How does voice recognition software work? Advertisement. by Chris Woodford. Last updated: December 2. It's just as well people can understand speech. Imagine if you. were like a computer: friends would have to "talk" to you by. If you wanted to say "hello" to someone, you'd. Conversations would be a long, slow, elaborate nightmare—a. We'd never put up with such clumsiness as humans, so why do we talk. Scientists have long dreamed of building machines that. · Voice-recognition software is nothing new. But put it on a smartphone, and it comes to life. All of the frustrations of trying to control your PC by voice. But although computerized. PCs, few of us actually use it. Why? Possibly because we never even bother to try it out, working on the. It's certainly true that speech. How. well are they doing at cracking the problem? Will we all be chatting. PCs one day soon? Let's take a closer look and find out! I down-loaded expressscribe from NCH (u need speech recognition already set up -USE ID 0) -options, speech-to-text. I used a sync folder (read the help from the.Windows also offers free, built-in voice dictation with Speech Recognition. Available on all versions of Windows since XP, Speech Recognition lets you type and use. Dragon Medical Speech Recognition Software is designed with smaller practices in mind to help clinicians accelerate adoption of their chosen EHR. Photo: Using a headset microphone like this makes a huge difference to the accuracy of speech recognition: it reduces background sound, making it much easier. Getting your head around speech. Photo: Speech recognition has been popping up all over the place for quite a few years now. This i. Pod Touch has a built- in "voice control" program that let you pick out music just by saying "Play albums by U2," or whatever band you're in the mood for. Language sets people far above our creeping, crawling animal. While the more intelligent creatures, such as dogs and. With just a couple of dozen. When we speak, our voices generate little sound packets called. Although you've probably never heard. LEGO™ blocks of sound that all words are built from. Although the difference between phones and phonemes is complex and. Computers and. computer models can juggle around with phonemes, but. When we listen to speech, our ears catch phones flying. Instant, easy, and quite dazzling, our amazing brains make. And it's perhaps because listening. If only it were that simple! Why is speech so hard to handle? The trouble is, listening is much harder than it looks (or. When someone speaks to you in the street, there's the sheer. When people talk quickly, and run all their words together in a long stream, how do we know. Did they just. say "dancing and smile" or "dance, sing, and smile"?)There's the problem of how everyone's voice is a little bit. How. do our brains figure out that a word like "bird" means exactly. What about words like "red" and "read" that sound identical but mean totally different things (homophones. How does our brain know which word the speaker. What about sentences that are misheard to mean radically. There's the age- old military example of "send. I always chuckle when I hear Kate Bush singing about "the. On top of all that stuff, there are issues like. Weighing up all these factors, it's easy to. It shouldn't surprise or disappoint us that computers struggle to. How do computers recognize speech? Speech recognition is one of the most complex areas of computer. If. you read through some of the technical and scientific papers that have been published. My. objective is to give a rough flavor of how computers recognize. I'm going to simplify. Broadly speaking, there are four different approaches a computer. Simple pattern matching (where each spoken word is recognized in its. Pattern and feature. Language modeling and statistical analysis (in which a knowledge of grammar and the. Artificial neural networks (brain- like computer models that can reliably. In practice, the everyday speech recognition we encounter in things. Siri and Cortana) combines a variety. For the purposes of understanding clearly how things work. Simple pattern matching. Ironically, the simplest kind of speech recognition isn't really. You'll have encountered it if you've ever. Utility companies often have systems like this that you. You simply dial a number, wait for a. Crucially, all you ever get to do is choose one. In other words, systems like this aren't really recognizing speech at all: they simply have to be able to distinguish. Touch- Tone phone keypad. DTMF) or the spoken sounds of your voice. From a computational point of view, there's not a huge difference. It's true that there can be quite a bit of variability in how different people say. And if the system can't. Photo: Voice- activated dialing on cellphones is little. You simply train the phone to recognize the spoken. When you say a name, the phone doesn't do any particularly sophisticated analysis. Pattern and feature analysis. Automated switchboard systems generally work very reliably because. The vocabulary that a speech. Early speech. systems were often optimized to work within very specific domains. Much like humans, modern. How do they. do it? Most of us have relatively large vocabularies, made from hundreds. Theoretically, you could train a speech recognition system to. The trouble with this approach is that it's hugely inefficient. Why learn to recognize every word in the dictionary when all those. No- one wants to. So what's the alternative? How do humans. do it? We don't need to have seen every Ford, Chevrolet, and Cadillac. In much the same way, we don't need. Earth read every word in the dictionary. Speech recognition systems take the same approach. The recognition process. Practical speech recognition systems start by listening to a chunk. The first step involves digitizing the sound (so the. A/D) converter. (for a basic introduction, see our article on analog. The. digital data is converted into a spectrogram (a graph. Fast Fourier Transform (FFT)). These are digitally. Assuming we've separated the utterance into words. Probably is always the word in speech. Seeing speech. Speech recognition programs start by turning utterances into a spectrogram. It's a three- dimensional graph. Time is shown on the horizontal axis, flowing from left to right. Frequency is on the vertical axis, running from bottom to top. The color of the chart at each point shows how much energy there is in each frequency of the sound at a given moment. In this example, I've sung three distinct tones into a microphone, each one lasting about 5–1. The first one, shown by the small red area on the left, is the trace for a quiet, low- frequency sound. That's why the graph shows dark colors (reds and purples) concentrated in the bottom of the screen. The second tone, in the middle, is a similar tone to the first but quite a bit louder (which is why the colors appear a bit brighter). The third tone, on the right, has both a higher frequency and intensity. So the trace goes higher up the screen (higher frequencies) and the colors are brighter (more energy). With a fair bit of practice, you could recognize what someone is saying just by looking at a diagram like this; indeed, it was once believed that deaf and hearing- impaired people might be trained to use spectrograms to help them decode words they couldn't hear. In theory, since spoken languages are built from only a few dozen. English uses about 4. Spanish has only about 2. Instead of having to recognize the sounds of. This method of analyzing. Most speech recognition programs get better as you use them. If you've ever used a program like one of the. Dragon dictation systems, you'll be familiar with the way you have to. If you don't correct mistakes, the program. If you force the. Screenshot: With speech dictation programs like Dragon Naturally. Speaking, shown here. Statistical analysis. In practice, recognizing speech is much more complex than simply. Speech is extremely variable: different people speak in different ways (even though we're all. You don't always pronounce a certain word in exactly the same way; even if you did, the way you spoke a word. As a speaker's vocabulary grows, the number of similar- sounding. So recognizing numbers is a tougher job for. PC, with a general 5. The more speakers a system has to. For something like an. The basic principle of. In other words, we need to use what's. When people speak, they're not simply muttering a series of random. Every word you utter depends on the words that come before or. For example, unless you're a contrary kind of poet, the word. Rules of grammar make it unlikely that a noun like "table". If a computer is trying to. So it can use the rules of grammar to. If it's already identified a "g" sound instead of a "b". Virtually all modern speech recognition systems also use a bit of. The probability of one phone following another, the probability of. Ultimately, the system builds what's called a. Markov model. (HMM) of each speech segment, which is the computer's best guess at. It's called a. Markov model (or Markov chain), for Russian mathematician.
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