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alphanumeric_input [2018/08/21 11:30] 127.0.0.1 external edit |
alphanumeric_input [2019/08/08 13:26] (current) lisa.illgen_concentrix.com Added Anchor Links |
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| ==== Alphanumeric Input ==== | ==== Alphanumeric Input ==== | ||
| - | Unconstrained alphanumeric input in the English language is notoriously poor due to the phonological similarities of the letter names in the English language (e.g., Lewis, 2011; Rolandi, 2006; Wright et al., 2002). For example, "B, C, D, E, G, P, Z, 3" etc. | + | Unconstrained alphanumeric input in the English language is notoriously poor due to the phonological similarities of the letter names in the English language (e.g., [[references#lewis2011|Lewis, 2011]]; [[references#rolandi2006|Rolandi, 2006]]; [[references#wright|Wright et al., 2002]]). For example, "B, C, D, E, G, P, Z, 3" etc. |
| There are two basic strategies to improve alphanumeric recognition. | There are two basic strategies to improve alphanumeric recognition. | ||
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| **// For long strings, consider breaking the input into chunks //**\\ | **// For long strings, consider breaking the input into chunks //**\\ | ||
| - | The longer an alphanumeric string is, the more likely it is that the string will have an error somewhere in it, which will make the entire string be in error. For example, suppose you have a recognition system that has an average character accuracy of 97%, so individual errors only happen 3/100 times (on average). If someone speaks 4 characters, the likelihood that the entire string will be correct is (1-(1-p))^n (where p is the average accuracy and n is the number of characters) -- in this case, 89%. For 16 characters (like a 16-digit credit card number), the full string accuracy drops to 61%. For this reason, if it makes contextual sense, consider breaking long strings up into chunks. On the other hand, many modern recognizers (especially tuned digit recognizers) have very high recognition accuracies, so it's OK to start by prompting for the full string -- especially if you can compare the n-best list to a database or set of business rules to weed out obviously incorrect strings (see [[Using n-best Lists]]). For a piecewise grammar and confirmation strategy to use when parts of a string have high recognition accuracy but another part does not, see Parkinson (2012). | + | The longer an alphanumeric string is, the more likely it is that the string will have an error somewhere in it, which will make the entire string be in error. For example, suppose you have a recognition system that has an average character accuracy of 97%, so individual errors only happen 3/100 times (on average). If someone speaks 4 characters, the likelihood that the entire string will be correct is (1-(1-p))^n (where p is the average accuracy and n is the number of characters) -- in this case, 89%. For 16 characters (like a 16-digit credit card number), the full string accuracy drops to 61%. For this reason, if it makes contextual sense, consider breaking long strings up into chunks. On the other hand, many modern recognizers (especially tuned digit recognizers) have very high recognition accuracies, so it's OK to start by prompting for the full string -- especially if you can compare the n-best list to a database or set of business rules to weed out obviously incorrect strings (see [[Using n-best Lists]]). For a piecewise grammar and confirmation strategy to use when parts of a string have high recognition accuracy but another part does not, see [[references#parkinson|Parkinson]] (2012). |
| ==== For Spanish ==== | ==== For Spanish ==== | ||