The language being spoken and the gender speaking the language has a dramatic effect on the accuracy of the translation. In general, men’s voices are more difficult for voice recognition software to detect.
When voice recognition software attempts to recognize words, it looks for pieces of information that follow a similar pattern. Humans do this too by looking for familiar chunks of information.
This is why if you were to read a passage completely devoid of vowels, you would still be able to understand what it says.
How Voice Recognition Software Works
Consider the following sentence: “Th ppl wnt t th prk.”
Most people would be able to ready this sentence even though it’s missing all of the vowels. Voice recognition software works much the same way since it doesn’t need to recognize every single consonant and vowel to identify the word being spoken.
Furthermore, voice recognition software is often able to correctly identify common words and phrases.
When a language consists of a large number of variations in expression and idiomatic sayings, it can be more difficult to design the program to anticipate what is actually being said. In these cases, the language must be processed word by word.
This sort of processing is much more difficult to program.
The Difficulty of Language Recognition
Language recognition software has a very difficult job. It has to process the many phonemes in the human language, and accurately put them together to form words.
Since we often use different inflections to represent words, even a different accent from different parts of the country can cause problems.
Dictation programs use a combination of comprehensive dictionaries and common phrases to help fill in the gaps. For example, if you pronounce a phrase as “thank dew,” the computer knows that you most likely mean “thank you” since that is the most commonly used phrase that sounds similar.
The Most Difficult Languages
The most difficult languages in the world to identify are the languages that use tonal inflections to determine the meaning. Chinese is one of these languages as well as Thai and Vietnamese.
Japanese is actually a very straightforward language to dictate because the intonation does not dictate the meaning of what is being said.
Korean is also a very difficult language for voice recognition software to detect. Swedish, Norwegian, Serbo-Croatian and Lithuanian are difficult languages because depending on where the accent is placed in the word it can have a very different meaning.
Humans have difficulty detecting tones when learning a new language, and computers aren’t necessarily doing any better on this front. When the meaning of the language changes based on the tonal inflection, it becomes very difficult to guess the actual meaning of a sentence.
One of the ways that computer technology is advancing in this arena is the use of an online, real-time processing of languages. High-quality software is able to compare spoken words to examples of native speakers to find the correct word for any given language.
Experience and Prediction
Humans rely on experience and prediction to anticipate what someone is saying. Computers are generally not as adept at predicting the words and phrases that are idiomatic to a culture.
This means that developers have to program this information into the software. Using a model for grammatical structure and a statistical model can help improve speech translation programs.
When a computer is confronted with a language that it can identify, but the dialect is slightly different, the program must often be adjusted to classify that dialect as another language entirely. In this way, if a program is designed to recognize Mandarin Chinese, it may only be able to recognize a very specific dialect with the Mandarin regional language.
As speech recognition software continues to develop, it will incorporate more examples from native speakers, a larger database of idiomatic sayings and an emphasis on the most commonly used words and phrases in a language. Integrating user feedback also helps a program to “learn” the preferences of a specific user. With time, the program begins to become more adept at learning how to recognize a specific users unique voice pattern.