Tech Solution

Podcast: AI finds its voice

Immediately’s voice assistants are nonetheless a far cry from the hyper-intelligent pondering machines we’ve been musing about for many years. And it’s as a result of that expertise is definitely the mixture of three completely different abilities: speech recognition, pure language processing and voice technology.

Every of those abilities already presents big challenges. To be able to grasp simply the pure language processing half? You just about should recreate human-level intelligence. Deep studying, the expertise driving the present AI growth, can prepare machines to develop into masters in any respect kinds of duties. However it could possibly solely be taught separately. And since most AI fashions prepare their skillset on 1000’s or tens of millions of current examples, they find yourself replicating patterns inside historic knowledge—together with the numerous unhealthy selections folks have made, like marginalizing folks of colour and girls.

Nonetheless, programs just like the board-game champion AlphaZero and the more and more convincing fake-text generator GPT-3 have stoked the flames of debate concerning when people will create a man-made normal intelligence—machines that may multitask, suppose, and purpose for themselves. On this episode, we discover how machines be taught to speak—and what it means for the people on the opposite finish of the dialog. 

 We meet:

  • Susan C. Bennett, voice of Siri
  • Cade Metz, The New York Instances
  • Charlotte Jee, MIT Expertise Evaluation


This episode was produced by Jennifer Robust, Emma Cillekens, Anthony Inexperienced, Karen Hao and Charlotte Jee. We’re edited by Michael Reilly and Niall Firth.


[TR ID]  

Jim: I do not know if it was AI… If that they had taken the recording of one thing he had accomplished… and had been in a position to manipulate it… however I am telling you, it was my son. 

Robust: The day began like another for a person.. we’re going to name Jim. He lives outdoors Boston. 

And by the way in which… he has a member of the family who works for MIT.

We’re not going to make use of his final title as a result of they’ve considerations about their security.

Jim: It was a Tuesday or Wednesday morning, 9 o’clock I am deep in thought engaged on one thing, 

Robust: That’s … till he obtained this name. 

Jim: The telephone rings… and I decide it up and it is my son. And he’s clearly agitated. This, this child’s a very chill man however when he does get upset, he has a lot of vocal mannerisms. And this was like, Oh my God, he is in bother.

And he mainly informed me, look, I am in jail, I am in Mexico. They took my telephone. I solely have 30 seconds. Um, they mentioned I used to be consuming, however I wasn’t and persons are damage. And look, I’ve to get off the telephone, name this lawyer and it offers me a telephone quantity and has to hold up.

Robust: His son is in Mexico… and there’s simply little doubt in his thoughts… it’s him.

Jim: And I gotta inform you, Jennifer, it, it was him. It was his voice. It was every little thing. Tone. Simply these little mannerisms, the, the pauses, the gulping for air, every little thing that you may think about.

Robust: His coronary heart is in his throat…

Jim: My hair standing on edge 

Robust: So, he calls that telephone quantity… A person picks up… and he presents extra particulars on what’s happening.

Jim: Your son is being charged with hitting this automotive. There was a pregnant girl driving whose arm was damaged. Her daughter was within the again seat.. is in essential situation and they’re, um, they booked him with driving beneath the affect. We do not suppose that he has accomplished that. That is we have, we have come throughout this a lot of occasions earlier than, however a very powerful factor is to get him out of jail, get him secure, as quick as doable.

Robust: Then the dialog turns to cash… he’s informed bail has been set… and he must put down ten p.c.

Jim: In order quickly as he began speaking about cash, you already know, the, the flag sort of went up and I mentioned, excuse me, is there any probability that this can be a rip-off of some kind? And he received actually sort of, um, irritated. He is like, “Hey, you referred to as me. Look, I discover this actually offensive that you just’re accusing me of one thing.” After which my coronary heart goes again in my throat. I am like, that is the one man who’s between my son and even worse jail. So I backtracked… 


My spouse walks in 10 minutes later and says, nicely, you already know, I used to be texting with him late final evening. Like that is across the time most likely that he would have been arrested and jailed. So, in fact we textual content him, he is simply getting up. He is utterly advantageous. 

Robust: He’s nonetheless undecided how somebody captured the essence of his son’s voice. However he has some theories…

Jim: They needed to have gotten a recording of one thing when he was upset. That is the one factor that I can say, trigger they could not have mocked up a few of these issues that he does.. They could not guess at that. I do not suppose, and they also, I believe that they had actually some uncooked materials to work with after which what they did with it from there. I do not know.

Robust:  And it’s not simply Jim who’s uncertain… We don’t know whether or not AI had something to do with this. 

However, the purpose is… we now reside in a world the place we can also’t make certain that it didn’t. 

It’s extremely straightforward to pretend somebody’s voice with even a couple of minutes of recordings… and youngsters like Jim’s son? They share numerous recordings by way of social media posts and messages…  

Jim: …was fairly impressed with how good it was. Um, like I mentioned, I am not simply fooled and man, that they had it nailed. So, um, simply warning.

Robust: I’m Jennifer Robust and this episode we take a look at what it takes to make a voice.


Zeyu Gin: You guys have been making bizarre stuff on-line.

Robust: Zeyu Jin is a analysis scientist at Adobe… That is him talking  at an organization convention about 5 years in the past… displaying how software program can rearrange the phrases on this recording.

Key: I jumped on the mattress and I kissed my canine and my spouse—in that order.

Zeyu: So how about we mess with who he really kissed. // Introducing Challenge VoCo. Challenge VoCo permits you to edit speech in textual content. So let’s carry it up. So I simply load this audio piece in VoCo. In order you’ll be able to see we now have the audio waveform and we now have the textual content beneath it. //

So what will we do? Copy paste. Oh! Yeah it’s accomplished. Let’s take heed to it. 

Key: And I kissed my spouse and my canine.

Zeyu: Wait there’s extra. We will really kind one thing that’s not right here.

Key: And I kissed Jordan and my canine.

Robust: Adobe by no means launched this prototype… however the underlying expertise retains getting higher.

For instance, right here’s a computer-generated pretend of podcaster Joe Rogan from 2019… It was produced by Sq.’s AI lab referred to as Dessa to lift consciousness concerning the expertise.

Rogan: 10-7 “Pals I’ve received one thing new to inform all of you. I’ve determined to sponsor a hockey workforce made up totally of chimps.” 

Robust: Whereas it seems like enjoyable and video games… specialists warn these synthetic voices may make some varieties of scams an entire lot extra widespread. Issues like what we heard about earlier.

Mona Sedky: Communication centered crime has traditionally been decrease on the totem pole. 

Robust: That’s federal Prosecutor Mona Sedky talking final 12 months on the Federal Commerce Fee about voice cloning applied sciences.

Mona Sedky: However now with the appearance of issues like deep pretend video…  now deep pretend audio you… you’ll be able to mainly have anonymizing instruments and be anyplace on the web you need to be…. anyplace on the planet… and talk anonymously with folks. So because of this there was an infinite uptick in communication centered crime. 

Balasubramaniyan: However think about in case you as a CFO or chief controller will get a telephone name that comes out of your CEO’s telephone quantity. 

Robust: And that is Pindrop Safety CEO Vijay Balasubramaniyan at a safety convention final 12 months.

Balasubramaniyan: It’s utterly spoofed… so it really makes use of your tackle e-book, and it reveals up as your CEOs title……after which on the opposite finish you hear your CEO’s voice with an amazing quantity of urgency. And we’re beginning to see loopy assaults like that. There was an instance that plenty of press media coated, which is a $220,000 wire that occurred as a result of a CEO of a UK agency thought he was speaking to his dad or mum firm… so he then despatched that cash out. However we’ve seen as excessive as $17 million {dollars} exit the door. 

Robust: And the very concept of faux voices… might be simply as damaging as a pretend voice itself… Like when former President Donald Trump tried guilty the expertise for some offensive issues he mentioned that had been caught on tape. 

However like another tech… it’s not inherently good or unhealthy… it’s only a software… and I used it within the trailer for season one to point out what the expertise can do.

Robust: If “seeing is believing”… 

How will we navigate a world the place we will’t belief our eyes… or ears? 

And so you already know… what you’re listening to… It’s not simply me talking.  I had some assist from a man-made model of my voice… filling in phrases right here and there.

Meet artificial Jennifer. 

Artificial Jennifer: “Hello there, of us!”

Robust: I may even click on to regulate my temper…  

Artificial Jennifer: “Hello there.”

Robust: Yeah, let’s not make it indignant..

Robust: Within the not so distant future this tech will likely be utilized in any variety of methods… for easy tweaks to pre-recorded displays… even… to carry again the voices of animated characters from a collection… 

In different phrases, synthetic voices are right here to remain. However they haven’t all the time been really easy to make… and I referred to as up an professional whose voice would possibly sound acquainted.. 

Bennet: How does this sound? Um, possibly I could possibly be a bit of extra pleasant. How are you? 

Hello, I am Susan C Bennet, the unique voice of Siri. 

Nicely, the day that Siri appeared, which was October 4th, 2011, a fellow voice actor emailed me and mentioned, ‘Hey, we’re taking part in round with this new iPhone app, is not this you?’ And I mentioned, what? I went on the Apple website and listened… and yep. That was my voice. [chuckles]

Robust: You heard that proper. The unique feminine voice that tens of millions affiliate with Apple gadgets…? Had no concept. And, she wasn’t alone. The human voices behind different early voice assistants had been additionally taken without warning. 

Bennet: Yeah, it has been an attention-grabbing factor. It was an adjustment at first as you’ll be able to think about, as a result of I wasn’t anticipating it. It was a bit of creepy at first, I am going to should say, I by no means actually did plenty of speaking to myself as Siri, however regularly I received accepting of it and really it ended up turning into one thing actually constructive so…

Robust: To be clear, Apple didn’t steal Susan Bennett’s voice. For many years, she’s accomplished voice work for corporations like McDonald’s and Delta Airways… and years earlier than Siri got here out …she did an odd collection of recordings that fueled its growth.

Bennet:  In 2005, we could not have imagined one thing like Siri or Alexa. And so all of us, I’ve talked to different individuals who’ve had the identical expertise, who’ve been a digital voice, you already know we simply thought we had been doing simply generic telephone voice messaging. And so when instantly Siri appeared in 2011, it is like, I am who, what, what is that this? So, it was a real shock, however I like to consider it as we had been simply on the slicing fringe of this new expertise. So, you already know, I select to consider it as a really constructive factor, though, we, none of us, had been ever paid for the tens of millions and tens of millions of telephones that our voices are heard on. In order that’s, that is a draw back.

Robust: One thing else that’s awkward… she says Apple by no means acknowledged her because the American voice of Siri … that’s regardless of changing into an unintended superstar… reaching tens of millions.

Bennet: The one precise acknowledgement that I’ve ever had is through Siri. Should you ask Siri, who’s Susan Bennett, she’ll say, I am the unique voice of Siri. Thanks a lot Siri. Recognize it. 

Robust: Nevertheless it’s not the primary time she’s given her voice to a machine. 

Bennet: Within the late seventies after they had been introducing ATMs I prefer to say it was my first expertise as a machine, and you already know, there have been no private computer systems or something at the moment and other people did not belief machines. They would not use the ATMs as a result of they did not belief the machines to provide them the proper cash. They, you already know, in the event that they put cash within the machine they had been afraid they’d by no means see it once more. And so a really enterprising promoting company in Atlanta on the time referred to as McDonald and Little determined to humanize the machine. In order that they wrote a jingle and I turned the voice of Tilly the all time teller after which they finally put a bit of face on the machine.

Robust:  The human voice helps corporations construct belief with customers…  

Bennet: There are such a lot of completely different feelings and meanings that we get throughout by way of the sound of our voices fairly than simply in print. That is why I believe emojis got here up as a result of you’ll be able to’t get the nuances in there with out the voice. And so I believe that is why voice has develop into such an vital a part of expertise.

Robust:  And in her personal expertise, interactions with this artificial model of her voice have led folks to belief and speak in confidence to her… to name her a good friend, though they’ve by no means met her.

Bennet: Nicely, I believe the oddest factor about being the voice of Siri, to me is once I first revealed myself it was astounding to me how many individuals thought of Siri their good friend or some kind of entity that they may actually relate to. I believe they really in lots of instances consider her as human.

Robust: It’s estimated the worldwide marketplace for voice applied sciences will attain almost 185-billion {dollars} this 12 months…and AI-generated voices? are a recreation changer. 

Bennet: You understand, after years and years of engaged on these voices, it is actually, actually arduous to get the precise rhythm of the human voice. And I am positive they’re going to most likely do it in some unspecified time in the future, however you’ll discover even to at the present time, you already know, you will take heed to Siri or Alexa or one of many others and so they’ll be speaking alongside and it sounds good till it does not, is like, Oh, I will the shop. You understand, there’s some weirdness within the rhythmic sense of it. 

Robust: However even as soon as human-like voices develop into commonplace…she’s not totally positive that will likely be a superb factor.  

Bennet:  However you already know, the benefit for them is they do not actually should get together with Siri. They’ll simply inform Siri what to do if they do not like what she says, they’ll simply flip it off. So it isn’t like actual human relations. It is like possibly what folks would really like human relations to be. All people does what I need. (laughter) Then everyone’s comfortable. Proper?

Robust: After all, voice assistants like Siri and Alexa aren’t simply voices. Their capabilities come from the AI behind the scenes too.

It’s been explored in science fiction movies like this one, referred to as Her… a few man who falls in love along with his voice assistant.

Theodore: How do you’re employed?

Samantha (AI): Nicely… Mainly I’ve instinct. I imply.. The DNA of who I’m is predicated on the tens of millions of personalities of all of the programmers who wrote me, however what makes me me is my capacity to develop by way of my experiences. So mainly in each second I am evolving, identical to you.

Robust: However at present’s voice assistants are a far cry from the hyper-intelligent pondering machines we’ve been musing about for many years. 

And it’s as a result of that expertise… is definitely many applied sciences. It’s the mixture of three completely different abilities…speech recognition, pure language processing and voice technology.

Speech recognition is what permits Siri to acknowledge the sounds you make and transcribe them into phrases. Pure language processing turns these phrases into that means…and figures out what to say in response. And voice technology is the ultimate piece…the human factor…that offers Siri the power to talk.

Every of those abilities is already an enormous problem… To be able to grasp simply the pure language processing half? You just about should recreate human-level intelligence.

And we’re nowhere close to that. However we’ve seen outstanding progress with the rise of deep studying… serving to Siri and Alexa be a bit of extra helpful.

Metz: What folks could not find out about Siri is that authentic expertise was one thing completely different.

Robust: Cade Metz is a tech reporter for The New York Instances. His new e-book is named Genius Makers: The Mavericks Who Introduced AI to Google, Fb, and the World. 

Metz: The best way that Siri was initially constructed… You needed to have a workforce of engineers, in a room, at their computer systems and piece by piece, they needed to outline with laptop code how it will acknowledge your voice. 

Robust: Again then… engineers would spend days writing detailed guidelines meant to point out machines methods to acknowledge phrases and what they imply.

And this was accomplished on the most fundamental stage… typically working with simply snippets of voice at a time.

Simply think about all of the other ways folks can say the phrase “whats up” … or all of the methods we piece collectively sentences … explaining why “time flies” or how some verbs may also be nouns. 

Metz: You may by no means piece collectively every little thing you want, regardless of what number of engineers you don’t have any matter how wealthy your organization is. Defining each little factor which may occur when somebody speaks into their iPhone… You simply haven’t got sufficient person-power to construct every little thing you want to construct. It is simply too sophisticated. 

Robust: Neural networks made that course of an entire lot simpler… They merely be taught by recognizing patterns in knowledge fed into the system. 

Metz: You’re taking that human speech… You give it to the neural community… And the neural community learns the patterns that outline human speech. That manner it could possibly recreate it with out engineers having to outline each little piece of it. The neural community actually learns the duty by itself. And that is the important thing change… is {that a} neural community can be taught to acknowledge what a cat seems like, versus folks having to outline for the machine what a cat seems like.

Robust: However even earlier than neural networks… Tech corporations like Microsoft aimed to construct programs that might perceive the on a regular basis manner folks write and speak.

And in 1996, Microsoft employed a linguist … Chris Brocket… to start work on what they referred to as pure language AI.

Metz: The man’s not a pc scientist, however what his job was was to outline the way in which that language is pieced collectively, proper. For a pc. And that’s simply an extremely tough process, proper? Why will we as English audio system order our phrases, the way in which we do, proper? And he, he spent years, actually years, 5 – 6 years at Microsoft, you already know, slowly, you already know, attempting to inform the pc the way in which that English is, is put collectively. So then the pc can try this.

Robust: Then, one afternoon in 2003… a small group at Microsoft… down the corridor from Brockett… began work on a brand new undertaking. They had been constructing a system that translated languages utilizing a method primarily based on statistics. 

The thought being if a set of phrases in a single language appeared with the identical frequency and context in one other, that was the possible translation. 

Metz: They put collectively a prototype in a matter of weeks and confirmed it off to a bunch on the Microsoft analysis heart—together with Chris Brocket. 

Robust: The system is… fairly cobbled collectively. It solely works when utilized to items of a sentence… And even then… the translations had been jumbled. 

Metz: As he sees them show this.. he has a panic assault to the purpose the place he actually thinks he is having a coronary heart assault as a result of he realizes that his profession could be over. That every little thing he has spent the previous six years on // is pointless and has been made pointless by the system that these guys inbuilt a matter of weeks. 

Robust: At the moment we didn’t have the quantity of information wanted to coach a neural community, nor the processing energy… however the concept of 1 has been round because the Nineteen Eighties.

And a type of concepts got here within the type of NetTalk…which was developed by AI pioneer Terry Sejnowski. 

The system may be taught to talk phrases by itself by finding out youngsters’s books. 

Metz: Terry had this unbelievable demo that he would present to folks at conferences. It was kind of time-lapsed as a result of it took some time for the neural community to be taught, however he may present that because it began to investigate the patterns in these youngsters’s books, they may begin to babble…

[Sounds from NetTalk Demo]

Metz: after which it may babble a bit of higher, after which it may begin to piece phrases collectively, after which instantly it may pronounce these phrases. 

[Sounds from NetTalk Demo]

Metz: He may present his viewers // with this demo, how a neural community may be taught.  

Robust: It will be one other 2 many years earlier than the computing energy existed to essentially make this handy..   

Metz: So pure language was an space the place even after the success of neural networks with speech and picture, folks thought, Oh, nicely, it is not going to work with pure language. Nicely, it has. That does not imply it is good. 

Robust: Deep studying, (the expertise driving the present AI growth), can prepare machines to develop into masters in any respect kinds of duties. However it could possibly solely be taught issues separately. And since most AI fashions prepare their skillset on 1000’s or tens of millions of examples, they find yourself repeating patterns present in previous knowledge—together with the numerous unhealthy selections that individuals have made, like marginalizing folks of colour and girls.

And any huge advances fire up this debate about when people will create a man-made normal intelligence—or machines that may multitask, suppose, and purpose for themselves. Not too long ago, that’s been advances just like the board-game champion AlphaZero… and the more and more convincing fake-text generator GPT-3…

Metz: It may, it could possibly generate weblog posts. It may generate tweets, emails. It may generate laptop applications. You understand, it really works possibly half the time, however when it does work, you can’t inform the distinction between its English and your English. Okay. That’s progress. It isn’t the mind, it is not even shut, but it surely’s progress.

Robust: And these and different instruments are additionally… extremely divisive. 

Metz: Can we, within the close to future, construct a system that may do something the human mind can do. Proper. And folks will argue about this, like foaming on the mouth on both facet. The truth is we don’t know. Like there are people who find themselves utterly positive that is going to occur fairly quickly, however they do not know what the trail is there. None of us can predict the longer term. And so it is an argument about nothing that may be basically determined. So in fact the argument by no means ends. You return to the fifties and it is, it is all the identical stuff, proper?

Robust: But when we are to sometime replicate that intelligence… would possibly we additionally have the ability to replicate ourselves? 

…That’s after the break. 


[Music transition]

Robust: Synthetic voices have been round for some time…however they didn’t begin getting extra human-like till actually the final 5 years.

Like when Deepmind’s text-to-speech algorithm referred to as WaveNet got here onto the scene… which is the idea of Google’s assistant Duplex…the one that may e-book your hair appointment or restaurant reservation.

[Sounds of Google Duplex scheduling a hair appointment on user’s behalf] 

*Cellphone ringing*

Stylist: “Hiya. How can I enable you?”

AI: “Hello. I’m calling to e-book a ladies’s haircut for a consumer. Umm.. I’m on the lookout for one thing on Might third.”

Stylist: “Certain. Give me one second..” 

Robust: Since then, many different corporations have labored to commercialize comparable voice applied sciences. Lyrebird…Descript…Respeecher…simply to call just a few. 

There’s additionally a slew of corporations geared towards preserving the reminiscence of our family members… by creating interactive, digital variations of them. 

One firm.. referred to as HereAfter.. turns them into chatbots that can be utilized with voice assistants. 

And Tech Evaluation reporter Charlotte Jee tried it out along with her dad and mom…

Jee: Alexa, open, HereAfter.

Artificial voice: Welcome to HereAfter. Would you fairly converse with Paul or with Jane? 

Jee: I might fairly converse with Jane.

Digital Jane: Hiya, that is Jane Jee, and I am comfortable to inform you about my life.


Digital Jane: How are you at present?

Jee:  Uh I am nicely, thanks mum. How are you?

Digital Jane: Good. 

Robust: The system’s listening abilities… aren’t the very best. You may solely ask quick, easy questions… and you need to wait till the system has completed talking earlier than you’ll be able to ask a brand new query. 

Digital Jane: There’s a lot to speak about. My childhood profession and my pursuits, which of these sounds finest?

Jee:  Uh… your childhood. 

Digital Jane: Certain. I received into bother as a baby as a result of I used to be very unbiased and I appreciated to train my freedom…

Robust:  However what you’re listening to now… isn’t really a pretend or artificial model of Charlotte’s mom. Actually, this expertise depends on surprisingly much less AI than you’d suppose. 

Her dad and mom had been interviewed for hours by HereAfter with questions provided by Charlotte and her sister. That interview was then edited and damaged into subject sections.. which might be introduced up and performed by the system primarily based on the questions they ask. 

However.. as we’ve seen.. voice is highly effective. Particularly when it’s offered as an interactive expertise. 

Jee: Oh my God. (laughter) That was so bizarre!

That was like listening to my mother.. as a machine. That was actually freaky. 

I felt extra emotional listening to that than I sort of anticipated to? When, like, the voice relaxed and it seemed like her.

Robust: This feels lots like one thing we’ve seen earlier than. Like in an episode of Black Mirror…  the place a girl makes use of her companion’s smartphone knowledge to create an artificial model of his voice after he dies. 

[Sounds from Black Mirror – AI sifting through shared media, montage of audio clips from the woman’s deceased partner] 

Robust: It sifts by way of previous movies, texts, voicemails, and social media posts to construct a system able to mimicking his voice.. and character.  

AI: “Hiya?”

Lady: “…Hiya! You… sound identical to him..” 

AI: “Virtually creepy isn’t it? I say creepy…. I imply, it’s completely batshit loopy I may even speak to you. I imply…I don’t also have a mouth.”
Lady: “Thats…That’s simply…

AI: “That’s what?”

Lady: “That’s simply the kind of factor he would say.”

AI: “Nicely…that’s why I mentioned it.” 

Robust: Which brings up a thorny difficulty… is she constructing belief along with her AI companion … or is it simply telling her what she needs to listen to… ?

And past how we would develop voice applied sciences able to widespread sense or self-improvement… lies one more query we’re simply beginning to elevate… which is..… how will we reckon with this newfound energy… to synthesize one thing as private as somebody’s voice? 


Robust: Subsequent episode… We take a look at the position of automation on our credit score. 

Michele Gilman: The witness for the state who was a nurse, could not clarify something concerning the algorithm. She simply stored repeating again and again that it was internationally and statistically validated, however she could not inform us the way it labored, what knowledge was fed into it, what elements it weighed, how the elements had been weighed. And so my scholar legal professional seems at me and we’re taking a look at one another pondering, how will we cross look at an algorithm…

Robust: This episode was made by me, Emma Cillekens, Anthony Inexperienced, Karen Hao and Charlotte Jee. We’re edited by Michael Reilly and Niall Firth.

Thanks for listening, I’m Jennifer Robust. 


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