vania-janet/multiturn-rag-retrieval-data huggingface dataset:
This parameter can also affect both the quality and the latency of speech recognition. However, because background noise suppression is disabled by default, setting the parameter to a value greater than zero can only improve latency. But higher values can gradually reduce the audio that is passed for speech recognition, which can cause valid content to be lost from the transcript.



 Background audio suppression example 

The following example request specifies a value of 0.5 for the background_audio_suppression parameter with the synchronous HTTP interface. The service suppresses a reasonable level of background audio.

IBM Cloud

curl -X POST -u "apikey:{apikey}" --header "Content-Type: audio/flac" --data-binary @{path}audio-file1.flac "{url}/v1/recognize?background_audio_suppression=0.5"

IBM Cloud Pak for Data

curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: audio/flac" --data-binary @{path}audio-file1.flac "{url}/v1/recognize?background_audio_suppression=0.5"







 Language model support 

The speech_detector_sensitivity and background_audio_suppression parameters are supported for use with the following language models:



* For next-generation models, the parameters are supported with all models.
* For previous-generation models, the parameters are supported with most models. The following models do not support speech activity detection at this time. The parameters are ignored if used with these models.



* Arabic broadband model (ar-MS_BroadbandModel)
* Brazilian Portuguese broadband model (pt-BR_BroadbandModel)
* Chinese broadband model (zh-CN_BroadbandModel)
* Chinese narrowband model (zh-CN_NarrowbandModel)
* German broadband model (de-DE_BroadbandModel)
Getting confirmation 

Add a slot after the others that asks the user to confirm that the information you have collected is accurate and complete. The slot can look for responses that match the #yes or #no intent.



Confirmation slot

 Check for Save it as Prompt Follow-up if found Follow-up if not found 

 #yes #no $confirmation I'm going to order you a $size pizza for delivery at $time. Should I go ahead? 



Complex response Because users might include affirmative or negative statements at other times during the dialog (Oh yes, we want the pizza delivered at 5pm) or (no guests tonight, let's make it a small), use the slot_in_focus property to make it clear in the slot condition that you are looking for a Yes or No response to the prompt for this slot only.

(yes || no) && slot_in_focus

The slot_in_focus property always evaluates to a boolean (true or false) value. Only include it in a condition for which you want a boolean result. Do not use it in slot conditions that check for an entity type and then save the entity value, for example.

In the Not found prompt, clarify that you are expecting the user to provide a Yes or No answer.

{
"output":{
"text": {
"values": [
"Respond with Yes to indicate that you want the order to
be placed as-is, or No to indicate that you do not."
]
}
}
}

In the Found prompt, add a condition that checks for a No response (#no). When found, ask for the information all over again and reset the context variables that you saved earlier.

{
"conditions": "no",
"output":{
"text": {
"values": [
"Let's try this again. Tell me what size pizza
you want and the time..."
]
}
},
"context":{
"size": null,
"time": null,
"confirmation": null
}
}
Show more
3. Set the switch to Off, then click Save.



If you are using dialog in your assistant, follow these steps to disable autocorrection:



1. In the Options section, click Autocorrection.
2. Set the switch to Off.







 Testing autocorrection in dialog 

If you are using dialog, you can test autocorrection using Try it out.



1. In Try it out, enter a request that includes some misspelled words.

If words in your input are misspelled, they are corrected automatically, and an ![auto-correct](https://cloud.ibm.com/docs-content/v1/content/ca9232b3702768f24bb897f48f10778fcda28718/watson-assistant/images/auto-correct.png) icon is displayed. The corrected utterance is underlined.
2. Hover over the underlined utterance to see the original wording.



If there are misspelled terms that you expected your assistant to correct, but it did not, then review the rules that your assistant uses to decide whether to correct a word to see if the word falls into the category of words that your assistant intentionally does not change.





 Autocorrection rules 

To avoid overcorrection, your assistant does not correct the spelling of the following types of input:



* Capitalized words
* Emojis
* Locations, such as states and street addresses
* Numbers and units of measurement or time
* Proper nouns, such as common first names or company names
* Text within quotation marks
* Words containing special characters, such as hyphens (-), asterisks (*), ampersands (&), or at signs (@), including those used in email addresses or URLs.
* Words that belong, meaning words that have implied significance because they occur in your action steps or dialog entity values, entity synonyms, or intent user examples.





 How is spelling autocorrection related to fuzzy matching? 

In dialog, fuzzy matching helps your assistant recognize dictionary-based entity mentions in user input.
This change affects Watson Assistant workspaces that are trained after May 18, 2022. Workspaces that were trained before May 18, 2022 maintain the same behavior.

Any visible changes are most likely to be seen in dictionary-based or pattern-based entity matching. For more information about defining entities, see [Defining information to look for in customer input](https://cloud.ibm.com/docs/assistant?topic=assistant-entities). As a suggested practice, you can test your dialog skill with your current test framework to determine whether your workspace is impacted before you update your production workspace.

If entity values or synonyms that previously matched no longer match, you can update the entity and add a synonym with white space between the tokens, for example:



* Japanese: Add “見 た” as a synonym for “見た”
* Korean: Add “잘 자 요” as a synonym for “잘자요”



Enhanced intent detection available for Portuguese, German, and Simplified Chinese
: Enhanced intent detection is now available for Portuguese, German, and Simplified Chinese. The enhanced intent detection model improves your assistant's ability to understand what customers want.





 28 April 2022 

Assistant preview link can be disabled
: Assistant preview now includes a toggle to disable the preview link. This allows you to stop access to the preview link if necessary. For more information, see [Using the assistant preview to test your assistant](https://cloud.ibm.com/docs/assistant?topic=assistant-deploy-web-linkdeploy-web-link-try).





 5 April 2022 

Dialog feature available in the new Watson Assistant
: 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
* The detect-secrets scan command generates a baseline file or updates the existing file to contain a snapshot of all detected potential secrets of the run.
* The detect-secrets audit command labels secrets so that you can narrow down your checklist of secrets to migrate.



Audits identify the detected secrets as either true or false positives, you can then update the baseline file so the false positives can be excluded from the future scan runs.





 Updating the baseline 

Take the following steps to update your baseline:



1. Install detect-secrets on your local computer:

pip install --upgrade "git+https://github.com/ibm/detect-secrets.git@masteregg=detect-secrets"
2. Run the following commands to scan, generate, or update the baseline file in your repo:



1. Run the following command to scan the repo folder:

detect-secrets scan --update .secrets.baseline

You can use the --exclude-files flag when you want to ignore certain files that are known to contain false positives or do not require scanning. The exclusion rules are based on regular expressions format.

To perform a scan with file and folder exclusions, use the following command:

detect-secrets scan --update .secrets.baseline --exclude-files '<folder_to_ignore>|<file_to_ignore>'

Example : detect-secrets scan --update .secrets.baseline --exclude-files "package-lock.json|go.sum"

The list of excluded files is recorded in the baseline file.

If no --exclude-files option is provided in subsequent scans, detect secrets automatically respects the existing exclude list from the baseline file.

However, if you want to specify a new exclude list during the detect-secrets scan, the new list overwrites the existing exclude list in the baseline file.
2. Run the following command to review and audit the baseline file that is created with the scan step:

detect-secrets audit .secrets.baseline

This command brings up an interactive terminal to mark false positives. You can indicate (y)es if the secret found is an actual secret or (n)o if it is a false positive.
3.
Fields that include whitespaces (for example: body.additional reading) are filtered out during ingestion. The notices description reads The field 'additional reading' is invalid: whitespace, '.', '' and ',' are invalid in a field name. The field result_metadata is filtered out during ingestion.





 16 October 2017 

Update to version string : The version string for all API calls changed to 2017-10-16 from 2017-09-01. : This version deprecates support for uploading new documents into existing collections enriched with AlchemyLanguage enrichments, and for creating new collections and enriching them with AlchemyLanguage enrichments. It is recommended that existing collections enriched with AlchemyLanguage be migrated to Natural Language Understanding enrichments as soon as possible. The Discovery tooling also uses the 2017-10-16 version, see below for more information. : The Discovery tooling uses the 2017-10-16 API version string, so if you are using the tooling, you can no longer upload documents into existing AlchemyLanguage collections or create new collections enriched with AlchemyLanguage enrichments after 2017-10-16. If you want to continue using the Discovery tooling for enriching collections, migrate your collections to Natural Language Understanding first.

New sample queries and link in Data schema explorer : The Data schema explorer displays sample queries for several enrichments in the IBM Watson™ Discovery News collection. It also now has a Show more values link that displays additional example values for that enrichment in IBM Watson™ Discovery News.

Improved Manage data screen : Multiple productivity enhancements, including combining the collection statistics, errors and warnings, and data insights on the Manage data screen.

New completion notification : A message was added that displays an alert when documents are finished processing.





 9 October 2017 

New unique count metric in the API : A new aggregation metric unique_count is available in the API. It returns a count of the unique instances of the specified field in a collection. See [unique_count](https://cloud.ibm.com/docs/discovery?topic=discovery-query-aggregationsunique_count) for more information.
If entity values or synonyms that previously matched no longer match, you can update the entity and add a synonym with white space between the tokens, for example:



* Japanese: Add “見 た” as a synonym for “見た”
* Korean: Add “잘 자 요” as a synonym for “잘자요”



Assistant preview link can be disabled
: Assistant preview now includes a toggle to disable the preview link. This allows you to stop access to the preview link if necessary. For more information, see [Using the preview link integration to test your assistant](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-deploy-web-linkdeploy-web-link-try).





 27 April 2022 

IBM Watson® Assistant Cartridge for IBM Cloud Pak® for Data 4.0.8 is available
: Watson Assistant for IBM Cloud Pak® for Data 4.0.8 is compatible with IBM Cloud Pak® for Data 4.0 on Red Hat OpenShift 4.8. This release of Watson Assistant for IBM Cloud Pak® for Data includes various fixes and features.

Closed entity matching with accent-normalized values in French
: Closed entities exact matches in French are completed using accent-normalized values or synonyms. For example, if you define a closed entity with a value or synonym with accent marks (for example, garçon or déjà), then variants without accent marks are also recognized (garcon or deja). Likewise, if a closed entity value or synonym is defined without accent marks, then user inputs with accent marks are also recognized. For more information about defining entities, see [Defining information to look for in customer input](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-entities).

Pattern entities do not prevent spelling autocorrection
: Pattern entities that match all characters and words that are usually used to count input words do not prevent spelling autocorrection. For example, if a customer defines the ^..{0,19}$ pattern entity that matches the first 20 characters of an input, then the entity match does not affect spelling autocorrection.
{"tokens":["my", "name", "is", "chris", "wright", "smith", "i", "live", "at", "a", "front", "street", "durham", "uk", "my", "email", "is", "chris", "aol","com"]}





 White space analyzer 

The White space analyzer changes the string in the following ways:



* Removes some punctuation.
* Splits words on spaces.
* No stop words removed (notice "is" and "at").
* Words remain case-sensitive.
* Note how email stays intact.



{"tokens":["My", "name", "is", "Chris", "Wright-Smith.", "I", "live", "at", "21a", "Front", "Street,", "Durham,", "UK", "-" , "my" ,"email", "is", "chris7767@aol.com."]}





 Classic analyzer 

The Classic analyzer changes the string in the following ways:



* Removes punctuation.
* Splits words based on spaces and punctuation.
* Removes stop words (no "is" or "at").
* Changes words to use lowercase letters.
* Note how email stays intact.



{"tokens":["my", "name", "chris", "wright", "smith", "i", "live", "21a", "front", "street", "durham", "uk", "my", "email", "chris7767@aol.com"]}





 English analyzer 

The English analyzer changes the string in the following ways:



* Removes punctuation.
* Splits words based on spaces and punctuation.
* Stems words (notice "chris changes to chri").
* Removes stop words (no "is" or "at").
* Changes words to use lowercase letters.
The following example request specifies a value of 0.6 for the speech_detector_sensitivity parameter with the synchronous HTTP interface. The service recognizes slightly more potential non-speech events than it would by default.

IBM Cloud

curl -X POST -u "apikey:{apikey}" --header "Content-Type: audio/flac" --data-binary @{path}audio-file1.flac "{url}/v1/recognize?speech_detector_sensitivity=0.6"

IBM Cloud Pak for Data

curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: audio/flac" --data-binary @{path}audio-file1.flac "{url}/v1/recognize?speech_detector_sensitivity=0.6"







 Background audio suppression 

Use the background_audio_suppression parameter to suppress background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise. For example, use this parameter when there is a relatively steady and quiet (low signal energy) background sound. Because such noise can interfere with transcription, it can produce content where no actual speech occurs in the audio.

Specify a float value in the range of 0.0 to 1.0. The default value is 0.0, which provides no suppression (background audio suppression is disabled). A value of 0.5 provides a reasonable level of audio suppression for general usage. A value of 1.0 suppresses all audio (no speech is transcribed). The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

This parameter can also affect both the quality and the latency of speech recognition. However, because background noise suppression is disabled by default, setting the parameter to a value greater than zero can only improve latency. But higher values can gradually reduce the audio that is passed for speech recognition, which can cause valid content to be lost from the transcript.



 Background audio suppression example 

The following example request specifies a value of 0.5 for the background_audio_suppression parameter with the synchronous HTTP interface.
* expected_body: A case-insensitive sub-string to look for in the response body.
* expected_codes: The expected HTTP response code or code range of the health check.
* method: The HTTP method to use for the health check.
* path: The endpoint path to health check against.
* header: The HTTP request headers to send in the health check.
* follow_redirects: Follow redirects if returned by the origin.
* allow_insecure: Do not validate the certificate when monitor use HTTPS.
* probe_zone: Assign this monitor to emulate the specified zone while probing.

<-- </ul> -->

<-- </ul> -->

Sample JSON data:

For HTTP/HTTPS:
{
"description": "Health monitor of web service",
"type": "https",
"method": "GET",
"path": "/health",
"header": {
"Host":
"example.com"
],
"X-App-ID":
"abc123"
] ! !
},
"timeout": 5,
"retries": 2,
"interval": 90,
"follow_redirects": true,
"allow_insecure": false,
"expected_codes": "2xx",
"expected_body": "alive",
"probe_zone": "example.com"
}
Show more
For TCP: {
"description": "Health monitor of TCP",
"type": "tcp",
"port": 80,
"timeout": 5,
"retries": 2,
"interval": 90
}
-s, --json-str: Deprecated. The JSON data used to describe a GLB monitor.-j, --json-file: Deprecated.
"TS_EXP_CONTAINS” (StringExpType, StringExpType) Output: Boolean Expression
Expression to denote if first expression contains second expression.

"TS_EXP_STARTS_WITH” (StringExpType, StringExpType) Output: Boolean Expression
Expression to denote if first expression starts with second.

"TS_EXP_ENDS_WITH” (StringExpType, StringExpType) Output: Boolean Expression
Expression to denote if first expression ends with second expression.

"TS_EXP_EQUALS” (DoubleExpType | StringExpType, DoubleExpType | StringExpType) Output: Boolean Expression
Expression to denote if first expression equals second expression.

"TS_EXP_IF_THEN_ELSE” (BooleanExpType, DoubleExpType | StringExpType, DoubleExpType | StringExpType) Output: Conditional Expression
Expression that if the “if” condition is met, it returns the “then” expression, otherwise it returns the “else” expression.

"TS_EXP_IF_THEN” (BooleanExpType, DoubleExpType | StringExpType) Output: Conditional Expression
Expression that if the “if” condition is met, it returns the “then” expression, otherwise it returns null.

"TS_EXP_MATCH_CASE” (Array[TS_EXP_IF_THEN], DoubleExpType | StringExpType) Output: Conditional Expression
Expression that takes multiple ”IF_THEN” expressions, if none are met, the otherwise expression is chosen.

"TS_EXP_ABS” (DoubleExpType) Output: Double Expression
Expression that gets the absolute value of an expression.

"TS_EXP_EXP” (DoubleExpType) Output: Double Expression
Expression that gets the Euler's Number e that is raised to the power of an indicated DoubleExpression.

"TS_EXP_LOG” (DoubleExpType) Output: Double Expression
Expression that gets the log of an expression.

"TS_EXP_LOG10” (DoubleExpType) Output: Double Expression
Expression that gets the log10 of an expression.
Understanding speech recognition results 

Regardless of the interface that you use, the IBM Watson® Speech to Text service returns transcription results that reflect the parameters that you specify. The service returns all JSON response content in the UTF-8 character set.



 Basic transcription response 

The service returns the following response for the examples in [Making a speech recognition request](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-basic-request). The examples pass only an audio file and its content type. The audio speaks a single sentence with no noticeable pauses between words.

{
"result_index": 0,
"results": [
{
"alternatives":
{
"confidence": 0.96,
"transcript": "several tornadoes touch down as a line of severe thunderstorms swept through Colorado on Sunday "
}
],
"final": true
}
]
}

The service returns a SpeechRecognitionResults object, which is the top-level response object. For these simple requests, the object includes one results field and and one result_index field:



* The results field provides an array of information about the transcription results. For this example, the alternatives field includes the transcript and the service's confidence in the results. The final field has a value of true to indicate that these results will not change.
* The result_index field provides a unique identifier for the results. The example shows final results for a request with a single audio file that has no pauses, and the request includes no additional parameters. So the service returns a single result_index field with a value of 0, which is always the initial index.



If the input audio is more complex or the request includes additional parameters, the results can contain much more information.



 The alternatives field 

The alternatives field provides an array of transcription results. For this request, the array includes just one element.



* The transcript field provides the results of the transcription.
* The confidence field is a score that indicates the service's confidence in the transcript, which for this example exceeds 90 percent.
Expression functions are more powerful and allow more advanced use cases.

"TS_EXP_ID” () Output: Identity Expression
Denotes current observation’s value in time series.

"TS_EXP_ID_INDEX” (Integer) Output: Identity Expression
Denotes current observation’s value (array index) in time series.

"TS_EXP_NOT” (BooleanExpType) Output: Boolean Expression
Logical expression that returns the negation.

"TS_EXP_AND" BooleanExpType, BooleanExpType) Output: Boolean Expression
Logical expression that returns the output of an AND operation.

"TS_EXP_OR” (BooleanExpType, BooleanExpType) Output: Boolean Expression
Logical expression that returns the output of an OR operation.

"TS_EXP_LT” (DoubleExpType, DoubleExpType) Output: Boolean Expression
Expression to denote if first expression is less than second expression.

"TS_EXP_LTE” (DoubleExpType, DoubleExpType) Output: Boolean Expression
Expression to denote if first expression is less than or equal to second expression.

"TS_EXP_GT” (DoubleExpType, DoubleExpType) Output: Boolean Expression
Expression to denote if first expression is greater than second expression.

"TS_EXP_GTE” (DoubleExpType, DoubleExpType) Output: Boolean Expression
Expression to denote if first expression is greater than or equal to second expression.

"TS_EXP_DIVISIBLE_BY” (DoubleExpType, DoubleExpType) Output: Boolean Expression
Expression to denote if first expression is divisible by second expression.

"TS_EXP_CONTAINS” (StringExpType, StringExpType) Output: Boolean Expression
Expression to denote if first expression contains second expression.

"TS_EXP_STARTS_WITH” (StringExpType, StringExpType) Output: Boolean Expression
Expression to denote if first expression starts with second.

"TS_EXP_ENDS_WITH” (StringExpType, StringExpType) Output: Boolean Expression