vania-janet/multiturn-rag-retrieval-data huggingface dataset:
You use SSML to indicate the style and the text to which it is to be applied. For more information, see [Using speaking styles](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-synthesis-expressivesyntheses-expressive-styles).





 Emphasizing interjections 

When you use expressive neural voices, the service automatically detects a collection of common interjections based on context. When it synthesizes these interjections, it gives them the natural emphasis that a human would use in normal conversation. For some of the interjections, you can use SSML to enable or disable their emphasis. For more information, see [Emphasizing interjections](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-synthesis-expressiveemphasizing-interjections).





 Emphasizing words 

The expressive voices use a conversational style that naturally applies the correct intonation from context. But you can indicate that one or more words are to be given more or less emphasis. The change in stress can be indicated by an increase or decrease in pitch, timing, volume, or other acoustic attributes. For more information, see [Emphasizing words](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-synthesis-expressiveemphasizing-words).







 Customizing the service 

The service includes a customization interface that you can use to create custom models for use during speech synthesis. A custom model is a dictionary of words and their translations for a specific language. Each word/translation pair in a model tells the service how to pronounce a word when it occurs in input text.

You can use custom models to create application-specific translations for unusual words for which the service's regular pronunciation rules might yield imperfect pronunciations. For example, your application might routinely encounter domain-specific terms, special terms with foreign origins, personal or geographic names, or abbreviations and acronyms. By using customization, you can define translations that tell the service how you want such terms to be pronounced.
curl -X POST -u "apikey:{apikey}" --header "Content-Type: application/json" --data '{
"url": "http://newsroom.ibm.com/Guerbet-and-IBM-Watson-Health-Announce-Strategic-Partnership-for-Artificial-Intelligence-in-Medical-Imaging-Liver",
"features": {
"sentiment": {},
"categories": {},
"concepts": {},
"entities": {},
"keywords": {}
}
}' "{url}/v1/analyze?version=2019-07-12"

Windows users: This command might not run on Windows. Run the following command instead:

curl -X POST -u "apikey:{apikey}" --header "Content-Type: application/json" --data "{"url":"http://newsroom.ibm.com/Guerbet-and-IBM-Watson-Health-Announce-Strategic-Partnership-for-Artificial-Intelligence-in-Medical-Imaging-Liver","features":{"sentiment":{},"categories":{},"concepts":{},"entities":{},"keywords":{}}}" "{url}/v1/analyze?version=2019-07-12"

The next step demonstrates how to specify options that customize the analysis for each feature.





 Step 2: Analyze target phrases and keywords 

Natural Language Understanding can analyze target phrases in context of the surrounding text for focused sentiment and emotion results. The targets option for sentiment in the following example tells the service to search for the targets "apples", "oranges", and "broccoli". Since "apples" and "oranges" are located in the text, sentiment scores are returned for those targets.

You can also get sentiment and emotion results for entities and keywords that are detected in your text. In the example, the emotion option for keywords tells the service to analyze each detected keyword for emotion results.
When you use a grammar with a custom language model for speech recognition, the service can recognize only words, phrases, and strings that are recognized by the grammar. Because the grammar defines a limited search space for valid matches, the service can deliver results faster and more accurately.

You add a grammar to a custom language model and train the model just as you do for a corpus. Unlike a corpus, however, you must explicitly specify that a grammar is to be used with a custom model during speech recognition.

For more information about getting started with grammars, see



* [Using grammars with custom language models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammars)
* [Understanding grammars](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarUnderstand)
* [Adding a grammar to a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarAdd)
* [Using a grammar for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarUse)







 Using acoustic and language customization together 

Using acoustic and language customization together applies only to previous-generation models. Also, some previous-generation models do not support both language and acoustic customization.

Using a custom acoustic model alone can improve the service's recognition capabilities. But if transcriptions or related corpora are available for your example audio, you can use that data to further improve the quality of speech recognition based on the custom acoustic model.

By creating a custom language model that complements your custom acoustic model, you can enhance speech recognition by using the two models together. When you train a custom acoustic model, you can specify a custom language model that includes transcriptions of the audio resources or a vocabulary of domain-specific words from the resources. Similarly, when you transcribe audio, the service accepts a custom language model, a custom acoustic model, or both. And if your custom language model includes a grammar, you can use that model and grammar with a custom acoustic model for speech recognition.
Each word/translation pair in a model tells the service how to pronounce a word when it occurs in input text.

You can use custom models to create application-specific translations for unusual words for which the service's regular pronunciation rules might yield imperfect pronunciations. For example, your application might routinely encounter domain-specific terms, special terms with foreign origins, personal or geographic names, or abbreviations and acronyms. By using customization, you can define translations that tell the service how you want such terms to be pronounced.

You can define the custom entry for a word/translation pair based on other words, or you can create pronunciations based on phoneme symbols in the standard International Phonetic Alphabet (IPA) or the proprietary IBM Symbolic Phonetic Representation (SPR). Customization is available for all languages.



* For more information about customization, see [Understanding customization](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-customIntro).
* For more information about using phonetic IPA and SPR symbols, see [Understanding phonetic symbols](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-symbols).



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 about pricing plans, see the Text to Speech service in the [IBM Cloud® Catalog](https://cloud.ibm.com/catalog/text-to-speech).



 Creating a custom voice 

IBM Cloud

Premium customers can work with IBM to train a new custom voice for their specific application needs. A custom voice is a unique voice that is based on audio training data that the customer provides. IBM can train a custom voice with as little as one hour of training data.

To request a custom voice or for more information, complete and submit this [IBM Request Form](https://form.asana.com/?k=CBuXK1uwlAf5ek6FwB6mcg&d=8612789739828).







 Using Tune by Example
* [Using languages and voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-service-featuresfeatures-languages-voices) and [Using audio formats](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-service-featuresfeatures-audio-formats) describe the basic underpinnings of the service's capabilities. You must choose a language and voice that are suitable for your text and application, and you must understand the characteristics of the audio the service returns.
* [Synthesizing speech with the service](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-service-featuresfeatures-synthesis-interfaces) provides links to detailed presentations of each of the service's interfaces. Experiment with the interfaces to determine which is best suited to your application needs.
* [Using speech synthesis features](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-service-featuresfeatures-synthesis) briefly describes the features that are available for speech synthesis and provides links for more information. Use the features to tailor the text that you send and the audio that you receive.
* [Using speech synthesis features with expressive neural voice](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-service-featuresfeatures-synthesis-expressive) introduces three additional features that are available for speech synthesis with expressive neural voices.
* [Customizing the service](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-service-featuresfeatures-customization) describes the more advanced topic of customization, which you can use to create custom models that contain dictionaries of words and their translations for specific languages.
* [Using Tune by Example](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-service-featuresfeatures-tune-by-example) introduces the Tune by Example feature that lets you create custom prompts.
The name you specify is saved as a title attribute of the node in the workspace JSON file and the system uses a unique ID that is stored in the name attribute to reference the node.





 23 August 2017 

Updates to Korean, Japanese, and Italian
: Language support has been enhanced for Korean, Japanese, and Italian. Note that the Watson Assistant service learning models may have been updated as part of this enhancement, and when you retrain your model any changes will be applied.





 10 August 2017 

Accent normalization
: In a conversational setting, users may or may not use accents while interacting with the Watson Assistant service. As such, an update has been made to the algorithm so that accented and non-accented versions of words are 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 may 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.

Note: Accent normalization is enabled for Portuguese, Spanish, French, and Czech.
About Language Translator 

IBM is announcing the deprecation of the IBM Watson® Language Translator service for IBM Cloud® in all regions. As of 10 June 2023, the Language Translator tile will be removed from the IBM Cloud Platform for new customers; only existing customers will be able to access the product. As of 10 June 2024, the service will reach its End of Support date. As of 10 December 2024, the service will be withdrawn entirely and will no longer be available to any customers.

IBM Watson® Language Translator can identify the language of text and translate it into different languages programmatically. In addition to using the provided translation models, you can build upon the base translation models to create your own [custom models](https://cloud.ibm.com/docs/language-translator?topic=language-translator-customizing) for your use case.

[Translating documents](https://cloud.ibm.com/docs/language-translator?topic=language-translator-document-translator-tutorial). Submit a Microsoft Office document, PDF, or other document with a [supported file format](https://cloud.ibm.com/docs/language-translator?topic=language-translator-document-translator-tutorialsupported-file-formats), and Language Translator will provide a translated copy that preserves the original formatting.



 Service updates 



* For information about all service updates, see the [Release notes for Language Translator](https://cloud.ibm.com/docs/language-translator?topic=language-translator-release-notes).
* For information about the latest service update, see the [13 January 2023](https://cloud.ibm.com/docs/language-translator?topic=language-translator-release-noteslanguage-translator-13january2023) update in the release notes.







 HIPAA support 

US Health Insurance Portability and Accountability Act (HIPAA) support is available for Premium plans in the Washington, DC location created on or after 1 April 2019. See [Enabling EU and HIPAA supported settings](https://cloud.ibm.com/docs/account?topic=account-eu-hipaa-supportedeu-hipaa-supported) for more information.





 Request logging
* [Improved language model customization for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-customizationcustomLanguage-intro-ng)
* [Upgrading custom models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-upgrade)
* [Using customization weight](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageUseweight)
* [Character insertion bias](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsinginsertion-bias)



Defect fix: Grammar files now handle strings of digits correctly
: Defect fix: When grammars are used, the service now handles longer strings of digits correctly. Previously, it was failing to complete recognition or returning incorrect results.





 15 February 2023 

Important: All previous-generation models are deprecated and will reach end of service on 31 July 2023
: Important: All previous-generation models are deprecated and will reach end of service effective 31 July 2023. On that date, all previous-generation models will be removed from the service and the documentation. The previous deprecation date was 3 March 2023. The new date allows users more time to migrate to the appropriate next-generation models. But users must migrate to the equivalent next-generation model by 31 July 2023.

Most previous-generation models were deprecated on 15 March 2022. Previously, the Arabic and Japanese models were not deprecated. Deprecation now applies to all previous-generation models.



* For more information about the next-generation models to which you can migrate from each of the deprecated models, see [Previous-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models)
curl -X GET --user "apikey:{apikey}" "{url}/v3/models?source=en&target=es&version=2018-05-01"

The method returns information about the en-es translation model that you can use to translate from English to Spanish. The model is customizable.

"models": [
{
"model_id": "en-es",
"source": "en",
"target": "es",
"base_model_id": "",
"domain": "general",
"customizable": true,
"default_model": true,
"owner": "",
"status": "available",
"name": "en-es"
},
]





 List of customizable translation models 

The following sections list the customizable translation models for each language. As mentioned previously, not all language combinations that are supported for translation are also customizable. Typically, only language combinations with English as the source or target language are customizable.



 Arabic 

The following Arabic translation model can be customized.



Table 2. Arabic translation model

 Model ID Source Target Domain 

 ar-en Arabic (ar) English (en) general 







 Basque 

The following Basque translation model can be customized.



Table 3. Basque translation model

 Model ID Source Target Domain 

 eu-es Basque (eu) Spanish (es) general 







 Bengali 

The following Bengali translation model can be customized.



Table 4. Bengali translation model

 Model ID Source Target Domain 

 bn-en Bengali (bn) English (en) general 







 Bosnian 

The following Bosnian translation model can be customized.



Table 5. Bosnian translation model

 Model ID Source Target Domain 

 bs-en Bosnian (bs) English (en) general 







 Bulgarian 

The following Bulgarian translation model can be customized.



Table 6. Bulgarian translation model

 Model ID Source Target Domain 

 bg-en Bulgarian (bg) English (en) general 







 Catalan 

The following Catalan translation model can be customized.
In text, each word or character is generally referred to as a token. The sentence "The CEO announced the earnings last week, missing analyst estimates by 5%." contains 15 tokens: 11 words separated by spaces, 3 special characters, and 1 number.

You might want to locate references to red brick houses, knowing that phrases such as house made of red brick, red house of brick and the brick on the house reflected red might appear in the text you are analyzing. Proximity rules can ensure that these phrases are extracted while excluding sentences or phrases that coincidentally include the target terms, for example, "The red car passed a house near the brick yard".



1. Right-click an extractor on the canvas and under one of the Add submenus, click Proximity Rule.
2. In the format minimum-maximum, specify the minimum and maximum number of words, special characters, or values to allow between terms. By default, the proximity pattern is set to match the number of tokens between the two numbers you specify in your range. If the desired proximity does not vary, you can change the setting. For example, enter 0-2.
3. Select the new proximity rule. The Extractor Properties opens to the Settings tab where you can change the number range or convert to an exact number of tokens.



Example 1:



1. Create a dictionary called Clerical title that includes terms such as Rabbi, Father, and Archbishop.
2. Drag the Person extractor to right side of the Clerical title dictionary so that the new sequence will find titles followed by names. When you release the mouse button, a sequence object is displayed on the canvas.
3. Right-click Clerical title and click Add After > Proximity Rule. To capture terms such as Archbishop of Canterbury, Robert Runcie, specify the minimum and maximum number of tokens between words, in this case 0-5.
4. Click the sequence on the canvas and then under Extractor Properties, open the General tab if it doesn't open automatically. Rename the sequence Clerics.

!
For a custom word, enter a commonly used notation that reflects the word's usage and pronunciation. This allows for more efficient customization. For instance, the following example does not reliably produce the string Artificial_Intelligence in response to the utterance エーアイ because Artificial_Intelligence and 人工知能 are not generally uttered as AI:

{"word": "Artificial_Intelligence", "sounds_like": ["エーアイ"], "display_as": "Artificial_Intelligence"},
{"word": "人工知能", "sounds_like": ["エーアイ"], "display_as": "Artificial_Intelligence"}

Because the context is typically something like これからＡＩはますます発展してきます, ＡＩ is the most appropriate notation for the sounds-like エーアイ. The following example is therefore likely to yield better results:

{"word": "ＡＩ", "sounds_like": ["エーアイ"], "display_as": "Artificial_Intelligence"}

Finally, in custom words, half-width alphabetic characters are converted to full-width characters. English uppercase and lowercase characters are treated as different characters.







 Working with corpora for next-generation models 

You use the POST /v1/customizations/{customization_id}/corpora/{corpus_name} method to add a corpus to a custom model. A corpus is a plain text file that contains sample sentences from your domain. The following example shows an abbreviated corpus for the healthcare domain. A corpus file is typically much longer.

Am I at risk for health problems during travel?
Some people are more likely to have health problems when traveling outside the United States.
How Is Coronary Microvascular Disease Treated?
If you're diagnosed with coronary MVD and also have anemia, you may benefit from treatment for that condition.
Anemia is thought to slow the growth of cells needed to repair damaged blood vessels.
What causes autoimmune hepatitis?
A combination of autoimmunity, environmental triggers, and a genetic predisposition can lead to autoimmune hepatitis.
What research is being done for Spinal Cord Injury?
For example, accountants adhere to a common set of standards and procedures that are known as Generally Accepted Accounting Principles (GAAP). When you create a custom model for a financial domain, provide sentences that use the term GAAP in context. The sentences help the service distinguish between general phrases such as "the gap between them is small" and domain-centric phrases such as "GAAP provides guidelines for measuring and disclosing financial information."

In general, it is better for corpora to use words in different contexts, which can improve how the service learns the words. However, if users speak the words in only a couple of contexts, then showing the words in other contexts does not improve the quality of the custom model: Speakers never use the words in those contexts. If speakers are likely to use the same phrase frequently, then repeating that phrase in the corpora can improve the quality of the model. (In some cases, even adding a few custom words directly to a custom model can make a positive difference.)



 Preparing a corpus text file 

Follow these guidelines to prepare a corpus text file:



* Provide a plain text file that is encoded in UTF-8 if it contains non-ASCII characters. The service assumes UTF-8 encoding if it encounters such characters.

Make sure that you know the character encoding of your corpus text files. The service preserves the encoding that it finds in the text files. You must use that same encoding when working with custom words in the custom model. For more information, see [Character encoding for custom words](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageWordscharEncoding).
* Use consistent capitalization for words in the corpus. The words resource is case-sensitive. Mix upper- and lowercase letters and use capitalization only when intended.
* Include each sentence of the corpus on its own line, and terminate each line with a carriage return. Including multiple sentences on the same line can degrade accuracy.
* Add personal names as discrete units on separate lines. Do not add the individual elements of a name on separate lines or as individual custom words, and do not include multiple names on the same line of a corpus.