vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Or you can edit them to complement other intents that you create. [Learn more](https://cloud.ibm.com/docs/assistant?topic=assistant-catalog). 
 Context variable A variable that you can use to collect information during a conversation, and reference it later in the same conversation. For example, you might want to ask for the customer's name and then address the person by name later on. A context variable is used by the dialog skill. [Learn more](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-runtime-contextdialog-runtime-context-variables). 
 Dialog The component where you build the conversation that your assistant has with your customers. For each defined intent, you can author the response your assistant should return. [Learn more](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-overview). 
 Digression A feature that gives the user the power to direct the conversation. It prevents customers from getting stuck in a dialog thread; they can switch topics whenever they choose. [Learn more](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-runtimedialog-runtime-digressions). 
 Disambiguation A feature that enables the assistant to ask customers to clarify their meaning when the assistant isn't sure what a user wants to do next. [Learn more](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-runtimedialog-runtime-disambiguation). 
 Entity Information in the user input that is related to the user's purpose. An intent represents the action a user wants to do. An entity represents the object of that action. [Learn more](https://cloud.ibm.com/docs/assistant?topic=assistant-entities). 
 Integrations Ways you can deploy your assistant to existing platforms or social media channels. [Learn more](https://cloud.ibm.com/docs/assistant?topic=assistant-deploy-integration-add).
* [Interim results and low latency](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interim), especially [Requesting interim results and low latency](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interiminterim-low-latency)
* [How the service sends recognition results](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-websocketsws-results)



Defect fix: Improve speakers labels results
: Defect fix: When you use speakers labels with next-generation models, the service now identifies the speaker for all words of the input audio, including very short words that have the same start and end timestamps.





 31 August 2021 

All next-generation models are now generally available
: All existing next-generation language models are now generally available (GA). They are supported for use in production environments and applications.



* For more information about all available next-generation language models, see [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng).
* For more information about the features that are supported for each next-generation model, see [Supported features for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ngmodels-ng-features).



Language model customization for next-generation models is now generally available
: Language model customization is now generally available (GA) for all available next-generation languages and models. Language model customization for next-generation models is supported for use in production environments and applications.

You use the same commands to create, manage, and use custom language models, corpora, and custom words for next-generation models as you do for previous-generation models. But customization for next-generation models works differently from customization for previous-generation models. For custom models that are based on next-generation models:
The Part of Speech enrichment is applied automatically to documents in most project types in v2, but the index fields that are generated by the enrichment are not displayed in the JSON representation of the document.

Zoom

![Difference in entities data structure](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/compare-result-syntax.png)

Figure 1. Entities data structure differences
* Instead of the count and relevance in v1, v2 includes the mentions.

Each entry in the mention corresponds to an occurrence of the entity in the document text. In the following example, seven occurrences are found. For each occurrence, a confidence score and the offsets of the mention text are displayed. You can use the offsets to highlight the mention in the document text when the result is displayed in a user interface.

Zoom

![Mentions in Discovery v2](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/result-detail.png)

Figure 2. Entity mentions in Discovery v2
* The JSON structure of query responses is rearranged slightly in v2.
* Deduplication information is not included in the v2 query response.
* In v2, enriched_text is an array instead of an object.
* In Discovery v2, the Entities v2 enrichment is used. Entity type names in v2 are specified in headline case, instead of all uppercase letters. If you use a query or aggregation that specifies an entity name, you must change the capitalization. For example, change PERSON to Person.
* Fields from JSON files that are added to a collection are converted differently during ingestion between v1 and v2. If your application manipulates these results, you might need to make adjustments.
A list of stopwords is enabled by default (file name: custom_stopwords_{language}.txt) in Discovery, so compare the words in that file to your query expansion file (expansions.json) and adjust accordingly.





 Creating custom tokenization dictionaries 

Tokenization breaks text into units called tokens. A standard tokenization dictionary is applied to your collections, but you can improve the search accuracy for your domain or language by uploading a custom tokenization dictionary. Your custom dictionary appends the standard dictionary. You can upload your dictionary, using the Discovery API.

See the [tokenization API reference](https://cloud.ibm.com/apidocs/discoverycreate-tokenization-dictionary) for the API commands to upload and delete tokenization files.

This feature is currently only available for Japanese collections.

In the example below, text is the phrase that is tokenized, when encountered, and tokens are the words that the text is split into (they must be defined individually). Readings list the version of the tokens represented by a different character set, and part_of_speech is the part of speech the tokens represent.

With this custom dictionary, if you search for this text: ネコ, the search results include text containing すしネコ, as well as text containing only ネコ.

{ "tokenization_rules":
[
{
"text":"すし",
"tokens":
"すし"
],
"readings":
"寿司"
],
"part_of_speech":"カスタム名詞"
},
{
"text":"ネコ",
"tokens":
"ネコ"
],
"readings":
"ネコ"
] ,
"part_of_speech":"カスタム名詞"
}
Show more
With this custom dictionary, you can create rules with a single token. In this example, ibm発見 is tokenized as a single token, so it is not broken up into smaller units. { "tokenization_rules":

{
Getting help 

Get help with solving issues that you encounter while using the product.

Use these resources to get answers to your questions:



*  For answers to frequently asked questions, see the [FAQ](https://cloud.ibm.com/docs/assistant?topic=assistant-faqs).
*  Click the Learning center link that is displayed in the header of the skill pages to find helpful product tours. The tours guide you through the steps to follow to complete a range of tasks, from adding your first intent to a dialog skill to enhancing the conversation in an actions skill. The Additional resources page has links to relevant documentation topics and how-to videos. You can search the resource link titles to find what you're looking for quickly.
*  Find answers to common questions or ask questions where experts and other community members can answer by visiting the [Watson Assistant Community forum](https://community.ibm.com/community/user/watsonapps/communities/community-home/digestviewer?communitykey=7a3dc5ba-3018-452d-9a43-a49dc6819633&tab=digestviewer).



If your service plan covers it, you can get help by creating a case from [IBM Cloud Support](https://cloud.ibm.com/unifiedsupport/supportcenter).
* If you have existing Speech to Text instances, select the instance you want to use from the list.
* If you do not have any existing Speech to Text instances, click Create new instance to create a new Plus or Enterprise instance.



8. In the Choose your Speech to Text language model field, select the language you want to use.

The list of language models is automatically filtered to use the same language as your assistant. To see all language models, toggle the Filter models based on assistant language switch to Off.

If you created specialized custom models that you want your assistant to use, choose the Speech to Text service instance that hosts the custom models now, and you can configure your assistant to use them later. The Speech to Text service instance must be hosted in the same location as your Watson Assistant service instance. For more information, see [Using a custom language model](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-phone-actionsphone-actions-custom-language).

For more information about language models, see [Languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models) in the Speech to Text documentation.

Click Next.
9. On the Text to Speech page, select the instance of the Text to Speech service you want to use.



* If you have existing Text to Speech instances, select the instance you want to use from the list.
* If you do not have any existing Text to Speech instances, click Create new instance to create a new Standard instance.



Click Next.
10. On the Contact center integrations page, click Test connection below the setup information you entered.



* If Invalid, check your credentials and enter each again.
* If the credentials are correct, the Save and exit button becomes clickable.



Click Save and exit.



The connection between your assistant and NICE CXone is complete.



 Configuring the NICE CXone script 

NICE CXone provides a scripting tool that allows workflow developers to define routing flows for their contact centers in CXone.

The following actions and settings in the workflow are necessary for integration to work properly.
The following example returns all of the custom prompts with which the specified speaker_id is associated:

IBM Cloud

curl -X GET -u "apikey:{apikey}" "{url}/v1/speakers/{speaker_id}"

IBM Cloud Pak for Data

curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/speakers/{speaker_id}"

The speaker model is associated with four prompts divided between two custom models:

{
"customizations": [!
{
"customization_id": "9937efc0-5341-b436-77e1-9923f6e693a1",
"prompts":
{
"prompt": "Hello and welcome",
"prompt_id": "greeting",
"status": "available"
},
{
"prompt": "How can I help you today?",
"prompt_id": "help",
"status": "processing"
}
]!
},
{
"customization_id": "82f4809a-bf63-89a6-52ca-22731fe467ba",
"prompts":
{
"prompt": "Thank you and good-bye",
"prompt_id": "goodbye",
"status": "available"
},
{
"prompt": "I do not understand that response.",
"prompt_id": "do_not_understand",
"status": "available"
}
]
}
]
}
Show more







 Deleting a speaker model 

To delete a speaker model, use the DELETE /v1/speakers/{speaker_id} method. Deleting a speaker model does not affect any prompts that are associated with the deleted speaker. The prosodic data that defines the quality of a prompt is established when the prompt is created.
: The dialog feature is available in the new Watson Assistant experience. If you have a dialog-based assistant that was built using the classic Watson Assistant, you can now migrate your dialog skill to the new Watson Assistant experience. For more information, see [Migrating to the new experience](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-migrate-overview).





 25 March 2022 

Improved irrelevance detection for Dutch
: Irrelevance detection for Dutch disregards any punctuation in an input sentence. For example, you can now expect the same confidence score for the following two inputs: ik ben een kleine krijger? and ik ben een kleine krijger. In this example, the question mark (?) doesn't affect the confidence score.

Improved enhanced intent detection
: The exact match in enhanced intent detection now better handles small differences between training examples and runtime utterances when the differences do not change the meaning of a sentence.

For example, suppose in your training examples, covid-19 is in the covid intent and @doctortype_facilitytype around Palm Beach is in the find_provide_master intent. In this example, the @doctortype_facilitytype direct entity reference contains entity values, including hospital. At run time, covid19 is predicted as 100% confident for the covid intent, and hospital around palm beach is predicted as 100% confident for the find_provide_master intent.

This update applies to the following languages: English, French, Spanish, Italian, and the universal language model. For more information, see [Accessing intents](https://cloud.ibm.com/docs/assistant?topic=assistant-expression-languageexpression-language-intent).





 23 March 2022 

Fuzzy matching updates
: Previously, an update was made so that interactions between the stemming and misspelling fuzzy matching features were not allowed. This change applied to the following languages: English, French, German, and Czech. This was updated so that this change applies only to the English language.
Changing the confidence level threshold to 0.5 instructs your assistant to not respond with an answer from the dialog unless the assistant is more than 50% confident that the dialog can understand the user's intent and can address it.



1. From the Dialog page of your dialog skill, make sure that the last node in the dialog tree has an anything_else condition.

Whenever this node is processed, the search skill is triggered.
2. Add a folder to the dialog. Position the folder before the first dialog node that you want to de-emphasize. Add the following condition to the folder:

intents[0].confidence > 0.5

This condition is applied to all of the nodes in the folder. The condition tells your assistant to process the nodes in the folder only if your assistant is at least 50% confident that it knows the user's intent.
3. Move any dialog nodes that you do not want your assistant to process often into the folder.



After changing the dialog, test the assistant to make sure the search skill is triggered as often as you want it to be.

An alternative approach is to teach the dialog about topics to ignore. To do so, you can add utterances that you want the assistant to send to the search skill immediately as test utterances in the dialog skill's "Try it out" pane. You can then select the Mark as irrelevant option within the "Try it out" pane to teach the dialog not to respond to this utterance or others like it. For more information, see [Teaching your assistant about topics to ignore](https://cloud.ibm.com/docs/assistant?topic=assistant-logslogs-mark-irrelevant).





 Disabling search 

You can disable the search skill from being triggered.

You might want to do so temporarily, while you are setting up the integration. Or you might want to only ever trigger a search for specific user queries that you can identify within the dialog, and use a search skill response type to answer.

To prevent the search skill from being triggered, complete the following steps:



1. From the Assistants page, click the menu for your assistant, and then choose Settings.
* Dealing with multiple intents: A user enters input that expresses a wish to complete two separate tasks. I want to open a savings account and apply for a credit card. How does the dialog recognize and address both of them? See the [Compound questions](https://sodoherty.ai/2017/02/06/compound-questions/) entry from Simon O'Doherty's blog for strategies you can try. (Simon is a developer on the Watson Assistant team.)
* Dealing with ambiguous intents: A user enters input that expresses a wish that is ambiguous enough that your assistant finds two or more nodes with intents that could potentially address it. How does the dialog know which dialog branch to follow? If you enable disambiguation, it can show users their options and ask the user to pick the correct one. See [Disambiguation](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-runtimedialog-runtime-disambiguation) for more details.
* Handling multiple entities in input: If you want to evaluate only the value of the first detected instance of an entity type, you can use the syntax @entity == 'specific-value' instead of the @entity:(specific-value) format.

For example, when you use @appliance == 'air conditioner', you are evaluating only the value of the first detected @appliance entity. But, using @appliance:(air conditioner) gets expanded to entity['appliance'].contains('air conditioner'), which matches whenever there is at least one @appliance entity of value 'air conditioner' detected in the user input.
* Hiding data from the log: You can prevent information from being stored in Watson logs by storing it in a context variable and nesting the context variable within the $private section of the message context. For example: $private.my_info. Storing data in the private object hides it from the logs only. The information is still stored in the underlying JSON object. Do not allow this information to be exposed to the client application.
"alternatives":
{
"transcript": "agent ",
"confidence": 0.78
}
]
}
],
"transactionID": "43dd6ce0-139a-4d76-95aa-86e03fcfc434",
"startTimestamp": "2021-09-29T17:43:29.436Z"
},
"is_dtmf": false,
"barge_in_occurred": false
}
}
},
"context": {
"skills": {
"main skill": {
"user_defined": {},
"system": {}
}
},
"integrations": {
"voice_telephony": {
"private": {
"sip_to_uri": "sip:watson-conversation@10.10.10.10",
"sip_from_uri": "sip:10.10.10.11",
"sip_request_uri": "sip:test@10.10.10.10:5064;transport=tcp"
},
"sip_call_id": "QjryZsuAS4",
"assistant_phone_number": "18882346789"
}
}
}
}
Show more
Detecting phrases that express sentiment 

Analyze a document to find phrases that express an opinion or reaction and assess whether the sentiment expressed is positive, neutral, or negative. For English and Japanese, you can also detect specific sentiment targets. The Content Mining application marks these extractions as annotations.

For example, if a product feedback form contains the following sentence, you want to find it and indicate that it is a positive statement.

I love my XYZ blender...

What's the difference between phrase and document sentiment?
: Document sentiment is a built-in Natural Language Processing enrichment that is available for all project types. Document sentiment evaluates the overall sentiment that is expressed in a document to determine whether it is positive, neutral, or negative. Phrase sentiment does the same. However, phrase sentiment can detect and assess multiple opinions in a single document and, in English and Japanese documents, can find specific phrases. For more information about the document sentiment enrichment, see [Sentiment](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-nlunlu-sentiment).

Complete the following steps to enable phrase sentiment analysis:



1. From the analysis view of your collection, click the Collections breadcrumb link in the page header.
2. In the tile for your collection, click the open and close list of options icon, and then choose Edit collection.
3. Click the Enrichment tab, and then select the Sentiment of phrases annotator.
4. Click Save, and then click OK to verify the change.

The collection is reindexed. Wait for processing to be completed.
5. Click Close to return to the Collections page, and then click your collection tile.
6. In the What do you want to analyze? field, enter a term to search for in your documents or select one or more facets, and then click Search to filter the documents.

The search results are displayed in the mining graph. The Facet analysis pane is displayed also. By default, Relevancy analysis is shown.
7. In the drop-down menu from the Facet analysis pane, select Sentiment.
8.
To request a custom voice or for more information, complete and submit this [IBM Request Form](https://form.asana.com/?k=CBuXK1uwlAf5ek6FwB6mcg&d=8612789739828).





 How do I use the Tune by Example feature? 

Tune by Example lets you control exactly how specified text is spoken by the service. You provide text and spoken audio to add a custom prompt to a custom model. The spoken audio can stress different syllables or words, introduce pauses, and generally make the synthesized audio sound more natural and appropriate for its context. When you synthesize the prompt, the service duplicates the qualities of the recorded speech with its voices.

You can further enhance the quality of a prompt by creating an optional speaker model that contains a sample of a speaker's voice. The service leverages the sample audio to train itself on the voice, which can help it produce higher-quality prompts for that speaker.

For more information, see [Understanding Tune by Example](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-intro).





 What limits exist for a custom model? 

The following limits apply to all custom models:



* A word in a custom entry can contain a maximum of 49 characters.
* A translation in a custom entry can contain a maximum of 499 characters.
* A custom model can include a maximum of 20,000 custom entries.
* A custom model can include a maximum of 1000 custom prompts.



For more information, see [Rules for creating custom entries](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-rules).





 Where can I find plans and pricing information? 

IBM Cloud

The Text to Speech service offers multiple pricing plans. For more information about pricing, see the Text to Speech service in the [IBM Cloud Catalog](https://cloud.ibm.com/catalog/text-to-speech).
* [Watson GitHub repos](https://github.com/watson-developer-cloud/)



* Help



* [Known limitations](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-known-limitations)
* [Usage FAQs](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-faq-usage)
* Developer community



* [StackOverflow](http://stackoverflow.com/questions/tagged/ibm-watson-cognitive+text-to-speech)



* [Accessibility](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-accessibility-platform)



* [About this product](https://www.ibm.com/cloud/watson-text-to-speech)



Expand all | Collapse all

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1. [IBM Cloud Docs](https://cloud.ibm.com/docs)
2. [Text to Speech](https://cloud.ibm.com/docs/text-to-speech)
3.



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 The HTTP interface * Last updated 2023-01-24 

To synthesize text to speech with the HTTP REST interface of the IBM Watson® Text to Speech service, you call the GET or POST /v1/synthesize method. You specify the text that is to be synthesized and the voice and format for the spoken audio. You can also specify a custom model that is to be used with the request.

For more information about the HTTP interface, see the [API & SDK reference](https://cloud.ibm.com/apidocs/text-to-speech).



 Synthesizing text to audio