Speaker 1: Get in tech with technology with tech Stuff from how
Speaker 1: stuff works dot com. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer with
Speaker 1: how Stuff Works in love all Things Tech and listener
Speaker 1: Nate wrote in and asked that I do an episode
Speaker 1: about personal digital assistance or virtual assistance or voice helpers.
Speaker 1: This is hard because we don't really have a great
Speaker 1: term for these things, but I'm talking about applications like
Speaker 1: and I apologize ahead of time if I activate your
Speaker 1: technology Sirie, Alexa and Google Assistant. These sort of voice
Speaker 1: helpers that can respond to voice commands as well as
Speaker 1: other means of input in a way that makes them
Speaker 1: seem almost intelligent. Now, as it turns out, that's actually
Speaker 1: a pretty complicated history because it requires a discussion about
Speaker 1: a lot of different connected ideas that we're all in
Speaker 1: dependent and then ultimately converged. We're talking about stuff like
Speaker 1: speech recognition, natural language processing, and technology that was meant
Speaker 1: to improve accessibility and a whole lot more So, it
Speaker 1: makes talking about the services somewhat challenging because it's not
Speaker 1: like there was just one pathway that led to their development.
Speaker 1: They exist largely because of these independent but converging areas
Speaker 1: of innovation. Much of the work that made these services
Speaker 1: possible took place in events that were concurrent with each other,
Speaker 1: with different organizations all working towards similar but unconnected, disconnected goals.
Speaker 1: So going by strict timeline approach would be really hard,
Speaker 1: if not impossible, just because you have to jump around
Speaker 1: a lot to talk about different advances. So today I'm
Speaker 1: going to focus solely on speech recognition. This in itself
Speaker 1: is a huge topic, so it's more than enough for
Speaker 1: a single episode of tech stuff. In the next episode,
Speaker 1: I'm going to dive more into natural language processing, which
Speaker 1: has some crossover with speech recognition, but it is its
Speaker 1: own thing. And then after that we'll take a look
Speaker 1: at how voice assistants like Sirie and Alexa popped up
Speaker 1: over time. First, the idea of creating a machine that
Speaker 1: could interpret speech is older than computers. If you listen
Speaker 1: to my episodes about the history of the turntable, you'll
Speaker 1: remember the phanatograph, designed by Eduard Leon Scott de Martinville
Speaker 1: in eighteen fifty seven. The gadget had a small brush
Speaker 1: that was attached to a parchment diaphragm and the bristles
Speaker 1: on the brush rested against a sheet of paper that
Speaker 1: itself was wrapped around a cylinder. On top of the
Speaker 1: sheet of paper was a layer of soot. So to
Speaker 1: operate the device, you would turn the cylinder, the brush
Speaker 1: would drag across the soot on the paper, and you
Speaker 1: would shout at the diaphragm. The vibrations of sound would
Speaker 1: cause the parchment diaphragm to vibrate. That would make the
Speaker 1: brush vibrate and move against the paper, and that would
Speaker 1: create a pattern corresponding to the vibrations that were made
Speaker 1: by the paper diaphragm. The phonautograph was supposed to aid
Speaker 1: in the study of language and sound. The machine itself
Speaker 1: was not intended to interpret sound, but rather facilitate interpretation.
Speaker 1: A human would take a look at these tracings essentially
Speaker 1: and be able to analyze sound, or at least that
Speaker 1: was the intent. It didn't quite work out that way,
Speaker 1: but that was the concept behind it. Now, let's set
Speaker 1: the way back machine to the nineteen fifties. In nineteen
Speaker 1: fifty two, Bell Labs created the Audrey system, which was
Speaker 1: not a mean green mother from outer space, but rather
Speaker 1: the first documented speech recognizer system. It was an analog system,
Speaker 1: not a digital one. It was its own dedicated massive circuit,
Speaker 1: and it even had vacuum tubes in this thing. Because
Speaker 1: this is before the transistor. It could recognize strings of
Speaker 1: digits spoken by its creator with about nine and accuracy.
Speaker 1: If anyone else tried it, the accuracy dropped a bit.
Speaker 1: This already shows that speech recognition is tough because not
Speaker 1: everyone says things exactly the same way. I know that's
Speaker 1: not a news flash, but it is important for the
Speaker 1: concept of speech recognition. Uh. You also had to pause
Speaker 1: between strings of numbers. You couldn't just rattle off conversationally.
Speaker 1: You had to put pauses in there. But it was
Speaker 1: also an enormous piece of machinery. It took up a
Speaker 1: six ft high relay rack and it consumed a lot
Speaker 1: of electricity. Then Big Blue, also known as IBM, had
Speaker 1: scientists and engineers working on the possibility of designing technologies
Speaker 1: that could recognize speech. They were kind of working around
Speaker 1: the same time that Bell South was computer scientists Nathaniel Rochester,
Speaker 1: who designed an IBM computer called the seven oh one.
Speaker 1: He also wrote the first assembler. Headed up a group
Speaker 1: of engineers at IBM who were researching pattern recognition and
Speaker 1: information theory. That work, which was early research into fundamental
Speaker 1: building blocks for artificial intelligence, would also become important for
Speaker 1: speech recognition. In the late nineteen fifties, William C. Dirsh,
Speaker 1: another IBM computer scientist, developed a computer system as part
Speaker 1: of IBMS Advanced Systems Development Division laboratory, and it incorporated
Speaker 1: basic elements of speech recognition. He unveiled the device, called
Speaker 1: the IBM Shoebox in nineteen sixty two at the World's Fair.
Speaker 1: Using a microphone, you could speak basic digits from zero
Speaker 1: to nine, and also six additional control words like plus
Speaker 1: or minus, and the shoebox would recognize the words and
Speaker 1: perform calculation, So essentially this was a basic voice controlled calculator.
Speaker 1: While the application was limited, this showed off a remarkable achievement.
Speaker 1: Finding a way to program a machine to accept speech
Speaker 1: as a command is a non trivial problem. Throughout the
Speaker 1: nineteen sixties, computer scientists took a brute force sort of
Speaker 1: approach to solving speech recognition, which could work in very
Speaker 1: narrow applications such as the calculator approach, but were by
Speaker 1: their nature difficult to scale up. Even in the early
Speaker 1: nineteen seventies, the Speech Understanding Research Project from our PA
Speaker 1: as the same organization that would help bring the Internet
Speaker 1: into being, produced a brute force template called Harpy. While
Speaker 1: it was reliant upon brute force, Harpy, which came out
Speaker 1: of Carnegie Melon Research, could recognize about one thousand words.
Speaker 1: Harpy also made use of a process called beam search.
Speaker 1: This is a search strategy in which a search algorithm
Speaker 1: can consider multiple possible hits at a single time, rather
Speaker 1: than looking through a large data set for a specific
Speaker 1: perfect hit. Then the algorithm would determine the probability of
Speaker 1: each of the hits as being the right word. The
Speaker 1: number of potential hits is determined by a value called
Speaker 1: the beam width, setting the speech recognition and application designer
Speaker 1: can set. Beam search is a much more efficient way
Speaker 1: to suss out speech, and it's frequently used today, not
Speaker 1: just in speech recognition but also in natural language process
Speaker 1: saying another sequential models, but it gets super technical, so
Speaker 1: we're gonna leave it at that kind of high level approach.
Speaker 1: But these systems still mapped all words to a template,
Speaker 1: one template per word. It didn't break words up into sounds,
Speaker 1: but look for a match against a database of established
Speaker 1: vocabulary words, which meant that if you did not pronounce
Speaker 1: the word the same way as it was represented in
Speaker 1: the database, you might not get a hit. You would
Speaker 1: have to get it close enough to that template for
Speaker 1: you to be able to get a hit. This is
Speaker 1: a big problem. People speak with accents or dialects, or
Speaker 1: they may have difficulty replicating certain sounds. The brute force
Speaker 1: approach often meant you you'd have to say the same
Speaker 1: word a few times with clear enunciation and long pauses
Speaker 1: to get a hit. And again, it just didn't scale
Speaker 1: very well. It wasn't until the late nineteen seventies that
Speaker 1: computer scientists were able to find a different approach that
Speaker 1: would power more modern speech recognition systems. And let's go
Speaker 1: through some of the steps that are necessary, from the
Speaker 1: basic physical attributes of speech to the processing of the information. First, speech,
Speaker 1: like all sound, ultimately is a physical phenomenon. It is vibration.
Speaker 1: We produce these vibrations with vocal cords and our lips, teeth,
Speaker 1: and tongue according to the rules of whatever language we
Speaker 1: are speaking. These vibrations travel through a medium such as
Speaker 1: the air, and then they get picked up by something else,
Speaker 1: like someone else's ears or a microphone or whatever. But
Speaker 1: at this stage we're talking about physical vibrations and analog
Speaker 1: form of input. Computers do not directly interpret physical vibrations.
Speaker 1: Computers process digital information, and speech is an analog phenomena.
Speaker 1: So the first thing we need for a computer to
Speaker 1: recognize speech is for some sort of analog to digital
Speaker 1: converter that can accept the analog information and then translated
Speaker 1: into digital information. The a d C would typically sample
Speaker 1: speech by taking precise measurements of the sound at frequent
Speaker 1: intervals or samples such as thousands of times per second,
Speaker 1: so you can almost think of it like snapshots, Like
Speaker 1: like pictures. The a d C is measuring quantifiable elements
Speaker 1: of the sound every time it takes a sample. That
Speaker 1: might include stuff like amplitude and frequency, or volume and pitch.
Speaker 1: If you're talking about how we perceive sound. There's usually
Speaker 1: some sort of noise filter incorporated into this step as
Speaker 1: well to help remove any unwanted sounds from the signal.
Speaker 1: The system has to be able to recognize which signals
Speaker 1: represent a command in which ones are not important. This
Speaker 1: is why I can do stuff like send vocal commands
Speaker 1: to a voice assistant, even if there's another conversation going
Speaker 1: on nearby, or if I have the radio or television on. Now.
Speaker 1: I have a lot more to say about the technology
Speaker 1: that makes speech recognition possible, but before I get into that,
Speaker 1: let's take a quick break to thank our sponsor. So,
Speaker 1: a speech recognition system typically as a database of sound
Speaker 1: samples that will allow the recognition system to compare incoming
Speaker 1: signals against that database. The speech recognition system might have
Speaker 1: to put the incoming sound through a process called temporal alignment,
Speaker 1: which is a fancy way of saying the system might
Speaker 1: have to slow down or speed up the incoming sound.
Speaker 1: You can think of this as like making a recording
Speaker 1: and then almost immediately playing the recording back. Obviously, the
Speaker 1: speech recognition system can't change the speed at which you're speaking,
Speaker 1: though you might get a feature that prompts you to
Speaker 1: slow down or speed up if the message may say
Speaker 1: could you say that again, but slower that kind of thing. Um.
Speaker 1: If you happen to be someone from the Northeastern United States,
Speaker 1: for example, you may frequently get these messages saying slow
Speaker 1: the heck down. Temporal alignment allows the speech recognition system
Speaker 1: to look for matches between the incoming sound and the
Speaker 1: samples in the system's memory. The system must also do
Speaker 1: gied up the sounds in the incoming signal into segments
Speaker 1: that represent specific sounds in the native language, such as
Speaker 1: the sound or the hard to sound. It looks for
Speaker 1: matches in its memory that represent phonemes, and a phoneme
Speaker 1: is a basic sound native to a specific language, to
Speaker 1: a particular language, whichever when you're looking at. So, for example,
Speaker 1: the English language has about forty phonemes. Linguists actually get
Speaker 1: into some pretty vicious fights about exactly how many phonemes
Speaker 1: English language has, but it's around forties. Some people argue
Speaker 1: that there are more phonemes, some say that there are.
Speaker 1: Some of the supposed additional phonemes are in fact repeats
Speaker 1: of existing ones. Other languages, though, will have different number
Speaker 1: of phonemes in them. Some may have far more than English,
Speaker 1: some may have fewer than English. The system then has
Speaker 1: to analyze the phonemes in sequence. So it's looking at
Speaker 1: these little markers that represent different sounds, and this is
Speaker 1: how it says them can look for matches between a
Speaker 1: series of phonemes and the words that it can recognize
Speaker 1: it can try and build words from these sounds. This
Speaker 1: is way harder than I'm making it. Sound speech recognition
Speaker 1: systems have complicated statistical models to help them determine what
Speaker 1: a word might be. Even a simple speech recognition system
Speaker 1: will have a complex statistical model to recognize individual words.
Speaker 1: More sophisticated systems might also look at contextual information surrounding
Speaker 1: the phonemes. In other words, a really sophisticated system isn't
Speaker 1: just looking for a match in phonemes to sus out
Speaker 1: what a single word is in a sentence. It's looking
Speaker 1: at the phonemes that came before and after to determine
Speaker 1: what those words were and to help increase the confidence
Speaker 1: level overall. So let me give an example. Let's say
Speaker 1: have activated one of these voice assistants, and I've used
Speaker 1: whatever voice command activates it. I'm not going to do
Speaker 1: it here because some of you might be listening on
Speaker 1: those devices. And then I say turn the volume up
Speaker 1: thirty percent. The speech recognition system begins to parse what
Speaker 1: I said by analyzing those sounds phone name by phone name,
Speaker 1: identifying them, analyzing them, trying to group them together to
Speaker 1: form words, and when it thinks it's found a word,
Speaker 1: it assigns a certain probability to that, and when it
Speaker 1: starts to analyze the phone names that make up the
Speaker 1: word volume, it's also looking at the words that came
Speaker 1: before turn the and it's looking at the words that
Speaker 1: came after up. That boosts the system's confidence overall that
Speaker 1: the keyword volume is in fact volume, and then it
Speaker 1: does what I told it to do. When I talk
Speaker 1: about confidence, I don't mean the system feels good about itself.
Speaker 1: I'm talking about probabilities. These systems largely work in the
Speaker 1: realm of probabilities. What is the probability that I said
Speaker 1: volume rather than some other word. For speech recognition system
Speaker 1: to work, it needs to be able to assign a
Speaker 1: confidence level towards. The higher the level, the more certain
Speaker 1: quote unquote the system is that it got things correct.
Speaker 1: Typical computer engineers will design systems that will only execute
Speaker 1: a command or return a result of some sort if
Speaker 1: the system has reached a certain threshold of confidence, and
Speaker 1: if it hasn't, you won't get a result. So, for example,
Speaker 1: and this isn't about speech recognition exactly, but it illustrates
Speaker 1: my point. IBM S Watson computer would not offer up
Speaker 1: an answer on Jeopardy unless it met a certain threshold
Speaker 1: of confidence in an answer, and I think it was
Speaker 1: about eight percent. So if it or eight percent certain
Speaker 1: that it had the right answer, it would buzz in.
Speaker 1: But if it was less than eight sure, it would
Speaker 1: not put forth that answer. There are two broad types
Speaker 1: of statistical models in speech recognition systems today. There are
Speaker 1: others that could be used, but there are two broad
Speaker 1: ones that tend to be used these days. They are
Speaker 1: the hidden Markov model and neural networks. Hidden Markov model,
Speaker 1: by the way, is overwhelmingly the most popular method of
Speaker 1: using a statistical model to analyze speech recognition. It is
Speaker 1: the prevalent approach, and it works sort of how I
Speaker 1: just described. It looks at each phone name and starts
Speaker 1: to build out a pathway. If you think of this
Speaker 1: as like an actual physical path that you're following, you
Speaker 1: would start off with the first phone name that represents
Speaker 1: the beginning of the path, and the phone name might
Speaker 1: eliminate other possible phone names right away. By that, I
Speaker 1: mean it might be a sound that doesn't combine with
Speaker 1: certain other sounds within that language. There might be a
Speaker 1: phone name that does not combine with other specific phone names.
Speaker 1: So imagine you have a path and originally it splits
Speaker 1: into tons of other pathways, but a couple of those
Speaker 1: pathways are blocked off with signs that say the pathway
Speaker 1: is closed. It's closed because those pathways represent phone names
Speaker 1: that would never be paired with the initial one. You
Speaker 1: just don't get that sound in English. The closed paths
Speaker 1: would therefore be off limits, and only the open paths
Speaker 1: would be the possibility. Then the hidden Markov model would
Speaker 1: look at the next phone name the next step along
Speaker 1: this pathway. That phone name determines which of the viable
Speaker 1: path options is actually the one to follow. All the
Speaker 1: other options would be discarded, and so on. It would
Speaker 1: go all the way down the list of phone names
Speaker 1: until the model arrives at a conclusion of the most
Speaker 1: likely word that was spoken. It assigns a probability score
Speaker 1: to each phone names, thinking I'm pretty sure the sound
Speaker 1: that I heard, quote unquote was this. That helps the
Speaker 1: system make an educated guess as to what word was
Speaker 1: actually spoken. Now, I've talked a lot about neural networks
Speaker 1: in the past. I'm just going to give it a
Speaker 1: quick cursory covering here, because they really aren't the dominant
Speaker 1: statistical model in speech recognition. UH Neural networks have nodes,
Speaker 1: computer nodes or algorithms that act like a neuron right
Speaker 1: like a like a brain cell, and they execute operations
Speaker 1: on data. The neurons also assigned a probability score to
Speaker 1: that x that execution of of data and shows the
Speaker 1: confidence in the system in the result before they pass
Speaker 1: it on to another neuron in the network, which then
Speaker 1: executes another operation on the data and so on, and
Speaker 1: ultimately the network produces an end result of all those
Speaker 1: operations and judges the probability of whether or not that
Speaker 1: result is the right one, and again, if it meets
Speaker 1: a certain threshold, then it's considered the correct answer or
Speaker 1: the closest to correct that the system can manage. In
Speaker 1: any case, speech recognition systems have to be trained, and
Speaker 1: there are trillions of potential combinations of sounds that could
Speaker 1: represent different words. And the How stuff Works article How
Speaker 1: Speech Recognition Works Ed Grabanowski, who is one of the
Speaker 1: powerhouses of the site. He's written some of the best
Speaker 1: articles on how stuff Works, gave a great example. He says,
Speaker 1: take the phrase recognize speech right the phone emes in
Speaker 1: that phrase happened to be pretty similar to a totally
Speaker 1: different phrase, which would be recognized beach. So you have
Speaker 1: recognized speech or wreck a nice beach. The speech recognition
Speaker 1: software has to be able to determine the difference, or
Speaker 1: else the next thing you know, you're gonna have terminators
Speaker 1: kicking sand in everyone's face, and that's no good. Alexander Wibel,
Speaker 1: who worked on that system called Harpy that I mentioned earlier,
Speaker 1: had another couple of examples. He said, you might say
Speaker 1: youth and Asia and get the result youth in Asia.
Speaker 1: Or you might say give me a new display and
Speaker 1: you get the result, give me a newdist play. If
Speaker 1: you've ever used something like Google transcripts, where if you
Speaker 1: had a Google Voice and you were reading the voicemails,
Speaker 1: you could get hilarious results. Because of this, the speech recognition,
Speaker 1: the speech to text feature could end up spelling out
Speaker 1: truly ridiculous messages. I would get messages from my mother,
Speaker 1: and I only wish my mom would leave me messages
Speaker 1: the way that Google transcript thought she was leaving me messages,
Speaker 1: because they were the most crazy messages ever. But it's
Speaker 1: mostly because my mom has a Southern accent and so
Speaker 1: Google would often misinterpret what she was saying, so these
Speaker 1: systems have to undergo hours of training. John Garofolo, a
Speaker 1: computer scientist who was cited in that House Stuff Works article,
Speaker 1: had this to say. These statistical systems need lots of
Speaker 1: exemplary training data to reach their optimal performance, sometimes on
Speaker 1: the order of thousands of hours of human transcribed speech
Speaker 1: and hundreds of megabytes of text. These training data are
Speaker 1: used to create acoustic models of words, word lists, and
Speaker 1: multi word probability networks. There is some art into how
Speaker 1: one selects, compiles, and prepares this training data for digestion
Speaker 1: by the system, and how the system models are tuned
Speaker 1: to a particular application. These details can make the difference
Speaker 1: between a well performing system and a poorly performing system,
Speaker 1: even when using the same basic algorith Rhythm speech recognition
Speaker 1: also requires a decent amount of processing power. This was
Speaker 1: a limiting factor on speech recognition for a really long time.
Speaker 1: Systems were limited in their capabilities, which meant that for years,
Speaker 1: if you wanted to incorporate speech recognition in a computer system,
Speaker 1: and then most of the computer's processing power would have
Speaker 1: to dedicate itself just to parsing speech. You couldn't do
Speaker 1: much else on that machine. But since Moore's laws held
Speaker 1: up so well for decades, we got to a point
Speaker 1: where the process and capabilities of machines reached a stage
Speaker 1: where this isn't as big a concern, And another development
Speaker 1: that Google really helped pioneer definitely change things. I'll talk
Speaker 1: more about that in our next section, but first let's
Speaker 1: take another quick break to thank our sponsors. Okay, So,
Speaker 1: advances in speech recognition in the late nineteen seventies paved
Speaker 1: the way from how most systems work these days, though
Speaker 1: of course the models have under gone multiple refinements and
Speaker 1: tweaking over time. The first speech recognition product to ever
Speaker 1: launch for consumers was a program called Dragon Dictate, which
Speaker 1: debuted in Dragon Dictate. The original version that is, because
Speaker 1: they still come out to this day, relied on discrete
Speaker 1: speech recognition. Now, I don't mean you had to be
Speaker 1: secretive and hush hush about it. It's not that kind
Speaker 1: of discreet. Rather, I mean you had to pronounce each
Speaker 1: word clearly, with a pause between words. You could not
Speaker 1: speak conversationally, or the dictation software could not interpret what
Speaker 1: you were saying, so using the software would sound like this.
Speaker 1: It was limited and it was primitive compared to today's
Speaker 1: speech recognition products, but it was a groundbreaking product in
Speaker 1: the early nineties. And it also costs somewhere between six
Speaker 1: thousand and nine thousand dollars I saw differing accounts, but
Speaker 1: that would be between nine and fourteen grand in today's dollars,
Speaker 1: so pretty expensive software package. Dragon still produces speech recognition
Speaker 1: technologies to this day, and of course they are much
Speaker 1: more adept at recognizing and transcribing speech than the original
Speaker 1: version was years ago. The software is also less expensive.
Speaker 1: One version I saw retails for less than a hundred dollars,
Speaker 1: so nice. Big deep price cut advancements and model design
Speaker 1: and processor speed meant that speech recognition technology advanced rather quickly.
Speaker 1: In Bell South released Vowel v a L. The Voice
Speaker 1: Portal VAL was an automated interactive system that could respond
Speaker 1: to questions over the phone. This was a basic implementation
Speaker 1: that would evolve over time to the systems you may
Speaker 1: have encountered when calling up automated menus where it's a
Speaker 1: press three or say three and that kind of thing,
Speaker 1: or do you have any questions? You can say anything
Speaker 1: from check my balance to you know, that kind of stuff.
Speaker 1: In two thousand five DARPA, which is the same brand
Speaker 1: each of the Department of Defense that used to be
Speaker 1: known as ARPA, So in other words, it's the same
Speaker 1: R and d ARM that funded the creation of the Internet.
Speaker 1: They funded a program in two thousand five called the
Speaker 1: Global Autonomous Language Exploitation Project or GALE. The purpose of
Speaker 1: this project was to advance research and development into automated
Speaker 1: translation between languages. So not only were computers supposed to
Speaker 1: be able to recognize speech, but also translate that speech
Speaker 1: from one language into another, which adds another layer of
Speaker 1: complexity on top. Right well, according to s r I International,
Speaker 1: the system should be able to quote automatically take multi
Speaker 1: lingual newscasts, text documents, and other forms of communication and
Speaker 1: make their information available to human queries end quote. So
Speaker 1: wouldn't just translate the information, which was already even more
Speaker 1: complicated than speech recognition, It could also index that information
Speaker 1: in a meaningful way so you could search for stuff.
Speaker 1: So layer upon layer of complexity for that project. Things
Speaker 1: that helped push speech recognition as well as natural language
Speaker 1: processing to new heights largely came from two competing companies,
Speaker 1: Apple and Google. So let me explain that In two
Speaker 1: thousand seven, Apple introduced the iPhone, which was the first
Speaker 1: truly successful consumer smartphone, especially here in the United States.
Speaker 1: The smartphone introduced a new era and form of computing.
Speaker 1: It created countless opportunities in numerous areas, including location based computing,
Speaker 1: mobile interactions, and speech recognition. The computer was in a
Speaker 1: phone form factor. Phones are designed for us to talk into,
Speaker 1: So now you can walk around carrying a computer that
Speaker 1: was designed to transmit your voice. It's only a matter
Speaker 1: of time before someone figured out a way to leverage
Speaker 1: that for speech recognition. Google meanwhile, was pioneering an approach
Speaker 1: in what would perform all the processing functions necessary to
Speaker 1: support speech recognition. It was doing it in the cloud,
Speaker 1: so instead of having the device itself have to run
Speaker 1: all that processing power, the device would have a persistent
Speaker 1: connection to a server on the Internet, and the server
Speaker 1: would do the work. It would just send the signal
Speaker 1: to the server. The server would process and analyze the
Speaker 1: signal and return the result back to the phone, and
Speaker 1: the phone just was acting as a transmitter. It wasn't
Speaker 1: really having to do any of that analysis itself. So
Speaker 1: in two thousand and eight, Google launched the Google Voice
Speaker 1: search app for the iPhone that would do all the
Speaker 1: this uh speech recognition processing. Right, you could speak into
Speaker 1: it and have Google search the terms for you for
Speaker 1: whatever it was you were saying. But again, what was
Speaker 1: really going on was that Google was sending those search
Speaker 1: terms or that that speech signal over to a server
Speaker 1: that Google operated, and then send the results back down
Speaker 1: to the phone. But to the user it looked like
Speaker 1: the phone itself was doing all the work. The truth
Speaker 1: was it was simply a very basic application of true
Speaker 1: cloud computing, and that created a new method of rolling
Speaker 1: out speech recognition and apps and services. No longer did
Speaker 1: you have to worry about creating a really powerful piece
Speaker 1: of equipment. You can have that be on the back end.
Speaker 1: The piece of equipment the user could have could be
Speaker 1: a relatively underpowered terminal. Essentially. Meanwhile, it also meant that
Speaker 1: Google could collect enormous samples of data, not necessarily to
Speaker 1: market to people or to identify specific individual but rather
Speaker 1: it could collect a lot of data for training its
Speaker 1: speech recognition and natural language recognition models. Google could build
Speaker 1: out a much more robust model of human speech patterns
Speaker 1: because they had thousands of real world uses going on
Speaker 1: in real time they could keep using that to build
Speaker 1: out and bolster their models, and that improved Google's speech
Speaker 1: recognition accuracy. Today, major speech recognition platforms typically have an
Speaker 1: error rate below five percent, which is pretty darn impressive.
Speaker 1: According to a calm score estimation, by twenty half of
Speaker 1: all searches on the Internet will be voice searches. So
Speaker 1: speech recognition, along with natural language processing, could lead to
Speaker 1: a future of ambient computing in which the environments we
Speaker 1: move through our effectively computer interfaces, and we can access
Speaker 1: them through voice commands and other ways of commanding, maybe
Speaker 1: gesture commands, but that seems like it might be better,
Speaker 1: say for our episode about voice assistance and where we're
Speaker 1: headed with that technology. In our next episode, I'm going
Speaker 1: to really explore natural language processing, how it works, and
Speaker 1: how that field of research has evolved over the last
Speaker 1: few decades. It's also really fascinating, and it does, in
Speaker 1: fact cross over quite a bit with speech recognition. But
Speaker 1: natural language processing goes beyond speech. It also includes text,
Speaker 1: and that will be our next episode. But if you
Speaker 1: have a suggestion for a future topic I should cover
Speaker 1: on tech Stuff, send me a message let me know
Speaker 1: about it. The email for the show is tech stuff
Speaker 1: at how stuff works dot com, or you can drop
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Speaker 1: can also follow us on Instagram. I would love it
Speaker 1: if you did, and I'll talk to you again really soon.
Speaker 1: For more on this and bouthsands of other topics, is
Speaker 1: it how stuff works dot com