Podcasts about ssml

In this episode of Two Voice Devs, hosts Allen Firstenberg and Mark Tucker discuss the CSS Speech Module Level 1 Candidate Recommendation Draft, a standard that enables webpages to talk, developed in collaboration with the voice browser activity. They explore its features including the 'aural' box model concept, voice families, earcons and more, drawing parallels with SSML and highlight its innovative approach to web accessibility complementing screen readers. Despite acknowledging its potential, they address some of its key omissions such as phonemes and the lack of a background audio feature. 00:04 Introduction and Welcome 01:14 Exploring the Concept of Webpages Talking 03:00 Deep Dive into CSS Speech Module 03:48 Understanding the Scope of CSS Speech Module 04:27 The Evolution of Voice Interaction 05:22 Comparing CSS Speech with SSML 07:13 The Power of CSS in Voice Development 22:49 The Impact of Voice Balance Property 29:20 The Limitations of CSS Speech 39:37 The Future of CSS Speech 42:50 Conclusion and Final Thoughts

Giuliana Schiavi"L'incredibile storia di Olaudah Equiano, o Gustavus Vassa, detto l'Africano"Olaudah EquianoOccam Editorehttps://occameditore.itOlaudah Equiano ha undici anni quando viene rapito nel villaggio di Essaka, da qualche parte in Africa occidentale. L'unica vita che conosceva non c'è più: prigioniero su una nave negriera diretta nel Mar dei Caraibi, verrà venduto come schiavo a un capitano della Royal Navy. Inizia così una traversata delle zone di confine fra la vita e la morte in cui la natura umana si manifesta – come forse a nessun'altra latitudine – con assoluta brutalità. Ma Equiano decide di non abbandonarsi alla disperazione. Decide di vivere. Insieme al suo padrone, lascia le Americhe e percorre le vie del mondo: naviga fino in Inghilterra, solca l'Egeo e il Mediterraneo, visita la Turchia, l'Italia, la Spagna, ritorna in Africa, partecipa a una missione diretta al Polo Nord, convinto (con Orazio) che «chi va per mare cambia cielo, non animo». Combatte contro i francesi nella guerra dei Sette anni, commercia rum nelle Indie Occidentali, impara a leggere e riscatta la propria libertà. Questa autobiografia è del 1789. Equiano la scrive per denunciare gli orrori dello schiavismo che oggi, in un mondo di migrazioni irreprimibili, ricordano altri orrori – a noi vicini.A cura di Giuliana SchiaviGiuliana Schiavi insegna Traduzione dall'inglese all'italiano e Teoria della traduzione presso la SSML di Vicenza (di cui è rappresentante legale) dove coordina anche i master di traduzione editoriale e tecnico–scientifica dall'inglese e di traduzione editoriale–letteraria dall'arabo; è stata più volte workshop leader ai seminari di traduzione letteraria del British Centre for Literary Translation della UEA, University of East Anglia, di Norwich, UK. Traduttrice e teorica della traduzione, si occupa da anni di strutture discorsive, argomento sul quale ha pubblicato alcuni articoli. Dal 2013 è membro del CdA della Fusp – Fondazione Universitaria San Pellegrino. Ha tradotto vari autori fra cui W M Thackeray (Il libro degli Snob), Henry James (Un bambino e gli altri; Giro di Vite); W D Howells (L'ombra di un sogno); Olaudah Equiano (L'incredibile storia di Olaudah Equiano, o Gustavus Vassa, detto l'Africano); K Moele (Stanza 207).https://traduzione-editoria.fusp.it/docenti/giuliana-schiavi_44.htmlIL POSTO DELLE PAROLEascoltare fa pensarehttps://ilpostodelleparole.itQuesto show fa parte del network Spreaker Prime. Se sei interessato a fare pubblicità in questo podcast, contattaci su https://www.spreaker.com/show/1487855/advertisement

Jason F. Gilbert (aka The BotMan) is the Lead Character Designer at Intuition Robotics. Originally a filmmaker, he's worked on loads of bots through the years for various brands on chat and voice, as well as digital humans (such as 'Anna') and now with ElliQ - a robotic companion for elderly Americans. We talked about personas, multimodality, TTS and SSML, empathetic bots like ElliQ and more... Hosted on Acast. See acast.com/privacy for more information.

Emmy Award-winning Respeecher is changing the speech synthesis game. Move over TTS and SSML, and enter Speech to Speech.CEO, Alex Serdiuk, joins us to share more.Supporting UkraineThe VoiceLunch Foundation is taking donations to help support the voice lunch and voice technology community in Ukraine. VUX World has, of course, donated. I plead with you to donate too.Donate hereLearn more at https://respeecher.com**Presented by Deepgram and Symbl.ai**Deepgram is a Speech Company whose goal is to have every voice heard and understood. We have revolutionized speech-to-text (STT) with an End-to-End Deep Learning platform. This AI architectural advantage means you don't have to compromise on speed, accuracy, scalability, or cost to build the next big idea in voice. Our easy-to-use SDKs and APIs allow developers to quickly test and embed our STT solution into their voice products. For more information, visit: https://deepgram.com/vuxworldSee how easy it is to add simple but powerful call coaching and call tracking functionality to your customer experience solutions with Symbl.ai's customizable Conversation Intelligence APIs. From calls to videos to text conversations — apply best in class contextual AI in no time by getting started for free at https://symbl.aiRunning order:00:00 Intro and presenting Deepgram and Symbl.ai04:05 Welcome Alex and closing the Ukraine airspace08:40 About Respeecher12:40 Sourcing voices14:10 Nixon and winning an Emmy17:37 Process of creating speech to speech25:34 Limitations of TTS for long for audio29:00 The future of voice acting34:25 Voice marketplace38:00 Pricing of speech to speech voices42:00 How to achieve higher quality voices43:30 Accessibility48:50 Endless use cases51:00 Ethics55:54 Outro and more information See acast.com/privacy for privacy and opt-out information.

In an audio-first environment, you want to sound like a movie or TV soundtrack... but with interaction and dynamic responses. With Google's flavor of SSML and Alexa's APLA, you can create these responses. Mark and Allen explore how these two methods are similar, and where they differ. For more info: Google's SSML "par" and "media" tags: https://developers.google.com/assistant/conversational/ssml#par Nightingale SSML editor: https://actions-on-google-labs.github.io/nightingale-ssml-editor/ Alexa's APLA: https://developer.amazon.com/en-US/docs/alexa/alexa-presentation-language/apla-interface.html

微信"siemensfm1847"，加入听友群，发现更多精彩！咨询西门子业务，请拨打400-616-2020“有救急用的电机请告知，我们全力配合。”眼看疫情不断发展，很多城市都在新建医院，顾凡给南京天加发了这样一条消息。丨大年初三，顾凡向客户了解需求。顾凡是西门子低压电机事业部（LVM）亚太区贝得品牌销售总监。他此前了解到南京天加要为武汉火神山等疫区医院提供中央空调。而天加中央空调所用的电机有80%都来自于西门子。在这样的危急关头，客户一定需要西门子的支持！“我们会联系您，感谢大力支持！”顾凡收到了这样的回复。他随时待命。果然，一天后，他收到了供货需求——天加需要近200台西门子三相异步电机紧急支援火神山医院，供货要快！接到需求的顾凡赶紧联系西门子电机（中国）有限公司（SSML）物流经理王鹏，请他安排发货。在SSML管理层微信群里，总经理邓洪指示大家一定要全力支持这个项目，并一定要保证现场工作同事们的安全！幸亏工厂早已有所准备，大家每天都处在待命状态，即便出门也经常查看手机，生怕错过什么。王鹏立即响应，但他发现，工厂里并没有所需型号电机的现货。疫情期间，工厂处于停工状态，紧急恢复生产需要不少手续，恐怕赶不及交货。但办法总比困难多。厂里技术人员仔细了解天加的需求，发现另一种型号的电机也能满足需求，而且工厂恰好也有库存。于是西门子和天加商定，为了抢时间，使用这款有库存的电机。1月30日，天加更新了订单，采购商定的电机，并希望SSML在四天后务必发货！危急关头，这是在和病毒赛跑。两天完成发货！工厂同事们全力支持，加紧处理订单，完成疫情期间进出工厂的手续、联系承运商……同事们春节期间在家紧急处理订单。 2月1日，SSML的潘良星、陈香林、孙飞在班长贾佑青的带领下一早就回到厂里，做好防护措施，体温测量正常后开始装货。和平时不同，因为人手紧缺，这次每个人都要兼顾两三道工序。大家心里着急，只顾着抓紧时间闷头干活，后来回想起来，整整一上午竟然互相一句话都没有说，四个人忙活到下午一点才装完。疫情期间，附近没有餐馆营业，大家就简单地吃了碗方便面，还开玩笑地说：“过年大鱼大肉吃多了，正好吃碗方便面调节调节。”闲谈之中大家得知当天是潘良星奶奶的九十大寿。贾佑青心里有些愧疚——早知道不该让小潘来的。潘良星说：“家里人还说呢，外面都封闭了，还要去上班？我告诉他们，这次是为了支持武汉，抗击疫情，我必须去。”这句话说到了大家的心坎里！就这样，187台西门子电机于2月1日发往天加南京工厂，比客户要求的还提前了两天！那一天是大年初八。187台电机火速发往南京。现在，这些电机已经被集成到天加中央空调里，在武汉火神山医院正常运转，于寒冬中为与病毒抗争的人们带来一丝温暖。西门子电机安装在天加中央空调设备上。 1月30日，SSML又收到了天加的第二批订单——575台电机，并已于2月10日加急生产。目前这批电机已经生产完毕，正在等待客户的发货通知。它们将被应用于位于北京、郑州和重庆的更多医院。SSML员工加急生产天加的第二批订单。第二批订单的电机已经生产完毕，正在等待客户的发货通知。 此外，SSML还于2月4日发送31台电机至皇家动力（武汉）有限公司，支持其风机生产。这批风机将被应用于位于湖北鄂州和上海的医院。疫情就是命令，SSML全员随时待命！

微信"siemensfm1847"，加入听友群，发现更多精彩！咨询西门子业务，请拨打400-616-2020“有救急用的电机请告知，我们全力配合。”眼看疫情不断发展，很多城市都在新建医院，顾凡给南京天加发了这样一条消息。丨大年初三，顾凡向客户了解需求。顾凡是西门子低压电机事业部（LVM）亚太区贝得品牌销售总监。他此前了解到南京天加要为武汉火神山等疫区医院提供中央空调。而天加中央空调所用的电机有80%都来自于西门子。在这样的危急关头，客户一定需要西门子的支持！“我们会联系您，感谢大力支持！”顾凡收到了这样的回复。他随时待命。果然，一天后，他收到了供货需求——天加需要近200台西门子三相异步电机紧急支援火神山医院，供货要快！接到需求的顾凡赶紧联系西门子电机（中国）有限公司（SSML）物流经理王鹏，请他安排发货。在SSML管理层微信群里，总经理邓洪指示大家一定要全力支持这个项目，并一定要保证现场工作同事们的安全！幸亏工厂早已有所准备，大家每天都处在待命状态，即便出门也经常查看手机，生怕错过什么。王鹏立即响应，但他发现，工厂里并没有所需型号电机的现货。疫情期间，工厂处于停工状态，紧急恢复生产需要不少手续，恐怕赶不及交货。但办法总比困难多。厂里技术人员仔细了解天加的需求，发现另一种型号的电机也能满足需求，而且工厂恰好也有库存。于是西门子和天加商定，为了抢时间，使用这款有库存的电机。1月30日，天加更新了订单，采购商定的电机，并希望SSML在四天后务必发货！危急关头，这是在和病毒赛跑。两天完成发货！工厂同事们全力支持，加紧处理订单，完成疫情期间进出工厂的手续、联系承运商……同事们春节期间在家紧急处理订单。 2月1日，SSML的潘良星、陈香林、孙飞在班长贾佑青的带领下一早就回到厂里，做好防护措施，体温测量正常后开始装货。和平时不同，因为人手紧缺，这次每个人都要兼顾两三道工序。大家心里着急，只顾着抓紧时间闷头干活，后来回想起来，整整一上午竟然互相一句话都没有说，四个人忙活到下午一点才装完。疫情期间，附近没有餐馆营业，大家就简单地吃了碗方便面，还开玩笑地说：“过年大鱼大肉吃多了，正好吃碗方便面调节调节。”闲谈之中大家得知当天是潘良星奶奶的九十大寿。贾佑青心里有些愧疚——早知道不该让小潘来的。潘良星说：“家里人还说呢，外面都封闭了，还要去上班？我告诉他们，这次是为了支持武汉，抗击疫情，我必须去。”这句话说到了大家的心坎里！就这样，187台西门子电机于2月1日发往天加南京工厂，比客户要求的还提前了两天！那一天是大年初八。187台电机火速发往南京。现在，这些电机已经被集成到天加中央空调里，在武汉火神山医院正常运转，于寒冬中为与病毒抗争的人们带来一丝温暖。西门子电机安装在天加中央空调设备上。 1月30日，SSML又收到了天加的第二批订单——575台电机，并已于2月10日加急生产。目前这批电机已经生产完毕，正在等待客户的发货通知。它们将被应用于位于北京、郑州和重庆的更多医院。SSML员工加急生产天加的第二批订单。第二批订单的电机已经生产完毕，正在等待客户的发货通知。 此外，SSML还于2月4日发送31台电机至皇家动力（武汉）有限公司，支持其风机生产。这批风机将被应用于位于湖北鄂州和上海的医院。疫情就是命令，SSML全员随时待命！

微信"siemensfm1847"，加入听友群，发现更多精彩！咨询西门子业务，请拨打400-616-2020位于江苏的西门子电机（中国）有限公司（简称SSML）2018年上线了西门子机床数字化监控系统。截至2019年3月，SSML内被系统覆盖的265台机床的利用率提高了约10%。同时，在不新增设备的情况下，SSML产品的日产量提高了5%。西门子机床数字化监控系统包含机床状态监控及工件追踪两个主要模块。西门子Simatic S7-1200可编程逻辑控制器（PLC）等设备将机床产生的实时数据采集上来，送至西门子Scalance交换机、数据采集服务器和数据库服务器，实现安全可靠的数据采集与存储。西门子机床数字化监控系统覆盖SSML机加工车间内超过200台机床。同时，系统会对数据进行分析和可视化，实时呈现不同工件的加工型号和数量、生产进度、整体产量以及机床状态和利用率等关键信息，实现生产透明化。这样一来，无论是在车间里还是在办公室中，相关人员都可以随时随地掌握生产状况，有的放矢地优化资源管理，从而提高机床利用率和生产效率。当设备出现异常时，系统会自动定位问题设备，并实时在维修班组的大屏幕上显示报警信息，指引维修人员及时解决问题。这些数据也能帮助维修人员理解异常原因以作出针对性改进，进一步减少停机时间。使用新的监控系统后，工厂内的设备故障率也大大降低。此外，系统还助力SSML促进生产标准化。由于不同数控加工程序涉及种类繁多的零件加工工艺，系统要求所有零件程序的命名都遵循统一的规则，以实现各个环节的高效沟通。在未来，SSML还将继续增加连入系统的机床数并持续优化系统功能。

微信"siemensfm1847"，加入听友群，发现更多精彩！咨询西门子业务，请拨打400-616-2020位于江苏的西门子电机（中国）有限公司（简称SSML）2018年上线了西门子机床数字化监控系统。截至2019年3月，SSML内被系统覆盖的265台机床的利用率提高了约10%。同时，在不新增设备的情况下，SSML产品的日产量提高了5%。西门子机床数字化监控系统包含机床状态监控及工件追踪两个主要模块。西门子Simatic S7-1200可编程逻辑控制器（PLC）等设备将机床产生的实时数据采集上来，送至西门子Scalance交换机、数据采集服务器和数据库服务器，实现安全可靠的数据采集与存储。西门子机床数字化监控系统覆盖SSML机加工车间内超过200台机床。同时，系统会对数据进行分析和可视化，实时呈现不同工件的加工型号和数量、生产进度、整体产量以及机床状态和利用率等关键信息，实现生产透明化。这样一来，无论是在车间里还是在办公室中，相关人员都可以随时随地掌握生产状况，有的放矢地优化资源管理，从而提高机床利用率和生产效率。当设备出现异常时，系统会自动定位问题设备，并实时在维修班组的大屏幕上显示报警信息，指引维修人员及时解决问题。这些数据也能帮助维修人员理解异常原因以作出针对性改进，进一步减少停机时间。使用新的监控系统后，工厂内的设备故障率也大大降低。此外，系统还助力SSML促进生产标准化。由于不同数控加工程序涉及种类繁多的零件加工工艺，系统要求所有零件程序的命名都遵循统一的规则，以实现各个环节的高效沟通。在未来，SSML还将继续增加连入系统的机床数并持续优化系统功能。

Der 315. Blick mit dem Schwerpunkt Voice Avatar, Voice User Interfaces, SSML und Polly.

Join us as we discuss SSML Alexa Friendly Audio

Saint Augustine said "This is the very perfection of a man, to find out his own imperfections."  On this episode we explore in detail the BEST "life hack" that will help the Executive Disciple course correct on our journey to eternity.  SSML is a powerful technique that used quarterly can help us make the adjustments we need to respond and adapt to a busy, dynamic, ever-changing life.

Join us as we discuss some useful SSML Tools

Join us as we discuss Using SSML When You Don't Have Voice Actors

Join us as we discuss What's SSML? Speech Coach for the Smart Speaker

添加微信"siemensfm1847"，加入听友群，发现更多精彩！咨询西门子业务，请拨打400-616-2020看着工人将一个个车削完成的铸铁机座从机床上搬下来，在工单上进行记录，之后再次装夹、启动机床，王达权觉得这些机床就好像一个个“黑匣子”，让人想知道它们到底“活儿干得怎么样”。王达权是西门子电机（中国）有限公司（SSML）生产部经理。在业内，他并不是唯一一个有这样的疑问的人。在中国机加工制造行业迈向“工业4.0”的过程中，企业首先需要做到的就是“将生产看个分明”，即实现设备状态的实时采集和监控，从而达到生产透明化。这也是SSML在从传统机加工模式向数字化生产转型时迈出的关键一步。在西门子工业服务工程师的协助下，2018年8月，西门子机床数字化监控系统在SSML正式上线。系统采用西门子数字化工厂集团与过程工业与驱动集团工业客户服务部为SSML量身定制的数控车间信息管理解决方案，帮助工厂实现了机床运行状态的透明化，不仅大大提升了机床利用率和生产效率，也降低了设备故障率。给我一双“慧眼”SSML占地超过13万平方米，每年生产90万台三相异步电机及15万台伺服电机。对电机生产来说，前端的车、磨、铣、钻等机床冷加工过程非常关键。SSML有数百台机床。每台机床每天的有效工作时间到底有多长？是不是有的机床总是在工作，而有的却好像在“偷懒”？这些问题的答案是打破工厂生产效率瓶颈的关键。因此，如何提高工厂内机床的利用率是SSML面临的最大挑战。以前，由于生产节奏较快，工厂无法及时获取工人每小时的实际产出信息，而人工点检产量又会导致部分设备无人值守，浪费加工时间，影响设备利用率。“在这种工作方式下，信息就好像一条受阻的河：不仅流得慢，还很不顺畅。因为信息反馈滞后，部门内的瓶颈机床换型频次过高，影响了所在产线的产量。”王达权表示。对工厂管理者来说，设备利用率的不透明使他无法快速找出瓶颈工序与设备从而进行调整，更无法根据设备使用情况制定新的机床采购计划。如今，为满足市场需求，SSML的产能逐年攀升。它需要一双“慧眼”来实时获取准确的数据，看破生产的“玄机”。为此，西门子工业服务工程师在仔细考察了工厂的实际情况后，提出了详尽的改造方案，工厂也专门成立了设备利用率改善小组协助项目推进。双方紧密协作，共同搭建机床数字化监控系统。“项目在实施过程中遇到了诸多挑战。一方面，需要系统覆盖的机床数量较大且情况复杂。另一方面，整个项目需要在尽量不影响工厂正常生产的情况下高效推进。这些都是对我们计划与施工能力的考验。”西门子数字化工厂集团与过程工业与驱动集团工业客户服务部业务经理刘伟表示，“项目最后提前一个月上线。这不仅是对我们过硬能力的证明，更是我们与SSML通力协作的成果。”心中有数，尽享其能西门子机床数字化监控系统覆盖SSML机加工车间内超过200台机床，包含机床状态监控及工件追踪两个主要模块。西门子Simatic S7-1200可编程逻辑控制器（PLC）等设备将机床产生的实时数据采集上来，送至西门子Scalance交换机、数据采集服务器和数据库服务器，实现安全可靠的数据采集与存储。同时，系统会对数据进行分析和可视化，实时呈现不同工件的加工型号和数量、生产进度、整体产量以及机床状态和利用率等关键信息，实现生产透明化。这样一来，无论是在车间里还是在办公室中，相关人员都可以随时随地掌握生产状况，有的放矢地优化资源管理，从而提高机床利用率和生产效率。使用新的监控系统后，工厂内的设备故障率也大大降低。当设备出现异常时，系统会自动定位问题设备，并实时在维修班组的大屏幕上显示报警信息，指引维修人员及时解决问题。这些数据也能帮助维修人员理解异常原因以作出针对性改进，进一步减少停机时间。此外，系统还助力SSML促进生产标准化。由于不同数控加工程序涉及种类繁多的零件加工工艺，系统要求所有零件程序的命名都遵循统一的规则，以实现各个环节的高效沟通。在项目中，西门子工业服务工程师帮助SSML完成了数百种零件程序的标准化命名。这不仅让零件长度等信息在名字中就一目了然，也让生产更加规范、高效。截至2019年3月，SSML内被系统覆盖的265台机床的利用率提高了约10%。同时，在不新增设备的情况下，SSML产品的日产量提高了5%。在未来，SSML还将继续增加连入系统的机床数并持续优化系统功能。“新的系统意味着新的开始。”西门子电机（中国）有限公司总经理邓洪表示，“更高的利用率和生产效率背后是工作方式的彻底变革，是我们向数字化工厂的大步迈进。我们相信，西门子的数字化技术将助力我们在行业中保持领先并持续改进！”

添加微信"siemensfm1847"，加入听友群，发现更多精彩！咨询西门子业务，请拨打400-616-2020看着工人将一个个车削完成的铸铁机座从机床上搬下来，在工单上进行记录，之后再次装夹、启动机床，王达权觉得这些机床就好像一个个“黑匣子”，让人想知道它们到底“活儿干得怎么样”。王达权是西门子电机（中国）有限公司（SSML）生产部经理。在业内，他并不是唯一一个有这样的疑问的人。在中国机加工制造行业迈向“工业4.0”的过程中，企业首先需要做到的就是“将生产看个分明”，即实现设备状态的实时采集和监控，从而达到生产透明化。这也是SSML在从传统机加工模式向数字化生产转型时迈出的关键一步。在西门子工业服务工程师的协助下，2018年8月，西门子机床数字化监控系统在SSML正式上线。系统采用西门子数字化工厂集团与过程工业与驱动集团工业客户服务部为SSML量身定制的数控车间信息管理解决方案，帮助工厂实现了机床运行状态的透明化，不仅大大提升了机床利用率和生产效率，也降低了设备故障率。给我一双“慧眼”SSML占地超过13万平方米，每年生产90万台三相异步电机及15万台伺服电机。对电机生产来说，前端的车、磨、铣、钻等机床冷加工过程非常关键。SSML有数百台机床。每台机床每天的有效工作时间到底有多长？是不是有的机床总是在工作，而有的却好像在“偷懒”？这些问题的答案是打破工厂生产效率瓶颈的关键。因此，如何提高工厂内机床的利用率是SSML面临的最大挑战。以前，由于生产节奏较快，工厂无法及时获取工人每小时的实际产出信息，而人工点检产量又会导致部分设备无人值守，浪费加工时间，影响设备利用率。“在这种工作方式下，信息就好像一条受阻的河：不仅流得慢，还很不顺畅。因为信息反馈滞后，部门内的瓶颈机床换型频次过高，影响了所在产线的产量。”王达权表示。对工厂管理者来说，设备利用率的不透明使他无法快速找出瓶颈工序与设备从而进行调整，更无法根据设备使用情况制定新的机床采购计划。如今，为满足市场需求，SSML的产能逐年攀升。它需要一双“慧眼”来实时获取准确的数据，看破生产的“玄机”。为此，西门子工业服务工程师在仔细考察了工厂的实际情况后，提出了详尽的改造方案，工厂也专门成立了设备利用率改善小组协助项目推进。双方紧密协作，共同搭建机床数字化监控系统。“项目在实施过程中遇到了诸多挑战。一方面，需要系统覆盖的机床数量较大且情况复杂。另一方面，整个项目需要在尽量不影响工厂正常生产的情况下高效推进。这些都是对我们计划与施工能力的考验。”西门子数字化工厂集团与过程工业与驱动集团工业客户服务部业务经理刘伟表示，“项目最后提前一个月上线。这不仅是对我们过硬能力的证明，更是我们与SSML通力协作的成果。”心中有数，尽享其能西门子机床数字化监控系统覆盖SSML机加工车间内超过200台机床，包含机床状态监控及工件追踪两个主要模块。西门子Simatic S7-1200可编程逻辑控制器（PLC）等设备将机床产生的实时数据采集上来，送至西门子Scalance交换机、数据采集服务器和数据库服务器，实现安全可靠的数据采集与存储。同时，系统会对数据进行分析和可视化，实时呈现不同工件的加工型号和数量、生产进度、整体产量以及机床状态和利用率等关键信息，实现生产透明化。这样一来，无论是在车间里还是在办公室中，相关人员都可以随时随地掌握生产状况，有的放矢地优化资源管理，从而提高机床利用率和生产效率。使用新的监控系统后，工厂内的设备故障率也大大降低。当设备出现异常时，系统会自动定位问题设备，并实时在维修班组的大屏幕上显示报警信息，指引维修人员及时解决问题。这些数据也能帮助维修人员理解异常原因以作出针对性改进，进一步减少停机时间。此外，系统还助力SSML促进生产标准化。由于不同数控加工程序涉及种类繁多的零件加工工艺，系统要求所有零件程序的命名都遵循统一的规则，以实现各个环节的高效沟通。在项目中，西门子工业服务工程师帮助SSML完成了数百种零件程序的标准化命名。这不仅让零件长度等信息在名字中就一目了然，也让生产更加规范、高效。截至2019年3月，SSML内被系统覆盖的265台机床的利用率提高了约10%。同时，在不新增设备的情况下，SSML产品的日产量提高了5%。在未来，SSML还将继续增加连入系统的机床数并持续优化系统功能。“新的系统意味着新的开始。”西门子电机（中国）有限公司总经理邓洪表示，“更高的利用率和生产效率背后是工作方式的彻底变革，是我们向数字化工厂的大步迈进。我们相信，西门子的数字化技术将助力我们在行业中保持领先并持续改进！”

Listen as we delve into the voice personality parameters you can use to shape synthetic voices. A goal of this process is to achieve a voice with personality traits that will be most persuasive with your users or target audience.We'll use an example of Darth Vader as a target voice personality, and play with different synthetic voice parameters to get as close as possible to the essence of that voice quality, using SSML with Amazon Polly.Episode Blog Posthttp://arrovox.com/2018/07/31/ep-02-synthetic-…ality-parameters/Resources"Wired for Speech", by Clifford Nasshttps://www.amazon.com/Wired-Speech-Activates-Human-Computer-Relationship-ebook/dp/B001949SMM/ CreditsHost: Doug SchumacherTwitter: @MemeRunnerProduction: Arrovox.comContact: d@arrovox.com

This week is all about Voices! 🎶🎤🔊 Mandy Chan joins Melanie and Mark to discuss the intricacies of building user Voice user interfaces with Actions on Google, developing with SSML and more! Mandy Chan Mandy Chan is the developer community manager for the Actions On Google team. Her role is to help expand the funnel of the Actions on Google developer community by creating practical tools and content like http://bit.ly/aog-codelab-1 and http://bit.ly/aog-codelab-2 Mandy began to build voice applications back in early 2016, and since then, she has built more than a dozen Voice Applications on Actions On Google and other platforms. One of her most frequently downloaded open source projects is called the SSML-Builder which creates well-formed Speech Synthesis Markup Language without worrying about string concatenation. You can learn more about her open source project on http://bit.ly/ssml-build When she is not pondering about how to improve the developer experience, you can find her hiking at mountains or learning new magic tricks. You can also learn more about Mandy by following @MandyChanNYC Cool things of the week AI at Google: our principles blog Incorporating Google’s AI Principles into Google Cloud blog Deploying to Google Kubernetes Engine blog Fighting fire with machine learning: two students use TensorFlow to predict wildfires blog Together, we can help Puerto Rico recover donation match Introducing sole-tenant nodes for Google Compute Engine — when sharing isn’t an option blog docs Interview Actions on Google site docs github console g+ community ssml-builder site npm Advanced SSML by Leon blog Actions on Google: SSML docs Actions on Google Codelabs level one level two Dialogflow site docs console Google Assistant SDK for devices site Cloud Functions for Firebase docs Google Action Firebase Services docs To get inspired by some interesting voice applications voice experiment Mandy Chan medium github Systers on June 21st 9AM PST – Getting started with Actions on Google Workshop site Question of the week I want to push a Docker image to Google Container Registry via docker push. How can I set things up so that I don’t have to use gcloud docker -- push every time? Pushing and Pulling Images docs Authentication Methods docs Where can you find us next? Mark is speaking at the San Francisco Kubernetes Meetup: Scaling Game Servers and the Conduit Service Mesh on June 14th, and also speaking at the Online Kubernetes Community Meeting on the 21st of June, at 10am Pacific. Melanie is speaking at a joint WiMLDS and PyLadies event “Paths to Data Science” on June 26th and Stanford AI4ALL on June 28th.

Although there are many ways to optimize the speech generated by Amazon Polly's text-to-speech voices, you might find it challenging to apply the most effective enhancements in each situation. Learn how you can control pronunciation, intonation, and timing for text-to-speech voices. In this session, you get a comprehensive overview of the available tools and methods available for modifying Amazon Polly speech output, including SSML tags, lexicons, and punctuation. You also get recommendations for streamlining application of these techniques. Come away with insider tips on the best speech optimization techniques to provide a more natural voice experience.

I had my first Alexa Skill certified today, one I built over the past couple of weeks for City Cinema here in Charlottetown. “Alexa Skills” are custom apps built for Amazon’s voice-controlled Echo line of products; think of them as a very early prototype of the computer on Star Trek, but lacking most of the artificial intelligence. While Echo devices aren’t yet available for sale in Canada, they work in Canada, at least mostly, and it’s clear they’ll be here eventually. So it’s a good time to build up some “voice app” muscle memory, and City Cinema was a good, simple, practical use case. Simple and practical because there’s really only one thing people want to know about City Cinema: what’s playing. Tonight. On Friday. Next Thursday. So here’s a high-level overview of what it took to make an Alexa Skill. First, I needed to select an Invocation Name. This is the “trigger word” or the “app name” that Alexa will glue to my skill. I selected the obvious: City Cinema. Next, I created an Intent Schema, a JSON description of the things my skill can do, its methods, in other words. In this case, it can only do a single thing–tell you what’s playing–so there’s only a single intent defined, WhatsPlaying, that has an optional parameter (called a “slot” in Alexa-speak), the date. There are also a few built-in intents added to the schema to allow me to define what happens when a user answers “yes” or “no” to a question, and when they cancel or stop. { "intents": [ { "intent": "WhatsPlaying", "slots": [ { "name": "Date", "type": "AMAZON.DATE" } ] }, { "intent": "AMAZON.YesIntent" }, { "intent": "AMAZON.NoIntent" }, { "intent": "AMAZON.CancelIntent" }, { "intent": "AMAZON.StopIntent" } ] } Next, I defined the Sample Utterances, a list of the actual things that users can say that will initiate a “what’s playing” lookup: WhatsPlaying what's playing on {Date} WhatsPlaying what's playing {Date} WhatsPlaying what's on {Date} WhatsPlaying what's showing on {Date} WhatsPlaying what is playing on {Date} WhatsPlaying what is playing {Date} WhatsPlaying what is on {Date} WhatsPlaying what is showing on {Date} WhatsPlaying showtimes for {Date} WhatsPlaying what are the showtimes for {Date} WhatsPlaying what are showtimes for {Date} WhatsPlaying showtimes for {Date} WhatsPlaying the schedule for {Date} WhatsPlaying schedule for {Date} Defining these utterances is where you realize that a lot of what we call “artificial intelligence” is still very ELIZA-like: a nest of if-then statements. Finally, I pointed the skill at an API endpoint on a server that I control. There are no limitations here other than that the endpoint must be served via HTTPS. From this point, I could code the endpoint in whatever language I liked; all I needed to do is accept inputs from Alexa, and respond with outputs. I opted to code in PHP, and to use the nascent third-party Amazon Alexa PHP Library as a convenience wrapper. There are a bunch of things the endpoint must do that using this wrapper makes easier: requests must be validated as having come from Amazon, and there must be application logic in place to respond to LaunchRequest, SessionEndedRequest, and IntentRequest requests. Other than that, the heavy lifting of the skill is relatively simple, at least in this case. When a user says, for example, “Alexa, ask City Cinema what’s playing tonight,” Alexa matches the utterance to one of those that I defined, WhatsPlaying what’s playing {Date}, and passes my endpoint the intent (WhatsPlaying) and the date (as YYYY-MM-DD). So I end up with a PHP object that looks, in part, like this: [intent] => Array ( [name] => WhatsPlaying [slots] => Array ( [Date] => Array ( [name] => Date [value] => 2017-03-02 ) ) ) From there I just use the same business logic that the regular CityCinema.net site uses to query the schedule database; I then munge the answer into SSML (Speech Synthesis Markup Language) to form the response. I pass back to Alexa a JSON response that looks like this: { "version": "1.0", "response": { "outputSpeech": { "type": "SSML", "ssml": "Playing at City Cinema on ????0302: Jackie at 7:00.Do you want to hear a description of this film?" }, "card": { "content": "Jackie at 7:00", "title": "Playing Thursday, March 2", "type": "Simple" }, "shouldEndSession": false }, "sessionAttributes": { "Operation": "FilmDetails", "Date": "2017-03-02" } } While I can return a plain text reply, using SSML allows me to express some additional nuance in how dates and times are interpreted, and to insert breathy pauses when it helps to increase clarity. Note that I also pass back some sessionAttributes values, Operation and Date. This allows me to respond properly when the user says “yes” or “no” in reaction to the question “Do you want to hear a description of this film?”; they are, in essence, parameters that are passed back to my endpoint with the follow-on intent. Like this, in part: case 'AMAZON.NoIntent': if (array_key_exists('Operation', $alexaRequest->session->attributes)) { $operation = $alexaRequest->session->attributes['Operation']; } switch ($operation) { case "FilmDetails": $message = ""; $message .= "Ok, see you at the movies!"; $message .= ""; $card = ''; $endSession = TRUE; break; } break; The Alexa Skills API also provides facility for passing back a “card,” which is a text representation (or variation) of the speech returned. For example, for a “what’s playing” intent, I return the name of the film and the time; if the user answers “yes” to the “Do you want to hear a description of this film?” question, then I follow up with a card that includes the full film description (I experimented with passing this back for speaking, but it was too long to be useful). And that’s it. The application logic is a little more complex than I’ve outlined, mostly to handle the edge cases and the required responses to things like a request without a date, or a request like “Alexa, launch City Cinema.” But the PHP endpoint code only runs 257 lines long. It is not rocket science. There’s an Apple-like certification process that happens once you’re ready to launch a skill to the public; in my case I submitted the skill for certification at 11:00 a.m. on February 28 and got back a positive response on March 2 at 1:46 a.m., so it was a less-than-48-hour turnaround. The skill is now live on Amazon.com. I foolishly selected “Canada” as the sole country where it would be available when I submitted the skill for certification; because the Echo isn’t available in Canada, this renders the skill effectively unusable for the moment because to use an Echo in Canada you have to pretend to be in the U.S. I’ve opened this up to all countries now, which requires a re-certification. So in a few days the world should have access to the skill. And, eventually, when the Echo gets released in Canada, the skill should be of practical utility to Echo owners in the neighbourhood.

In this episode Dave and Alexa community member Jo Jaquinta cover their early gaming backgrounds, how Jo has built an Alexa Space Empire game and what pitfalls he ran into along the way. Jo also covers how he has increased engagement rates within his Alexa skills but continued to evolve the game with new ASK features like SSML audio.