识计算技术);③以Wikipedia为代表的知识库(和Wolfram Alpha不同的是,这些知识来自人类的手工编辑)技术(包括其他百科,如电影百科等);④以Yelp为代表的问答以及推荐技术

下面,对上面提到的各种技术进行简要介绍(如有空,后面的博文可能会对某些技术详细的介绍,大家耳熟能详的就免了),强调下,介绍的有些参考来源是维基百科相关词条,下面不一一列出:

 

语音识别以及语音合成技术

语音识别技术,也被称为自动语音识别(英语:Automatic Speech Recognition, ASR),其目标是将人类的语音中的词汇内容转换为计算机可读的输入,例如按键、二进制编码或者字符序列。与说话人识别及说话人确认不同,后者尝试识别或确认发出语音的说话人而非其中所包含的词汇内容。语音识别技术所涉及的领域包括:信号处理、模式识别、概率论和信息论、发声机理和听觉机理、人工智能等等。

语音合成是将人类语音用人工的方式所产生。若是将电脑系统用在语音合成上,则称为语音合成器,而语音合成器可以用软/硬件所实现。文字转语音(text-to-speech, TTS)系统则是将一般语言的文字转换为语音,其他的系统可以描绘语言符号的表示方式,就像音标转换至语音一样。

相比于语音识别,语音合成感觉难度要低,并且基本能够在本地完成(很多OS都有内置的语音合成引擎)。而语音识别,由于涉及到语义理解,难度大大加大,需要大量的知识库,一般需要在云端完成。

另外,在国内,这方面做的很好的公司也有,比如鄙人本科所在学校的科大讯飞,就是中文语音合成以及识别的领军企业,在英文语音合成领域其实也很牛,拿过很多奖。国外的话,比较早的应该是IBM ViaVoice。

网页搜索技术

这个我想没必要介绍,大家天天用的Google就是这个技术的代表。而国内的话,则以百度为代表。

知识计算(搜索)技术(Computational Knowledge)

这个技术的代表是Wolfram|Alpha。

不同于搜索互联网信息,Wolfram|Alpha将从公众的(包括公开的网页等)和获得授权的资源中,发掘、建立起一个异常庞大的经过组织的数据库,再利用高级的自然语言算法进行处理,最终构造出一个类似于谷歌搜索的工具。

和网页搜索技术不同的是,在这个系统中,得到的答案结构化程度很高,比如搜索China,能得到和中国相关的各种参数以及资料,并以接近表格的方式呈现。Wolfram|Alpha也能理解部分自然语言,比如输出How old are you,其会回答Wolfram|Alpha的年龄。想测试这项技术的请移步Wolfram|Alpha

这个技术国内做的应该有,但还没有产品,也许百度的框计算算是半个。所以机会大大的有。

知识库技术

这名字是我自己起的,不知道有没有贻笑大方。。。这个技术的代表是维基百科,以及各种专门的百科网站。相比于网页搜索技术,基本以一个词条或者主题为单位,因此得到的数据价值高,知识量大,并且结构化程度好。相比于知识计算技术,这些技术需要人的参与,这有利也有弊,利就是,毕竟暂时人比机器聪明,编辑出来的知识更丰富,准确;弊就是,人力有限,即使像维基那样,发动社区的力量,也不能产生足够的知识,而知识计算,理论上,只需要算法够牛叉,是可以产生“无限”的知识的。

另外,写到这里,我想起Yahoo和Google的故事,当年,Yahoo是搜索老大,就像现在的Wekipedia在知识搜索领域一样,而Google是小弟,就像Wolfram|Alpha在知识搜索领域一样。但后来,却反过来了,Googe成了网页搜索老大,Yahoo成了小弟,原因就是Google相信算法的结果,把所有事都交给算法做,而Yahoo,很多索引都是人工编辑的。我想,也许有一天,Wekipedia和Wolfram|Alpha也会出现这样的情况。

问答推荐技术

其实这不能称为一个技术,应该属于知识库的技术。不同的是,这个技术针对的是一些生活信息,这些信息的地域化程度很高,典型代表为Yelp。由于这东西比较简单,就不仔细介绍了。

其实在国内,这方面的网站也有,那就是大众点评网这些。

 

读完此文,你也许会发现,其实Siri并没有什么革
命性的技术,其本质是将各种已经比较成熟的技术融合成一个产品,最终呈现给用户,因此,完全没必要对Siri神圣化。

另外,Siri其实是苹果买来的,在苹果买来之前,Siri本来是会推出Android版本的,可是,被苹果购买后,Android版本的Siri遥遥无期了(不过,这为广大的Android开发者以及公司提供了机会)。而Siri之前默默无闻,但是一从苹果推出,却声名鹊起,不得不佩服Apple以及Jobs的眼光以及执行力。



本文作者: Sigma    在新浪微博关注SigmaSigmaWeibo    RSS订阅本博客
本文链接: http://www.sigma.me/2011/10/23/the-technology-behand-siri.html
本博客采用知识共享署名—非商业性-禁止演绎使用3.0协议进行许可,转载请保留作者和原文链接。

  1. 2011年10月24日04:08 | #1

    @mr.pppoe

    nod,感觉苹果牛就牛在合适的时机采用了合适的技术

    太早采用,会成为先烈

    太晚,成为炮灰

  2. 2011年10月24日05:43 | #2

    Hi Sigma

    我在cnbeta看到你写的关于Siri的文章,很漂亮,其中涉及一些语音合成的技术对此很感兴趣,可以通过邮件认识一下么?谢谢

  3. 2011年10月24日08:56 | #3

    为什么最关键的技术被一语带过了。。。

    “基本的结构猜测可能是分析用户的输入(已经通过语音转化),根据输入类型,分别采用合适的技术(合适的技术后面)进行处理。”

    Siri的革命性在于语义解析,其他基本都是现有技术的整合。从语音识别到后端技术整合这之间才是AI部分。

  4. 2011年10月24日14:59 | #4

    @passingby

    看了这篇文章,感觉语义分析也不是那么牛叉,都是早已经很成熟,Wolfram|Alpha肯定就用了

    http://www.cnbeta.com/articles/159561.htm

  5. 2011年10月24日01:58 | #5

    其实应该说是一个语音问答系统。问答系统做了很多年了,相比而言,Siri在本地服务支持方面要好很多。

    语音也做了很久了,看起来siri在未经训练的情况下,也有不错的识别率。

    总的来说,技术上貌似没有大的突破。但是工程上做得相当好。

  6. 2011年10月24日08:11 | #6

    分析的很好。其实国外已经在研究web3.0技术了,并且有许多应用。Siri在语音帮助下,让百姓切身体验到并理解基于知识的搜索技术。iPhone的功劳可能就体现在拉近人与科学技术的距离。

  7. 2011年10月25日05:02 | #7

    单句语义解析相对而言不是很复杂,至少是可预见的关键词判断,但Siri的强大在于上下文结合以及数据库扩展性,这要求算法本身就需要非常良好的兼容性,表现在前端就是模拟真实的人机对话,而不是传统意义上的输入输出

  8. 2011年10月24日05:22 | #8

    后端人工智能部分好像没提?

  9. 2011年10月24日10:54 | #9

    通篇都没有提到,Siri中最重要的技术无疑是语义分析,从而用正确的参数与引擎去给出正确的答案。

  10. 2011年10月24日11:02 | #10

    @passingby

    我承认这一点是本文的缺陷,谢谢指正

    解释同上

  11. 2011年10月25日07:35 | #11

    @Sigma

    语义分析离成熟差很远,所以那文章才总结出来那么少(更像是有语法规则的句子),也只是容易识别的部分而已。

    人的每一句话都可以用其它的方式描述,主谓宾什么的并非一成不变。。

    语义分析成熟的时候,机器高准确度同声传译就没难度了(现在许多都停留在语法分析阶段)。

    wolfram上的搜索也只有语法分析,而且支持度有限,测试便知。

  12. 2011年10月24日10:59 | #12

    @lirujinzhi

    对,所以这样的产品能够成功

    而像linux这样的,虽然作用很大,但还是不能说是成功

  13. 2011年10月24日15:04 | #13

    我晕!昨天在cnbeta上看到这篇!没注意作者!

  14. 2011年10月24日16:47 | #14

    我觉得语义解析应该是个很复杂的事情。没有4S,不敢说。语音到字符、字符到语音都好说,根据字符再到回答这块具体是怎么做的我也很好奇。有空也去关注下!

  15. 2011年10月25日01:29 | #15

    @Rex

    可能我上面那段有点偏,其实我想说的是siri的语义分析不是很牛叉(就是其实有很多问答机器人就有类似的水平),我的来源是这篇文章

    http://www.cnbeta.com/articles/159561.htm

    当然,语义分析整个东西应该是很难的,感觉这算是整个人工智能中一个很大的问题

  16. 2011年10月24日11:02 | #16

    @benjy

    你说的很对,Siri很关键一部分是这些的整合,也即所谓的语义解析

    但是语义解析应该也不是siri独有的,这些研究和demo很早就有了,像Wolfram|Alpha也有语义解析,否则不能回答类似how old are you的问题

    我是个“伪内行”而已,所以。。。

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  40. 2019年1月20日09:42 | #40

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  41. 2019年1月21日03:55 | #41

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  42. 2019年1月21日10:29 | #42

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  43. 2019年1月21日18:11 | #43

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  44. 2019年1月22日12:31 | #44

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  45. 2019年1月22日21:21 | #45

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