Speech Recognition

Today, when we call most large companies, a person doesn't usually answer the phone. Instead, an automated voice recording answers and instructs you to press buttons to move through option menus. Many companies have moved beyond requiring you to press buttons, though. Often you can just speak certain words (again, as instructed by a recording) to get what you need. The system that makes this possible is a type of speech recognition program -- an automated phone system.

You can also use speech recognition software in homes and businesses. A range of software products allows users to dictate to their computer and have their words converted to text in a word processing or e-mail document. You can access function commands, such as opening files and accessing menus, with voice instructions. Some programs are for specific business settings, such as medical or legal transcription.

Current programs fall into two categories:

Small-vocabularies/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-vocabularies/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 discrete and continuous 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. 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. 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 focus of most speech recognition research. The program examines phonemes in the context of the other phonemes around them 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: Weaknesses and Flaws

No speech recognition system is 100 percent perfect; several factors can reduce accuracy. Some of these factors are issues that continue to improve as the technology improves. Others can be lessened -- if not completely corrected -- by the user.

The program needs to " hear" the words spoken distinctly, and any extra noise introduced into the sound will interfere with this. The noise can come from a number of sources, including loud background noise in an office environment. Users should work in a quiet room with a quality microphone positioned as close to their mouths as possible. Low-quality sound cards, which provide the input for the microphone to send the signal to the computer, often do not have enough shielding from the electrical signals produced by other computer components. They can introduce hum or hiss into the signal.

Overlapping speech. Current systems have difficulty separating simultaneous speech from multiple users. If you try to employ recognition technology in conversations or meetings where people frequently interrupt each other or talk over one another, you're likely to get extremely poor results.

Intensive use of computer power. Running the statistical models needed for speech recognition requires the computer's processor to do a lot of heavy work. One reason for this is the need to remember each stage of the word-recognition search in case the system needs to backtrack to come up with the right word. The fastest personal computers in use today can still have difficulties with complicated commands or phrases, slowing down the response time significantly. The vocabularies needed by the programs also take up a large amount of hard drive space. Fortunately, disk storage and processor speed are areas of rapid advancement -- the computers in use 10 years from now will benefit from an exponential increase in both factors.

The potential problems with using speech recognition were on public display recently in a Windows Vista demonstration. While the system performed flawlessly at opening programs and accessing documents, when it came to transcribing text, it wasn't very accurate. The problems likely stemmed from the background noise and echo present in the large auditorium with an audience where the demo took place. A video of the incident soon spread across the Internet, hurting the reputations of Windows Vista and speech recognition in general.

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