vania-janet/multiturn-rag-retrieval-data huggingface dataset:
This setting is not reflected in the Try it out panel.

![Bidi options](https://cloud.ibm.com/docs-content/v1/content/a2b18c153837bce00a5b4de496e4b4ca64bbf56e/assistant-data/images/bidi-options.png)
3. Click the X to close the page. Your changes are saved automatically.







 Working with accented characters 

In a conversational setting, users might or might not use accents while interacting with the Watson Assistant service. As such, both accented and non-accented versions of words might be treated the same for intent detection and entity recognition.

However for some languages, like Spanish, some accents can alter the meaning of the entity. Thus, for entity detection, although the original entity might implicitly have an accent, your assistant can also match the non-accented version of the same entity, but with a slightly lower confidence score.

For example, for the word "barrió", which has an accent and corresponds to the past tense of the verb "barrer" (to sweep), your assistant can also match the word "barrio" (neighborhood), but with a slightly lower confidence.

The system will provide the highest confidence scores in entities with exact matches. For example, barrio will not be detected if barrió is in the training set; and barrió will not be detected if barrio is in the training set.

You are expected to train the system with the proper characters and accents. For example, if you are expecting barrió as a response, then you should put barrió into the training set.

Although not an accent mark, the same applies to words using, for example, the Spanish letter ñ vs. the letter n, such as "uña" vs. "una". In this case the letter ñ is not simply an n with an accent; it is a unique, Spanish-specific letter.

You can enable fuzzy matching if you think your customers will not use the appropriate accents, or misspell words (including, for example, putting a n instead of a ñ), or you can explicitly include them in the training examples.
In combination with words from its base vocabulary, the service uses these words from the custom model to predict and transcribe speech from audio. You provide the information for a custom language model in the form of corpora, custom words, and grammars. The service stores this information in the words resource for the custom model.
* When you create a custom language model that is based on a next-generation model, the services relies on sequences of characters from the custom model to create transcripts that reflect domain-specific terms. In combination with character sequences from the base model, the service uses these series of characters from the custom model to predict and transcribe speech from audio.

You provide the information for a custom language model in the form of corpora, custom words, and grammars. But rather than rely on a words resource that contains these words, the service extracts and stores character sequences from the corpora and custom words. The service does not extract and calculate out-of-vocabulary (OOV) words from corpora and custom words. The words resource is simply where it stores custom words that you add directly to the model.



When you develop a custom language model based on a next-generation model, you must still provide corpora and custom words to train the model on domain-specific terminology. So the process of creating and training a custom model is largely the same for next-generation and previous-generation models.

The following topics describe the rules for providing corpora and custom words for a custom language model that is based on a next-generation model. The rules are similar to those for working with a custom model based on a previous-generation model, but some important differences do exist.



 The words resource 

The words resource includes custom words that you add directly to the custom model. The words resource contains the following information about each custom word:



* word - The spelling of the word as added by you.

Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, question marks, etc. in the name. The service does not prevent the use of these characters, but because they must be URL-encoded wherever they are used, it is strongly discouraged.
* Korean: ko-KR_HyunjunVoice, ko-KR_SiWooVoice, ko-KR_YoungmiVoice, and ko-KR_YunaVoice
* Swedish: sv-SE_IngridVoice



The deprecated neural voices continue to be available to existing users but are no longer available to new users:



* Existing users: Instances of the service created before 31 March 2022 can continue to use the deprecated voices for speech synthesis. The instances can also be used create and work with custom models that are based on the deprecated voices. And they can continue to list and query the voices with the GET /v1/voices and GET /v1/voices/{voice} methods.
* New users: Instances of the service created on or after 31 March 2022 cannot use the deprecated voices for speech synthesis. The instances also cannot be used create custom models that are based on the deprecated voices. And they cannot list or query the voices with the GET /v1/voices and GET /v1/voices/{voice} methods. Any API call that includes one of the deprecated voices returns an HTTP error code of 400 or 404, depending on the call.



New voices for Australian English, Dutch, and Korean will be released by 15 February 2023. If you are using Australian English, Dutch, or Korean voices, your API calls will automatically redirect to the new voices in that language. Voices in Arabic, Chinese, Czech, Swedish, and Flemish will be removed from service.

For more information about all available languages and voices, see [Languages and voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-voices).

Deprecated standard voices are removed from the documentation
: The standard concatenative voices were deprecated on [2 December 2020](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-release-notestext-to-speech-2december2020). These standard voices have now been removed from the API reference.
You can also define an English entity value/synonym, and fuzzy matching will match only your defined entity value/synonym. For example, fuzzy matching may match the term unsure with insurance; but if you have unsure defined as a value/synonym for an entity like @option, then unsure will always be matched to @option, and not to insurance.

Your fuzzy matching setting has no impact on synonym recommendations. Even if fuzzy matching is enabled, synonyms are suggested for the exact value you specify only, not the value and slight variations of the value.

To understand how fuzzy matching and autocorrection are related to one another, see the [autocorrection documentation](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-runtime-spell-checkdialog-runtime-spell-check-vs-fuzzy-matching).







 Adding contextual entities 

If you are using version 1.3 of the product, see [Adding contextual entities in v1.3](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-entitiesentities-create-annotation-based-v130) instead. The way you annotate entity mentions changed between releases.

Annotation-based entites are those for which you annotate occurrences of the entity in sample sentences to teach your assistant about the context in which the entity is typically used.

In order to train a contextual entity model, you can take advantage of your intent examples, which provide readily-available sentences to annotate.

This feature is generally available in English-language dialog skills and is available as a beta feature in French-langage dialog skills. For more information about language support, see [Supported languages](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-language-support).

Using an intent's user examples to define contextual entities does not affect the classification of that intent. However, entity mentions that you label are also added to that entity as synonyms. And intent classification does use synonym mentions in intent user examples to establish a weak reference between an intent and an entity.



1.
Relation type systems 

The relation type system that Natural Language Understanding uses differs depending on which language you are using. This page describes which relation type system is used for each language.

For example, analyzing relations in German text will use the [Version 2 relation type system](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-relation-types-version-2).



 Language    Relation type system                        

 Arabic      [Version 1](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-relation-types-version-1)                               
 English     [Version 1](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-relation-types-version-1)                               
 French      [Version 2](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-relation-types-version-2) 
 German      [Version 2](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-relation-types-version-2) 
 Italian     [Version 2](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-relation-types-version-2) 
 Japanese    [Version 2](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-relation-types-version-2) 
 Korean      [Version 1](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-relation-types-version-1)                               
 Portuguese  [Version 2](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-relation-types-version-2) 
 Spanish     [Version 1](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-relation-types-version-1)
Understanding customization 

When you synthesize text with IBM Watson® Text to Speech, the service applies language-dependent pronunciation rules. The service applies the rules to convert the ordinary (orthographic) spelling of each word to a phonetic spelling. A word's phonetic spelling uses phoneme symbols to define how the word is pronounced. These symbols are the distinct units of sound that distinguish words in a language, the boundaries between syllables, and the stress marks for the syllables.

The service's regular pronunciation rules work well for common words. However, they can yield imperfect results for unusual words. Such words include domain-specific terms, words with foreign origins, personal or geographic names, and abbreviations or acronyms. If your application's lexicon includes such words, you can use the customization interface to specify how the service pronounces the words.

A custom model can also include custom prompts that you define for the Tune by Example feature. For more information about Tune by Example, custom prompts, and speaker models, see [Understanding Tune by Example](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-intro).



 Status and support 

The following status and support information applies to customization:



* Customization is available for all languages.
IBM Cloud

You must have the Standard or Premium pricing plan to use customization. Users of the Lite plan cannot use the customization interface. For more information, see the Text to Speech service in the [IBM Cloud® Catalog](https://cloud.ibm.com/catalog/text-to-speech).
IBM Cloud

Premium customers can work with IBM to train a new custom voice for their specific application needs. 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 customization works 

The customization interface of the Text to Speech service creates a dictionary of words and their translations for a specific language. This dictionary is referred to as a custom model.
The Availability column indicates whether each voice is generally available (GA) for production use or beta. The column also indicates whether each voice is available for

IBM Cloud

,

IBM Cloud Pak for Data

, or both (no product version is cited).



* Expressive neural voices support additional features that are not available with other types of voices. These features include additional speaking styles, automatic emphasis of interjections, and emphasis of specified words. For more information, see [Modifying speech synthesis with expressive neural voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-synthesis-expressive).
* When used with the SSML <prosody> element, expressive voices support only percentage values for the rate and pitch attributes. For more information, see [The <prosody> element](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-elementsprosody_element).



Expressive neural voices determine sentiment from context and automatically use the proper intonation to suit the text. To produce the most natural-sounding prosody, expressive neural voices need to consider the context of all words and phrases of a sentence. Expressive voices are therefore more compute-intensive and have slightly higher latency than other types of voices. The initial response for a synthesis request that uses an expressive voice might take a fraction of a second longer (for example, a few hundred milliseconds) to arrive. The total response time for the request to complete is also longer.

To minimize the latency and response time for an expressive voice, use shorter sentences wherever possible.



Table 2. Expressive neural languages and voices

 Language Availability Voice / Gender Audio sample 

 English <br>(Australian) GA en-AU_HeidiExpressive <br>Female Your browser does not support the audio tag. 
 GA en-AU_JackExpressive <br>Male Your browser does not support the audio tag. 
 English <br>(United States) GA en-US_AllisonExpressive <br>Female Your browser does not support the audio tag. 
 GA en-US_EmmaExpressive <br>Female Your browser does not support the audio tag.
* The GET /v1/customizations/{customization_id}/words/{word_name} method lists information about a single specified word from the model's words resource.



In addition to a word field that identifies the word, both methods return the following information about each word:



* A sounds_like field that presents an array of as many as five pronunciations for the word. The array of sounds-like pronunciations can include a sounds-like value that is automatically generated by the service if no sounds-like value is provided when the word is added to the custom model. For more information, see



* For custom models that are based on previous-generation models,[Using the sounds_like field](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWordssoundsLike)
* For custom models that are based on next-generation models,[Using the sounds_like field](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords-ngsounds-like-ng)



* A display_as field that shows the spelling of the custom word that the service displays in transcriptions. The field contains an empty string if no display-as value is provided for the word, in which case the word is displayed as it is spelled. For more information, see



* For custom models that are based on previous-generation models, [Using the display_as field](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWordsdisplayAs)
* For custom models that are based on next-generation models, [Using the display_as field](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords-ngdisplayAs-ng)



* A source field that indicates how the word was added to the custom model's words resource.



* For custom models that are based on previous-generation models, the field includes the name of each corpus and grammar from which the service extracted the word.
"text": ""What have I learned?" he asked. "Everything!""
}
* SSML input:

{
"text": "
"What have I learned?" he asked.
"<prodody rate="50">Everything!</prosody>"
"
}







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On this page[Synthesizing text to audio](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-usingHTTPsynthesize)[Specifying input text](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-usingHTTPinput)[Punctuating input text](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-usingHTTPinput-punctuation)[Specifying SSML input](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-usingHTTPssml-http)[Escaping XML control characters](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-usingHTTPescape)[Examples of input text](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-usingHTTPhttpExamples)


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curl -X PUT -u "apikey:{apikey}" --header "Content-Type: application/json" --data "{"translation":"ニューヨーク", "part_of_speech":"Mesi"}" "{url}/v1/customizations/{customization_id}/words/%EF%BC%AE%EF%BC%B9"

IBM Cloud Pak for Data

curl -X PUT --header "Authorization: Bearer {token}" --header "Content-Type: application/json" --data "{"translation":"ニューヨーク", "part_of_speech":"Mesi"}" "{url}/v1/customizations/{customization_id}/words/%EF%BC%AE%EF%BC%B9"
* Phonetic IPA:

IBM Cloud

curl -X PUT -u "apikey:{apikey}" --header "Content-Type: application/json" --data "{"translation":"<phoneme alphabet=\"ipa\" ph=\"ɲɯːjoːkɯ\"></phoneme>", "part_of_speech":"Mesi"}" "{url}/v1/customizations/{customization_id}/words/%EF%BC%AE%EF%BC%B9"

IBM Cloud Pak for Data

curl -X PUT --header "Authorization: Bearer {token}" --header "Content-Type: application/json" --data "{"translation":"<phoneme alphabet=\"ipa\" ph=\"ɲɯːjoːkɯ\"></phoneme>", "part_of_speech":"Mesi"}" "{url}/v1/customizations/{customization_id}/words/%EF%BC%AE%EF%BC%B9"
* Phonetic IBM SPR:

IBM Cloud
Also, you can check the status of all of your models with the [List models](https://cloud.ibm.com/apidocs/language-translator/language-translatorlistmodels) method.

The status response attribute describes the state of the model in the training process:



* uploading
* uploaded
* dispatching
* queued
* training
* trained
* publishing
* available



When the model status is available, your model is ready to use with your service instance. If your model is deleted, or if it encounters an error in the training process, you might see one of the following statuses:



* deleted
* error





 Example request 

The following example gets information for the model identified by the model ID 96221b69-8e46-42e4-a3c1-808e17c787ca.

curl -X GET --user "apikey:{apikey}" "{url}/v3/models/96221b69-8e46-42e4-a3c1-808e17c787ca?version=2018-05-01"







 Step 4: Translate text with your custom model 

To use your custom model, specify the text that you want to translate and the custom model's model ID in the [Translate](https://cloud.ibm.com/apidocs/language-translator/language-translatortranslate) method.



 Example request 

The following example translates text with the custom model identified by the model ID 96221b69-8e46-42e4-a3c1-808e17c787ca.

curl -X POST --user "apikey:{apikey}" --header "Content-Type: application/json" --data "{"text":"Hello","model_id":"96221b69-8e46-42e4-a3c1-808e17c787ca"}" "{url}/v3/translate?version=2018-05-01"
: Fixed a bug for English and Korean models using Version 2 Entity type system where not all entities are returned properly.





 3 November 2022 

Improved Error Handling and Validation for Sentiment and Custom Sentiment
: If a request contains both an error in the sentiment feature and a valid feature request for another feature, the response returns a 200 with a warning for the sentiment feature.

Requests for Custom Sentiment that include at least one target found in the document will now return a 200 with sentiment analysis for the found targets.

The error log has changed from sentiment request must specify at least one of: document, targets to sentiment request must specify at least one of: document or targets.





 1 September 2022 

Improved Categories and Custom Categories (beta) Models
: The model for [Categories](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-categories-hierarchy) and [Custom Categories (beta)](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-categories) now supports more contemporary vocabulary. For example, newer words such as "Covid-19" can be interpreted.





 10 August 2022 

Entities support for additional languages
: Support for entities is now available, for all public and premium service instances, for the following languages: Czech, Danish, Finnish, Hebrew, Hindi, Norwegian Bokmål, Norwegian Nynorsk, Polish, Romanian, Slovak, Turkish. For details, see [Language support](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-language-support).

Version 2 entity support for Russian and Swedish
: Version 2 entity type support is now available, for all public and premium service instances, for the following languages: Russian and Swedish. For details, see [Entity type systems](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-entity-type-systems).





 3 August 2022 

Support for Single Label Classifications
: Classifications now allows users to pass in a model_type training parameter when creating or updating a model, in order to train a single label classifier.
English <br>(Australian) GA en-AU_HeidiExpressive <br>Female Your browser does not support the audio tag. 
 GA en-AU_JackExpressive <br>Male Your browser does not support the audio tag. 
 English <br>(United States) GA en-US_AllisonExpressive <br>Female Your browser does not support the audio tag. 
 GA en-US_EmmaExpressive <br>Female Your browser does not support the audio tag. 
 GA en-US_LisaExpressive <br>Female Your browser does not support the audio tag. 
 GA en-US_MichaelExpressive <br>Male Your browser does not support the audio tag. 







 Enhanced neural voices 

Table 3 lists and provides audio samples for all available enhanced neural voices. The Availability column indicates whether each voice is generally available (GA) for production use or beta. The column also indicates whether each voice is available for

IBM Cloud

,

IBM Cloud Pak for Data

, or both (no product version is cited).



Table 3. Enhanced neural languages and voices

 Language Availability Voice / Gender Audio sample 

 Dutch <br>(Netherlands) Beta nl-NL_MerelV3Voice <br>Female Your browser does not support the audio tag. 
 English <br>(United Kingdom) GA en-GB_CharlotteV3Voice <br>Female Your browser does not support the audio tag. 
 GA en-GB_JamesV3Voice <br>Male Your browser does not support the audio tag. 
 GA en-GB_KateV3Voice <br>Female Your browser does not support the audio tag. 
 English <br>(United States) GA en-US_AllisonV3Voice <br>Female Your browser does not support the audio tag. 
 GA en-US_EmilyV3Voice <br>Female Your browser does not support the audio tag. 
 GA en-US_HenryV3Voice <br>Male Your browser does not support the audio tag. 
 GA en-US_KevinV3Voice <br>Male Your browser does not support the audio tag. 
 GA en-US_LisaV3Voice <br>Female Your browser does not support the audio tag.