Google - STT & DialogFlow
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Google TTS services are used for Converting text to speech just like Amaxon Poly. But google TTS supports a larger number of languages and has a better speech quality.
You will be able to use all the languages offered by
For Google Cloud Services account, you'll have more options! Such as, you can use Standard or Wavenet voices and change pitch and speed easily.
DialogFlow is a natural understanding language used to create bots, interactive voice response etc. In order to use DialogFlow, in your central account you need to configure certain parameters in DialogFlow.
Let's say you need to achieve the following Use Case:
There is an agent bot ‘Eva’ which when called, gives you 3 options and asks you to choose one
Option 1 - To get Payment status
Option 2 - To get Account status
Option 3 - To get Loan status
If the user selects option 1 then he will be able to get the payment status, if the user says option 2 then he will be able to get account status and if the user selects option 3 then he will get status regarding loan. To do so you can configure dialogFlow as follows:
Entity: Each intent parameter has a type, called the entity type, which dictates exactly how data from an end-user expression is extracted.
Dialogflow provides predefined system entities that can match many common types of data. There are system entities for matching dates, times, colors, email addresses, and so on. You can also create your own custom entities for matching custom data. For the above example , you could define a "Options entity" that can match the types of options available (like option1, option2, option3 etc).
3. Create New Entity and give entity a name
In order to create new entity you need to be aware of following terms-
Entity type: Defines the type of information you want to extract from user input. For example, OPTIONS could be the name of an entity type in our case as we want the user to select an ‘option’. Click on ‘Create Entity’ to create an entity type.
Entity entry: For each entity type, there are many entity entries. Each entity entry provides a set of words or phrases that are considered equivalent. For example - If ‘options’ is an entity type, you could define these entity entries like: Option one Option two Option three etc
Entity reference value and synonyms: Some entity entries have multiple words or phrases that are considered equivalent, like ‘option one’ and ‘first option’. For these entity entries, you provide one reference value and one or more synonyms.For Example : Reference value can be: Option 1 and Synonyms can be:Option one, Option 1, First option, 1 option etc
4. After adding the details, SAVE the Entity
If you want to use this entities in your flow then you can directly fetch entity (in speech processing widget) of particular intent and store the Entity in any variable to further use in the flow as shown below.
An intent categorizes an end-user's intention for one conversation turn. For each agent, you define many intents, where your combined intents can handle a complete conversation. When an end-user writes or says something, referred to as an end-user expression, Dialogflow matches the end-user expression to the best intent in your agent. Matching an intent is also known as intent classification.
6. Create a new intent and give intent a name.
7. Add training phrases in it
Training phrases: These are example phrases for what end-users might say. When an end-user expression resembles one of these phrases, Dialogflow matches the intent. You don't have to define every possible example, because Dialogflow's built-in machine learning expands on your list with other, similar phrases.
For Example- If we want user to select ‘option1’, we can create an intent name ‘option1’ and can add some training phrases which the end user might say like "I choose option one", "I want first option", "I prefer option 1" etc.
8. Now add the responses of the intent
Responses: Responses can be text, speech, or visual responses to return to the end-user. These may provide the end-user with answers, ask the end-user for more information, or terminate the conversation.
For example- Suppose if the end user selects option 1 and we need to provide the payment status then we add response as shown below.
So whenever the user gives input from the training phrases of this intent, one of the response will be given back to the user. Response will be picked randomly each time giving a dynamic conversational experience.
9. Now Save the intent
10. To use this responses in your flow you can configure flow as follows
First configure NLP Engine account in your central account.
Create any inbound or outbound flow as per need, for the above example the flow can be like -
3. Enable ‘Fetch intent’ and then ‘fetch response’ in speech processing widget.
4. Use this response variable further in any widgets like in play widget to give the user voice response.
1.Open
2.Go to Page
5. Now go to page
