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You should
add more
Speech API
into your Bot
@GosiaBorzecka
About me
@GosiaBorzecka
Agenda
Create your bot..
And add more intelligent!
With Speech API?
Azure Speech API (Cognitive Services)
Summary
@GosiaBorzecka
Your bot
Your bot
 Answer common questions
 Making appointment in your system
 Automate helpdesk
 Help with interview process
 Finding events near you
 Showing latest updates about your company/organisation
 Helping plan your day/meeting
 Finding people that works on similar project
@GosiaBorzecka
The basic (and most
popular) bot
QnA
@GosiaBorzecka
Add some intelligent
But which one?
Microsoft (Cognitive Services)
Google (Cloud)
AWS
IBM Watson
 Mozilla
 Speechmatics
 Neospeech
 iSpeech
 ….
@GosiaBorzecka
But which one?
Microsoft (Cognitive Services)
Google (Cloud)
AWS
IBM Watson
Speech
Natural Language
Bot
Custom Model
@GosiaBorzecka
Intelligent API providers
Microsoft (Cognitive
Google (Cloud)
AWS
IBM Watson
Speech
 Speech to Text
 Text to Speech
 Speech translation
Natural Language
 LUIS
Bot
 Bot Framework
Custom Model
 LUIS
@GosiaBorzecka
Intelligent API providers
Microsoft (Cognitive Services)
Google (Cloud)
AWS
IBM Watson
Speech
 Speech-to-Text
 Text-to-Speech
 Translation
Natural Language
 Google NLP
Bot
 Dialogflow
Custom Model
 Auto ML
@GosiaBorzecka
Intelligent API providers
Microsoft (Cognitive Services)
Google (Cloud)
AWS
IBM Watson
Speech
 Amazon Polly (Text-to-
Speech)
 Amazon Transcribe
(Speech-to-Text)
 Amazon Translate
Natural Language
 Amazon Comprehend
Bot
 Amazon Lex
Custom Model
 Sage Maker
@GosiaBorzecka
Intelligent API providers
Microsoft (Cognitive Services)
Google (Cloud)
AWS
IBM Watson
Speech
 Speech to Text
 Text to Speech
 Language Translator
Natural Language
 Natural Language
Understanding
Bot
 Watson Assistant
Custom Model
 Knowledge Studio
@GosiaBorzecka
Which one choose?
@GosiaBorzecka
In this case…
@GosiaBorzecka
Bot Framework
@GosiaBorzecka
@GosiaBorzecka
Bot Framework
@GosiaBorzecka
Bot Framework
@GosiaBorzecka
Bot Framework
@GosiaBorzecka
QnA Maker
@GosiaBorzecka
QnA
Maker
@GosiaBorzecka
QnA
Maker
@GosiaBorzecka
Call QnA Maker
@GosiaBorzecka
LUIS
@GosiaBorzecka
LUIS
@GosiaBorzecka
LUIS
@GosiaBorzecka
Natural Language Processing
Intents
 None
 List
 Help
 Confirmation
 Purchase
 Weather
Entities
 Address
 Age
 Location
 Category
 Url
 Time
@GosiaBorzecka
Call LUIS
@GosiaBorzecka
We have a bot with some
intelligent…
LET IT SPEAK!
@GosiaBorzecka
Cognitive
Services:
Speech
Speech-to-Text
Text to Speech
Speaker Recognition (Preview)
Speech translation
@GosiaBorzecka
Speech APIs (30 days)
@GosiaBorzecka
Pricing (Free)
 Speech translation (5h free per month)
 Speech-to-Text (5h free per month)
 Speech to Text with Custom Speech Model (5h
free per month)
 Speech endpoint hosting (1 model free per
month)
 Text to Speech with Standard Voices (5M
characters free per month)
 Text to Speech with Custom Voice Font (5M
characters free per month)
 Text to Speech with Neural Voices (0.5M
characters free per month)
@GosiaBorzecka
Pricing
(Standard)
 Speech translation (£1.87 per hour)
 Speech-to-Text (£0.746 hour)
 Speech to Text with Custom Speech Model
(£1.044 per hour)
 Speech endpoint hosting
(£29.82/model/month)
 Text to Speech with Standard Voices (£2.982
per 1M chars)
 Text to Speech with Custom Voice (£4.472 per
1M chars)
 Text to Speech with Neural Voices (£11.925 per
1M chars)
 Custom Voice Font hosting
(£29.82/model/month)
@GosiaBorzecka
SDKs
C/C++
C#
Java
JavaScript/NodeJS
Objective-C
Python
@GosiaBorzecka
Speech-to-Text
@GosiaBorzecka
Speech-To-Text
 Transcribes continuous real-time speech into text
 Can batch transcribe speech from audio recording
 Supports intermediate results, end-of-speech detection, automatic text
formatting, and profanity masking
 Can call on Language Understanding (LUIS) to deliver user intent from transcribed
speech
@GosiaBorzecka
Speech-To-Text: Custom
Text-to-Speech
@GosiaBorzecka
Text to Speech (TTS)
Synthesis into human-sounding speech Return as audio file
@GosiaBorzecka
Neural Voices
GuyNeural (en-US, male)
JessaNeural (en-US, female)
XiaoxiaoNeural (zh-CN, female) – only Southen Asia
@GosiaBorzecka
Standard voices (English UK)
Susan-Apollo (female)
HazelRUS (female)
George-Apollo (male)
https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/language-support#standard-voices
@GosiaBorzecka
Standard voices: other
English
(Ireland)
English
(Australia)
English
(Canada)
English
(India)
English (US)
Spanish
(Spain)
Spanish
(Mexico)
French
(France)
French
(Canada)
French
(Switzerland)
Text to Speech (SDK, C#)
@GosiaBorzecka
Text to
Speech (SDK,
C#)
Speech translation
@GosiaBorzecka
Speech translation
 End-to-end, real time,
multilanguage transaction
 Can be used to:
 Speech to speech
 Speech to text
 Technology:
 SMT (Statistical Machine
Translation)
 NMT (Neural Machine
Translation)
@GosiaBorzecka
Neural machine translation
https://www.microsoft.com/en-us/translator/business/machine-translation/ @GosiaBorzecka
How does speech translation work?
https://www.microsoft.com/en-us/translator/business/machine-translation/ @GosiaBorzecka
Speaker Recognition
(Preview)
@GosiaBorzecka
Speaker Recognition (Preview)
Speaker Verification
Speaker Identification
@GosiaBorzecka
Speaker Recognition (Preview)
SDK
• Android (Java)
• Windows (C#)
@GosiaBorzecka
Speaker Recognition (Preview)
 Audio format requirements:
Container WAV
Encoding PCM
Rate 16K
Sample Format 16 bit
Channels Mono
@GosiaBorzecka
Create Profile
@GosiaBorzecka
Supported locale
es-ES (Castilian Spanish)
en-US (American English)
fr-FR (Standard French)
zh-CN (Mandarin Chinese)
@GosiaBorzecka
Let’s add Speech to the bot!
@GosiaBorzecka
Configure bot
@GosiaBorzecka
Bot configuration: default.htm
@GosiaBorzecka
Bot configuration: default.htm
@GosiaBorzecka
Bot configuration: EchoBot.cs
@GosiaBorzecka
Speech priming
 Improve speech recognition accuracy for
important words
 Only U.S. regional LUIS apps
 Applies to:
 Cortana Channels
 Web Channels
@GosiaBorzecka
@GosiaBorzecka
Ok, ok.. But why add Speech
API?
@GosiaBorzecka
What most popular chatbots (with Speech
API) do you know?
Siri Google Now Alexa
Cortana Facebook
@GosiaBorzecka
Use Speech API for:
DIFFICULT WITH
SEEING
DIFFICULT WITH
TYPING
SAVE TIME WITH
TYPING
AUTOMATIC
TRANSLATION
DETECT EMOTIONS
@GosiaBorzecka
Who using Speech API?
This Photo by Unknown Author is licensed under CC BY-SA
Not bot but..
Are you going to use Speech API with your bot?
Thank you
Dziękuję
谢谢
Dankie
धन्यवाद
‫תודה‬
Mesi
Ευχαριστούμε
Questions?
Je vous remercie
Kiitos
Salamat sa iyo
Vinaka vakalevu
Aitäh
Dank u
Tak
Děkuju
Hvala ti
謝謝
Gràcies
多謝
Благодаря
Hvala
ধন্যবাদ
‫شكرا‬ačiū
paldies
감사합니다 Asante
ありがとう
Grazie
Terima kasih
köszönöm
Þakka þér
ua tsaug
Спасибо
vă mulţumesc
Jamädi
Obrigado
‫متشکرم‬
takk
grazzi terima kasih
Misaotra anaoGracias
ďakujem
Hvala ti
Хвала ти
Faafetai
Tack
நன்றி
Mauruuru ia oe
ขอบคุณ
ధన్యవాదాలుFakamalo atu 'i ho'o
‫شکريا‬
Дякую
Teşekkür ederiz
Níib óolal
Diolch Cảm ơn bạn
@GosiaBorzecka

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Add more Speech API to your bot

Editor's Notes

  1. Plan As with any type of software, having a thorough understanding of the goals, processes and user needs is important to the process of creating a successful bot. Before writing code, review the bot design guidelines for best practices and identify the needs for your bot. You can create a simple bot or include more sophisticated capabilities such as speech, natural language understanding,and question answering. Build Your bot is a web service that implements a conversational interface and communicates with the Bot Framework Service to send and receive messages and events. Bot Framework Service is one of the components of the Azure Bot Service. You can create bots in any number of environments and languages. You can start your bot development in the Azure portal, or use [C# | JavaScript] templates for local development. As part of the Azure Bot Service, we offer additional components you can use to extend your bot's functionality FeatureDescriptionLinkAdd natural language processingEnable your bot to understand natural language, understand spelling errors, use speech, and recognize the user's intentHow to use LUISAnswer questionsAdd a knowledge base to answer questions users ask in a more natural, conversational wayHow to use QnA MakerManage multiple modelsIf using more than one model, such as for LUIS and QnA Maker, intelligently determine when to use which one during your bot's conversationDispatch toolAdd cards and buttonsEnhance the user experience with media other than text, such as graphics, menus, and cardsHow to add cards  Note The table above is not a comprehensive list. Explore the articles on the left, starting with sending messages, for more bot functionality. Additionally, we provide command line tools to help you to create, manage, and test bot assets. These tools can manage a bot configuration file, configure LUIS apps, build a QnA knowledge base, mock a conversation, and more. You can find more details in the command line tools readme. You also have access to a variety of samples that showcase many of the capabilities available through the SDK. These are great for developers looking for a more feature rich starting point. Test Bots are complex apps, with a lot of different parts working together. Like any other complex app, this can lead to some interesting bugs or cause your bot to behave differently than expected. Before publishing, test your bot. We provide several ways to test bots before they are released for use: Test your bot locally with the emulator. The Bot Framework Emulator is a stand-alone app that not only provides a chat interface, but also debugging and interrogation tools to help understand how and why your bot does what it does. The emulator can be run on a locally alongside your in-development bot application. Test your bot on the web. Once configured through the Azure portal your bot can also be reached through a web chat interface. The web chat interface is a great way to grant access to your bot to testers and other people who do not have direct access to the bot's running code. Publish When you are ready for your bot to be available on the web, publish your bot to Azure or to your own web service or data center. Having an address on the public internet is the first step to your bot coming to life on your site, or inside chat channels. Connect          Connect your bot to channels such as Facebook, Messenger, Kik, Skype, Slack, Microsoft Teams, Telegram, text/SMS, Twilio, Cortana, and Skype. Bot Framework does most of the work necessary to send and receive messages from all of these different platforms - your bot application receives a unified, normalized stream of messages regardless of the number and type of channels it is connected to. For information on adding channels, see channels topic. Evaluate Use the data collected in Azure portal to identify opportunities to improve the capabilities and performance of your bot. You can get service-level and instrumentation data like traffic, latency, and integrations. Analytics also provides conversation-level reporting on user, message, and channel data. For more information, see how to gather analytics.
  2. The steps neural network translations go through are the following: Each word, or more specifically the 500-dimension vector representing it, goes through a first layer of “neurons” that will encode it in a 1000-dimension vector (b) representing the word within the context of the other words in the sentence. Once all words have been encoded one time into these 1000-dimension vectors, the process is repeated several times, each layer allowing better fine-tuning of this 1000-dimension representation of the word within the context of the full sentence (contrary to SMT technology that can only take into consideration a 3 to 5 words window) The final output matrix is then used by the attention layer (i.e. a software algorithm) that will use both this final output matrix and the output of previously translated words to define which word, from the source sentence, should be translated next. It will also use these calculations to potentially drop unnecessary words in the target language. The decoder (translation) layer, translates the selected word (or more specifically the 1000-dimension vector representing this word within the context of the full sentence) in its most appropriate target language equivalent. The output of this last layer (c) is then fed back into the attention layer to calculate which next word from the source sentence should be translated.
  3. Identification – who is speaking in the group of people Verification – verify and authenticate user using their voice or speech