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Customize speech recognition text output format as you need.
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","body":"
Display output text format in automatic Speech Recognition is critical to final readability and downstream tasks, and one-size doesn’t always fit all.
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We are thrilled to announce the Public Preview of Custom Display Format (also known as “Custom Display-Post-Processing” or “Custom DPP”) within Azure Custom Speech Service. that allows you to customize speech recognition output format per your business needs.
\nCustom Display Formatting allows customers to define their own speech recognition display formats with custom speech models and endpoints. It includes following capabilities:
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\n
en-us, en-au, en-ca, en-gb, en-hk, en-in, en-ie, en-nz, en-ng, en-ph, en-sg, en-zh, zh-cn, zh-hk, pt-br, pt-pt, it-it, es-es, es-mx, fr-fr, fr-ca, de-de, ja-jp, ko-kr, nb-no, nl-nl, sv-se, da-dk, fi-fi, tr-tr, hi-in, pl-pl, etc (Language support)
\nThe display text pipeline is composed by a sequence of display format builders. Each builder corresponds to a display format tasks such as ITN, capitalization, and profanity filtering.
\nI spend twenty dollars -> I spend $20she is from microsoft -> She is from MicrosoftI never say abcd -> I never say ****\n
The base builders of the display text pipeline are maintained by Microsoft for the general purpose display processing tasks. You get the base builders by default when you use the Speech service. Beside the base builders maintained by Microsoft for the general purpose display processing tasks, you can define custom display text formatting rules (Custom ITN, Custom Profanity, Rewrite) to customize the display text formatting pipeline for your specific scenarios:
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The order of the display text formatting pipeline is illustrated in this diagram.
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\n
General speaking, for an input string, rewrite model will try to replace the original phrase in the input string with the corresponding new phrase for each rewrite rule. A rewrite model is a collection of rewrite rules.
\n
original phrase{TAB}new phrase. original phrase will be used as the match.Grammar punctuations are used to separate a sentence or phrase, and clarify how a sentence or phrase should be read. The following punctuations are grammar punctuations if they are followed by space or at the begin or end of a sentence or phrase.
\n. , ? 、 ! : ; ？ 。 ， ¿ ¡ । ؟ ،
\n
Here are the grammar punctuations rules:
\n. in x. y (with a space between . and y) is a grammar punctuation.zh-cn and ja-jp) aren't grammar punctuations. In that case they are ordinary characters. For example, the . in x.y isn't a grammar punctuation.zh-cn and ja-jp (non-spacing locales), punctuation characters are always used as grammar punctuation even if they are between characters. For example, the . in 中.文 is a grammar punctuation.The name 'CVOID-19' sometimes was recognized as 'covered 19', to correct it:
\nReform rule: covered 19{TAB}COVID-19
Sample: \"covered 19 is a virus\" -> \"COVID-19 is a virus\"
\n
Gottfried Wilhelm Leibniz was a German mathematician. To make his name displays correctly:
\nReform rule: gottfried leibniz{TAB}Gottfried Leibniz
Sample: \"gottfried leibniz was a mathematician\" -> \"Gottfried Leibniz was a mathematician\"
A custom profanity model acts the same as the base profanity model, except it uses a custom profanity phrase list. In addition, the custom profanity model will try to match (case insensitive) all the profanity phrases defined in the display text formatting file.
\n. , ? 、 ! : ; ？ 。 ， ¿ ¡ । ؟ ، .zh-cn and ja-jp locales, English profanity phrases aren't supported. English profanity words are supported. Profanity phrases for zh-cn and ja-jp locales are supported.Assume xyz is a profanity word, to add it:
Profanity rule: xyz
Test sample: Turned on profanity masking to mask xyz -> Turned on profanity masking to mask ***
Assume abc lmn is a profanity phrase, to add it:
Profanity rule: abc lmn
Test sample: Turned on profanity masking to mask abc lmn -> Turned on profanity masking to mask *** ***
The philosophy of pattern-based custom ITN is that you can simply specify the final output that you want to see. The Speech service will figure out how the words might be spoken and map the predicted spoken expressions to the specified output format.
\nA custom ITN model is built from a set of ITN rules. An ITN rule is a regular expression like pattern string which describes:
\nThe default ITN rules provided by Microsoft will be applied first. The output of the default ITN model will be used as the input of the custom ITN model. The matching algorithm inside the custom ITN model is case-insensitive.
\n\n
There are four categories of pattern matching with custom ITN rules.
\nFor example, a developer may have an item (maybe a product) named with the alphanumeric form \"JO:500\". The job of our system will be to figure out that users might say the letter part as \"J O\", or they might say \"joe\", and the number part as \"five hundred\" or \"five zero zero\" or \"five oh oh\" or \"five double zero\", and then build a model that maps all of these possibilities back to \"JO:500\" (including inserting the colon).
\nPatterns can be applied in parallel (put one per line), so a pattern specification file can be specified like:
\nJO:500
MM:760
Suppose a customer needs to refer to a whole series of alphanumeric items named \"JO:500\", \"JO:600\", \"JO:700\", etc. We can support this without requiring spelling out all possibilities in several ways.
\nCharacter ranges can be specified with the notation [...], so JO:[5-7]00 is equivalent to writing out three patterns.
There is also a set of wildcard items that can be used. One of these is \\d, which means any digit. So \"JO:\\d00\" covers \"JO:000\", \"JO:100\", etc ... \"JO:900\".
\nHere is a list of supported character classes:
\n\\d - match a digit from '0' to '9', and output it directly\\l - match a letter (case-insensitive) and transduce it to lower case\\u - match a letter (case-insensitive) and transduce it to upper case\\a - match a letter (case-insensitive) and output it directly\n
There are also \"whack escape\" expressions for referring to characters that otherwise have special syntactic meaning:
\n\\\\ - match and output the char \\\\( and \\)\\{ and \\}\\|\\+ and \\? and \\*To enhance the flexibily of pattern writing, regular expression-like constructions of phrases with alternatives and Kleene-closure are supported.
\n(...) - The parentheses don't literally count as characters to be matched.| character such as (AB|CDE).? to indicate that it is optional, + to indicate that it can be repeated, or * to indicate both. Note that you can only suffix phrases with these characters and not individual characters (which is more restrictive than most regular expression implementations).A pattern such as (AB|CD)-(\\d)+ would represent constructs like \"AB-9\" or \"CD-22\" and be expanded to spoken words like A B nine and C D twenty two (or C D two two).
Although our general philosophy is \"you show us what the output should look like, and we'll figure out how people will say it\", this doesn't work 100% of the time because some scenarios may have quirky unpredictable ways of saying things, or our background rules may just have gaps. For example, there may be colloquial pronunciations for initials and acronyms -- \"ZPI\" might be spoken as \"zippy\". In this case a pattern like ZPI-\\d\\d is unlikely to work if a user says \"zippy twenty two\". For this sort of situation, we have a notation {spoken>written}. This particular case could be written out {zippy>ZPI}-\\d\\d.
This can be useful for handling things that the Speech mapping rules but don't yet support. For example you might write a pattern \\d0-\\d0 expecting the system to understand that \"-\" can mean a range, and should be pronounced \"to\", as in \"twenty to thirty\". But perhaps it doesn't. So you can write a more explicit pattern like \\d0{to>-}\\d0 and tell it how you expect the dash to be read.
You can also leave out the > and following written form to indicate words that should be recognized but ignored. So a pattern like {write} (\\u.)+ will recognize \"write A B C\" and output \"A.B.C\"--dropping the \"write\" part.
ITN rule: \\d\\d\\d-\\d\\d\\d
Sample: \"cadence one oh five one fifteen\" -> \"cadence 105-115\"
ITN rule: Space: 1999
Sample: \"watching space nineteen ninety nine\" -> \"watching Space: 1999\"
ITN rule: AK-\\d\\d
Sample: \"a k forty seven\" -> \"AK-47\"
ITN rule: \\d[05]{ to >-}\\d[05]
Sample: \"fifteen to twenty -> 15-20\"
Learn more about how to prepare Custom Display Format data
\nDisplay output text format in automatic Speech Recognition is critical to final readability and downstream tasks, and one-size doesn’t always fit all.
\n\n
We are thrilled to announce the Public Preview of Custom Display Format (also known as “Custom Display-Post-Processing” or “Custom DPP”) within Azure Custom Speech Service. that allows you to customize speech recognition output format per your business needs.
\nCustom Display Formatting allows customers to define their own speech recognition display formats with custom speech models and endpoints. It includes following capabilities:
\n\n
en-us, en-au, en-ca, en-gb, en-hk, en-in, en-ie, en-nz, en-ng, en-ph, en-sg, en-zh, zh-cn, zh-hk, pt-br, pt-pt, it-it, es-es, es-mx, fr-fr, fr-ca, de-de, ja-jp, ko-kr, nb-no, nl-nl, sv-se, da-dk, fi-fi, tr-tr, hi-in, pl-pl, etc (Language support)
\nThe display text pipeline is composed by a sequence of display format builders. Each builder corresponds to a display format tasks such as ITN, capitalization, and profanity filtering.
\nI spend twenty dollars -> I spend $20she is from microsoft -> She is from MicrosoftI never say abcd -> I never say ****\n
The base builders of the display text pipeline are maintained by Microsoft for the general purpose display processing tasks. You get the base builders by default when you use the Speech service. Beside the base builders maintained by Microsoft for the general purpose display processing tasks, you can define custom display text formatting rules (Custom ITN, Custom Profanity, Rewrite) to customize the display text formatting pipeline for your specific scenarios:
\n\n
The order of the display text formatting pipeline is illustrated in this diagram.
\n\n
General speaking, for an input string, rewrite model will try to replace the original phrase in the input string with the corresponding new phrase for each rewrite rule. A rewrite model is a collection of rewrite rules.
\n
original phrase{TAB}new phrase. original phrase will be used as the match.Grammar punctuations are used to separate a sentence or phrase, and clarify how a sentence or phrase should be read. The following punctuations are grammar punctuations if they are followed by space or at the begin or end of a sentence or phrase.
\n. , ? 、 ! : ; ？ 。 ， ¿ ¡ । ؟ ،
\n
Here are the grammar punctuations rules:
\n. in x. y (with a space between . and y) is a grammar punctuation.zh-cn and ja-jp) aren't grammar punctuations. In that case they are ordinary characters. For example, the . in x.y isn't a grammar punctuation.zh-cn and ja-jp (non-spacing locales), punctuation characters are always used as grammar punctuation even if they are between characters. For example, the . in 中.文 is a grammar punctuation.The name 'CVOID-19' sometimes was recognized as 'covered 19', to correct it:
\nReform rule: covered 19{TAB}COVID-19
Sample: \"covered 19 is a virus\" -> \"COVID-19 is a virus\"
\n
Gottfried Wilhelm Leibniz was a German mathematician. To make his name displays correctly:
\nReform rule: gottfried leibniz{TAB}Gottfried Leibniz
Sample: \"gottfried leibniz was a mathematician\" -> \"Gottfried Leibniz was a mathematician\"
A custom profanity model acts the same as the base profanity model, except it uses a custom profanity phrase list. In addition, the custom profanity model will try to match (case insensitive) all the profanity phrases defined in the display text formatting file.
\n. , ? 、 ! : ; ？ 。 ， ¿ ¡ । ؟ ، .zh-cn and ja-jp locales, English profanity phrases aren't supported. English profanity words are supported. Profanity phrases for zh-cn and ja-jp locales are supported.Assume xyz is a profanity word, to add it:
Profanity rule: xyz
Test sample: Turned on profanity masking to mask xyz -> Turned on profanity masking to mask ***
Assume abc lmn is a profanity phrase, to add it:
Profanity rule: abc lmn
Test sample: Turned on profanity masking to mask abc lmn -> Turned on profanity masking to mask *** ***
The philosophy of pattern-based custom ITN is that you can simply specify the final output that you want to see. The Speech service will figure out how the words might be spoken and map the predicted spoken expressions to the specified output format.
\nA custom ITN model is built from a set of ITN rules. An ITN rule is a regular expression like pattern string which describes:
\nThe default ITN rules provided by Microsoft will be applied first. The output of the default ITN model will be used as the input of the custom ITN model. The matching algorithm inside the custom ITN model is case-insensitive.
\n\n
There are four categories of pattern matching with custom ITN rules.
\nFor example, a developer may have an item (maybe a product) named with the alphanumeric form \"JO:500\". The job of our system will be to figure out that users might say the letter part as \"J O\", or they might say \"joe\", and the number part as \"five hundred\" or \"five zero zero\" or \"five oh oh\" or \"five double zero\", and then build a model that maps all of these possibilities back to \"JO:500\" (including inserting the colon).
\nPatterns can be applied in parallel (put one per line), so a pattern specification file can be specified like:
\nJO:500
MM:760
Suppose a customer needs to refer to a whole series of alphanumeric items named \"JO:500\", \"JO:600\", \"JO:700\", etc. We can support this without requiring spelling out all possibilities in several ways.
\nCharacter ranges can be specified with the notation [...], so JO:[5-7]00 is equivalent to writing out three patterns.
There is also a set of wildcard items that can be used. One of these is \\d, which means any digit. So \"JO:\\d00\" covers \"JO:000\", \"JO:100\", etc ... \"JO:900\".
\nHere is a list of supported character classes:
\n\\d - match a digit from '0' to '9', and output it directly\\l - match a letter (case-insensitive) and transduce it to lower case\\u - match a letter (case-insensitive) and transduce it to upper case\\a - match a letter (case-insensitive) and output it directly\n
There are also \"whack escape\" expressions for referring to characters that otherwise have special syntactic meaning:
\n\\\\ - match and output the char \\\\( and \\)\\{ and \\}\\|\\+ and \\? and \\*To enhance the flexibily of pattern writing, regular expression-like constructions of phrases with alternatives and Kleene-closure are supported.
\n(...) - The parentheses don't literally count as characters to be matched.| character such as (AB|CDE).? to indicate that it is optional, + to indicate that it can be repeated, or * to indicate both. Note that you can only suffix phrases with these characters and not individual characters (which is more restrictive than most regular expression implementations).A pattern such as (AB|CD)-(\\d)+ would represent constructs like \"AB-9\" or \"CD-22\" and be expanded to spoken words like A B nine and C D twenty two (or C D two two).
Although our general philosophy is \"you show us what the output should look like, and we'll figure out how people will say it\", this doesn't work 100% of the time because some scenarios may have quirky unpredictable ways of saying things, or our background rules may just have gaps. For example, there may be colloquial pronunciations for initials and acronyms -- \"ZPI\" might be spoken as \"zippy\". In this case a pattern like ZPI-\\d\\d is unlikely to work if a user says \"zippy twenty two\". For this sort of situation, we have a notation {spoken>written}. This particular case could be written out {zippy>ZPI}-\\d\\d.
This can be useful for handling things that the Speech mapping rules but don't yet support. For example you might write a pattern \\d0-\\d0 expecting the system to understand that \"-\" can mean a range, and should be pronounced \"to\", as in \"twenty to thirty\". But perhaps it doesn't. So you can write a more explicit pattern like \\d0{to>-}\\d0 and tell it how you expect the dash to be read.
You can also leave out the > and following written form to indicate words that should be recognized but ignored. So a pattern like {write} (\\u.)+ will recognize \"write A B C\" and output \"A.B.C\"--dropping the \"write\" part.
ITN rule: \\d\\d\\d-\\d\\d\\d
Sample: \"cadence one oh five one fifteen\" -> \"cadence 105-115\"
ITN rule: Space: 1999
Sample: \"watching space nineteen ninety nine\" -> \"watching Space: 1999\"
ITN rule: AK-\\d\\d
Sample: \"a k forty seven\" -> \"AK-47\"
ITN rule: \\d[05]{ to >-}\\d[05]
Sample: \"fifteen to twenty -> 15-20\"
Learn more about how to prepare Custom Display Format data
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Customize speech recognition text output format as you need.
\n\n
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/discussions/excelgeneral/sharing-and-object-between-excel-on-mac-and-excel-on-windows/4103095