编程语音接口：赋予连接设备声音

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"Programming Voice Interfaces .pdf" 本书"Programming Voice Interfaces"由Walter Quesada和Bob Lautenbach合著，旨在教会读者如何为连接的设备赋予语音功能，使其能够与用户通过语音进行交互。这本书深入探讨了语音界面的设计与编程技术，特别适合对语音识别和语音合成感兴趣的开发者和设计师。在当今的物联网(IoT)时代，语音接口正逐渐成为人机交互的重要方式，尤其是在智能家居、智能汽车和移动设备等场景中。本书将介绍如何利用现有的语音平台（如Amazon Alexa, Google Assistant, 和Apple Siri）以及相关的开发工具来创建高效、自然的语音体验。书中可能涵盖了以下关键知识点： 1. **语音识别技术**：包括基础的语音到文本转换（STT，Speech-to-Text）原理，如信号处理、特征提取、声学模型和语言模型等。 2. **自然语言理解**（NLU）：解释用户意图，理解口语表达中的复杂性和模糊性，如语义分析、对话管理及槽填充。 3. **语音合成技术**（TTS，Text-to-Speech）：如何将文本转化为流畅、自然的语音输出，包括不同的发音风格和情感表达。 4. **对话设计**：构建有效的语音用户界面（VUI），包括编写合适的语音指令、处理多轮对话和错误处理策略。 5. **特定平台的开发**：如Amazon的Alexa Skills Kit (ASK)和Google Assistant的Actions on Google，以及对应的开发流程和工具。 6. **测试与优化**：如何对语音应用进行测试，收集用户反馈，以及如何根据反馈优化语音体验。 7. **隐私与安全**：在开发语音接口时应考虑的隐私问题，如数据加密、用户信息保护等。 8. **实例分析**：书中可能会包含实际案例，分析成功的语音应用是如何设计和实现的，以供读者学习和参考。 9. **最佳实践**：作者可能分享了一些在设计和开发语音接口时的最佳实践和建议，帮助读者避免常见的陷阱。 10. **未来趋势**：讨论语音技术的未来发展，包括人工智能的进步、边缘计算的影响以及多模态交互的可能性。通过阅读本书，读者不仅可以掌握开发语音接口的基本技能，还能了解到当前行业的最新动态，为构建下一代人机交互系统做好准备。如果你有兴趣在物联网或智能设备领域开发创新的语音应用，这本书无疑是一份宝贵的资源。

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then by all means, add a voice interface to your toilet, if for nothing else, the enter‐

tainment value.

What’s a “Wake Word”?

Wake words are the words used to wake up a device (e.g., “Hey Siri,” “OK Google,” or

“Alexa”). These wake words trigger a user’s device to start listening and processing the

subsequent commands spoken by the user. We’ll talk more about wake words later in

this chapter.

When adding voice capability to inanimate objects, it is important to consider how

many devices within a small proximity will have voice enablement, how many of

them share the same wake words, and so on. Think of it this way: imagine being in a

room with a dozen voice-enabled devices, then you ask for the time and you get a

response from ten different voices. How annoying would that be on a daily basis? In

that scenario, ideally there would be one main voice interface that interacts with

many different devices.

In thinking about today’s consumer voice landscape, the everyday voice experiences

include Siri, OK Google, Cortana, and Alexa. Luckily, these all have their respective

wake words; therefore, having all these in the same room at the same time shouldn’t

be an issue unless you say something like “Hey Siri, search OK Google for Alexa.” As

close to the edge as edge cases can go, this one is pretty damn close but becoming

increasingly annoying as more voice interfaces get introduced into the market and,

therefore, more potential of clashing instead of communicating between devices is as

well. Amazon and Microsoft are trying to thwart this calamity with a newly minted

deal in which they will incorporate each of their AI personas on the other’s platform.

Additionally, users will sometimes mix up the wake words for their various devices—

for example, imagine someone pressing the button on their iPhone and saying “Hey

Alexa.” One of the biggest issues with voice is discoverability. Alexa has over 15,000

skills and it’s very difficult to find out what is available. Even when you do find a rele‐

vant skill, it can be difficult to remember the commands. This is why most people

tend to go with a single voice interface versus having to deal with remembering mul‐

tiple wake words and commands.

If you are creating a brand-new device with voice capabilities, there are a ton of

things you need to think about to ensure that it offers a pleasant experience. For start‐

ers, the wake word. Do you include a wake word, or will you choose not to? If you

include it, what should it be? Do you build it from scratch or do you leverage an

existing voice framework? These design considerations require significant planning,

whether you’re building a product for market or even just a new voice interface that

you’d like to integrate into your own home or office.

4 | Chapter 1: Introduction to Voice Interfaces and the IoT

If you want to learn more about the user experience design side of voice interfaces,

check out Laura Klein’s “Design for Voice Interfaces: Building Products that Talk”. In

her report she dives into the various dos and don’ts, nuances, and differences between

working with VUI versus GUI and multimodal interfaces. Cathy Pearl’s book, Design‐

ing Voice User Interfaces, is another great guide with tons of examples and expert

advice to help with all of your design considerations.

This book will cover some existing voice frameworks and APIs that can be leveraged

in your own internet-enabled device. We will explore Amazon’s Alexa Skills Kit

(ASK) and Alexa Voice Service (AVS) as well as Microsoft’s native Speech APIs and

its cloud-based solution called Cognitive Services (formerly Project Oxford). Addi‐

tionally, we will quickly touch on a few other voice frameworks, including Web

Speech API, Watson, and Google Speech API.

To gain a high-level understanding of how these APIs operate, we must first grasp the

underlying concepts and technologies that power them. With this in mind let’s

quickly dive into some areas specifically around NLP, STT, TTS, and other acronyms

that make it possible to program voice interfaces into our IoT devices today.

What Is NLP?

Natural language processing (NLP) uses computational linguistics and machine

learning to parse text-based human language (i.e., English, Spanish, etc.) and process

the phrases for specific events. In other words, it can be used to initiate commands,

teach machines to find specific patterns, and so much more. NLP is primarily used in

chatbots (both text-based and voice-enabled versions) as well as other applications

that aid in machine learning, artificial intelligence, and human–computer interfaces

(HCIs) designed for a wide array of use cases.

The biggest challenge facing NLP and AI engineers today is natural language under‐

standing (NLU)—that is, getting the algorithms to process the differences between a

metaphor versus a literal statement. Part of the problem is understanding context,

discourse, relationships, pronoun resolution, word-sense disambiguation (WSD), and

a whole slew of extremely complex computational challenges that are being tackled

each and every day. For instance, in 2016 the Microsoft Research Advanced Speech

Recognition (ASR) algorithms matched and in some cases outperformed their human

counterparts with an impressive error rate of just 5.9%, according to Microsoft’s Chief

Speech Scientist Xuedong Huang. Compare that to everyday humans attempting to

understand one another, with an error rate of 4% to a whopping 12%, depending on

who you ask.

While the concepts behind NLP and NLU are important to comprehend, we will not

delve into the details here; however, you will want to make sure that NLP is a key

component or feature offered by at least one of the service APIs we will be plugging

What Is NLP? | 5

1 Not to be confused with speaker recognition or voice biometrics, which uses biometric signatures to identify

who is speaking versus what they are saying.

into our IoT devices. The two top components that complement NLP and allow us as

humans to speak and listen to machines are speech-to-text (STT) and text-to-speech

(TTS). STT and TTS are crucial components for providing your users with speech

recognition and synthesis. We’ll take a look at each of these in the following sections.

Speech-to-Text (STT)

While NLP can be used with or without actual voice input, when used with voice you

would essentially be bringing in STT to parse audible words to text. While this is a

required feature for converting human spoken language to text, some services might

not have this available. For example, if you want to plug into one of the many chat‐

bots available today, you might need to leverage an STT API on another service or

framework to first translate the spoken words into text-based strings that you can

then post to the chatbot API to get your desired end result.

STT is also known as speech recognition (SR) or advanced speech recognition (ASR).

Nuance offers a great example of an STT/ASR service; it is used by Siri to convert

speech to plain text, which is then processed further by the Siri NLP/NLU layers to

understand the user’s intents.

Google’s Cloud Speech API, which converts an audio stream and recognizes over 80

languages using advanced neural network models, is another excellent example of a

straightforward STT service. The API sends back plain text that you can then route to

one or many chatbots or NLP-based services. While it may be easy to integrate with a

single full-service voice API, one thing you might want to keep in mind is this separa‐

tion of concerns (SoC) in order to maintain flexibility with your devices’ services

integration architecture.

Here’s a partial example of what a nonstreaming audio HTTP POST request to the

Google Cloud Speech API might look like:

{

'config': {

'encoding': 'LINEAR16',

'sampleRate': 16000,

'languageCode': 'en-US'

'audio': {

'content': speech_content.decode('UTF-8') //base64 encoded audio

}

6 | Chapter 1: Introduction to Voice Interfaces and the IoT

2 This example is taken from IVONA Software’s “Signing POST Requests”.

Text-to-Speech (TTS)

TTS is the exact opposite direction of STT. With text-to-speech (TTS), text is synthe‐

sized into artificial human speech. Similar to STT, TTS might not come bundled with

an NLP service or even an STT/ASR service. If you will be using voice for input only

(e.g., to speak to a PC and have it display the results rather than verbally respond),

then TTS is not necessarily required.

If you simply need TTS there are some standalone services out there today that you

can leverage in your projects. For instance, with IVONA (now Amazon Polly), you

can post simple HTTP requests with the text you want synthesized and you will

receive an HTTP response with an audio stream that you can play back to end users.

This is the same TTS service that enables the amazingly smooth voice of Amazon

Alexa.

Here’s a partial example of what an unsigned HTTP POST request might look like:

POST /CreateSpeech HTTP/1.1

Host: tts.eu-west-1.ivonacloud.com

Content-type: application/json

X-Amz-Date: 20130913T092054Z

Content-Length: 32

{"Input":{"Data":"Hello world"}}

As you can see in this example, the phrase “Hello world” has been requested to be

synthesized. The response would include an HTTP stream of the audible “Hello

world” phrase. Since no additional parameters were set, the defaults for encoding,

language, voice, and other variables will be used to synthesize the speech—for exam‐

ple, MP3 encoding in US English and so on. With this in mind, you can get up and

running quickly with the default settings, then evolve the voice output by adjusting

the settings as you continue to develop your voice interface.

PLS, SSML, and Other Acronyms

Aside from NLP, NLU, STT, TTS, ML, and AI, you will hear or read about Pronuncia‐

tion Lexicon Specification (PLS), Speech Synthesis Markup Language (SSML), Speech

Recognition Grammar Specification (SRGS), and a hodgepodge of alphabet soup in

this acronym-filled field. Volumes of literature have been written for each of these

individually, outlining their various technical approaches and high-level methodolo‐

gies. Here we will focus on some examples of SSML, SRGS, and other meta and/or

markup type of specifications as these are also applicable to developers looking to

incorporate existing and flexible voice interfaces into their own IoT devices.

What Is NLP? | 7

In addition to all these wonderful acronyms, we will also learn about related termi‐

nology and paradigms such as utterances, slots, domains, context, agents, entities,

intents, and conversational interfaces.

To get us started we’ll briefly define some of the voice-related terminology you will

encounter throughout the following chapters.

Utterances

An utterance is a spoken word or phrase. In some services you will see many refer‐

ences to the word “utterances”—for instance, in Amazon’s ASK, you are required to

include some “sample utterances” in plain-text form, prefixed with their related

intent, as in Example 1-1.

Example 1-1. Sample utterances in the Amazon Alexa Skills Kit

ToggleLightIntent turn {OnOff} lights

ToggleLightIntent turn lights {OnOff}

OrderPizzaIntent order a {Product}

OrderPizzaIntent order a {Size} {Product}

...

Slots and entities

Slots are the variables and arguments of the NLP world. In Example 1-1, the tokens

shown with curly braces (i.e., {OnOff}, {Product}, and {Size}) are all examples of cus‐

tom slots. In the context of this book, you will specifically read about these Amazon

Alexa slot types such as custom slot types, custom slot values, and built-in slot types.

In some cases, we will reference other services such as Wit.ai or API.AI, which refer

to slot types as “entities.” Entities are essentially the type of variable; for instance, the

Size slot can be a custom slot type that supports any number of string values such as

large, medium, or small. When choosing a service you will want to ensure that you

select one that supports both a robust built-in set of slots or entities but also custom

slots that you design for your own use cases.

Skills

Skills are somewhat similar to apps—in other words, with Amazon Alexa, you don’t

necessarily create apps in the traditional graphical sense; instead you create skills that

add functionality to the Alexa ecosystem. There are a number of built-in skills like

weather, music, and so on. Currently, the Alexa Skills Kit (ASK), the SDK of sorts for

Amazon Alexa, includes various types of skills, including Custom Interaction Model,

Smart Home Skill, Flash Briefing Skill, and List Skill.

8 | Chapter 1: Introduction to Voice Interfaces and the IoT

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编程语音接口：赋予连接设备声音

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