Small-vocabulary/many-users
These systems are ideal for automated telephone answering. The users can speak with a great deal of variation in accent and speech patterns, and the system will still understand them most of the time. However, usage is limited to a small number of predetermined commands and inputs, such as basic menu options or numbers.
Large-vocabulary/limited-users
These systems work best in a business environment where a small number of users will work with the program. While these systems work with a good degree of accuracy (85 percent or higher with an expert user) and have vocabularies in the tens of thousands, you must train them to work best with a small number of primary users. The accuracy rate will fall drastically with any other user.
Speech recognition systems made more than 10 years ago also faced a choice between discretecontinuous and speech. It is much easier for the program to understand words when we speak them separately, with a distinct pause between each one. However, most users prefer to speak in a normal, conversational speed. Almost all modern systems are capable of understanding continuous speech.
Speech to data:
To convert speech to on-screen text or a computer command, a computer has to go through several complex steps. When you speak, you create vibrations in the air. The analog-to-digital converter (ADC) translates this analog wave into digital data that the computer can understand. To do this, it samples, or digitizes, the sound by taking precise measurements of the wave at frequent intervals. The system filters the digitized sound to remove unwanted noise, and sometimes to separate it into different bands of frequency (frequency is the wavelength of the sound waves, heard by humans as differences in pitch). It also normalizes the sound, or adjusts it to a constant volume level. It may also have to be temporally aligned. People don't always speak at the same speed, so the sound must be adjusted to match the speed of the template sound samples already stored in the system's memory.
Next the signal is divided into small segments as short as a few hundredths of a second, or even thousandths in the case of plosive consonant sounds -- consonant stops produced by obstructing airflow in the vocal tract -- like "p" or "t." The program then matches these segments to known phonemes in the appropriate language. A phoneme is the smallest element of a language -- a representation of the sounds we make and put together to form meaningful expressions. There are roughly 40 phonemes in the English language (different linguists have different opinions on the exact number), while other languages have more or fewer phonemes.
The next step seems simple, but it is actually the most difficult to accomplish and is the is focus of most speech recognition research. The program examines phonemes in the context of the other phonemes around them. It runs the contextual phoneme plot through a complex statistical model and compares them to a large library of known words, phrases and sentences. The program then determines what the user was probably saying and either outputs it as text or issues a computer command.
Speech Recognition and Statistical Modeling:
Early speech recognition systems tried to apply a set of grammatical and syntactical rules to speech. If the words spoken fit into a certain set of rules, the program could determine what the words were. However, human language has numerous exceptions to its own rules, even when it's spoken consistently. Accents, dialects and mannerisms can vastly change the way certain words or phrases are spoken. Imagine someone from Boston saying the word "barn." He wouldn't pronounce the "r" at all, and the word comes out rhyming with "John." Or consider the sentence, "I'm going to see the ocean." Most people don't enunciate their words very carefully. The result might come out as "I'm goin' da see tha ocean." They run several of the words together with no noticeable break, such as "I'm goin'" and "the ocean." Rules-based systems were unsuccessful because they couldn't handle these variations. This also explains why earlier systems could not handle continuous speech -- you had to speak each word separately, with a brief pause in between them.
Today's speech recognition systems use powerful and complicated statistical modeling systems. These systems use probability and mathematical functions to determine the most likely outcome. According to John Garofolo, Speech Group Manager at the Information Technology Laboratory of the National Institute of Standards and Technology, the two models that dominate the field today are the Hidden Markov Model and neural networks. These methods involve complex mathematical functions, but essentially, they take the information known to the system to figure out the information hidden from it.
This process is even more complicated for phrases and sentences -- the system has to figure out where each word stops and starts. The classic example is the phrase "recognize speech," which sounds a lot like "wreck a nice beach" when you say it very quickly. The program has to analyze the phonemes using the phrase that came before it in order to get it right. Here's a breakdown of the two phrases:
r eh k ao g n ay z s p iy ch
"recognize speech"
r eh k ay n ay s b iy ch
"wreck a nice beach"
That help comes in the form of program training. According to John Garofolo:
These statistical systems need lots of exemplary training data to reach their optimal performance -- sometimes on the order of thousands of hours of human-transcribed speech and hundreds of megabytes of text. These training data are used to create acoustic models of words, word lists, and [...] multi-word probability networks. There is some art into how one selects, compiles and prepares this training data for "digestion" by the system and how the system models are "tuned" to a particular application. These details can make the difference between a well-performing system and a poorly-performing system -- even when using the same basic algorithm.
While the software developers who set up the system's initial vocabulary perform much of this training, the end user must also spend some time training it. In a business setting, the primary users of the program must spend some time (sometimes as little as 10 minutes) speaking into the system to train it on their particular speech patterns. They must also train the system to recognize terms and acronyms particular to the company. Special editions of speech recognition programs for medical or legal offices have terms commonly used in those fields already trained into them.
