Speaker 1: Pushkin. There are a lot of reasons that I'm excited
Speaker 1: about today's show. I'm going to tell you three right now.
Speaker 1: Number One, the show is about this whole dimension of
Speaker 1: medicine that I essentially didn't know existed, acoustic biomarkers, basically
Speaker 1: using a person's voice to assess their health. Second thing
Speaker 1: I'm excited about the show is about the intersection of
Speaker 1: AI and healthcare, one of my top say five intersections.
Speaker 1: Love that intersection. And three, today's guest doctor Yael Bensusan,
Speaker 1: gave me what was truly the best excuse that anyone
Speaker 1: has ever given me for canceling an interview at the
Speaker 1: last minute.
Speaker 2: Yeah, so I'm really sorry for having to cancel on
Speaker 2: you yesterday.
Speaker 1: What was the what was the surgery you had to
Speaker 1: do yesterday?
Speaker 2: So yesterday we call it airway surgery, where I take
Speaker 2: a base to the oar and I have to open
Speaker 2: up their windpipe or their trachia because they're a scar
Speaker 2: tissue that's blocking them from breathing. So I have to
Speaker 2: go with a laser and cut the scar tissue out
Speaker 2: and then take a balloon and open up their windpipe
Speaker 2: so that they can wake up and breathe better, and
Speaker 2: that translates to a different sound when they're breathing.
Speaker 3: So when they're not.
Speaker 2: Breathing because of the scar tissue, we can sound like,
Speaker 2: you know, very noisy breathing. We call it the Darth
Speaker 2: Vader breathing. And then when wake up they wake up
Speaker 2: from surgery and they're done, they have silent breathing, which
Speaker 2: means that I know that I did a good job.
Speaker 1: I'm Jacob Goldstein and this is What's your Problem the
Speaker 1: show where I talk to people. We're trying to make
Speaker 1: technological progress. Doctor Yeah. Eld and Susan run the Health
Speaker 1: Voice Center at the University of South Florida, and she
Speaker 1: is also leading a team of researchers that's building a
Speaker 1: giant database of human voices and breadths and health information.
Speaker 1: Her problem is this, how do you record the voices
Speaker 1: of thousands of people without violating patient privacy laws while
Speaker 1: building a giant public database that could someday allow your
Speaker 1: phone to warn you, based solely on your voice that
Speaker 1: you may be getting sick. Yeah El told me that
Speaker 1: she got into this field in part because she used
Speaker 1: to be a singer.
Speaker 2: So I growing up, you know, I always was in
Speaker 2: a very musical family. I took singing lessons when I
Speaker 2: was a kid, and then I started singing more professionally
Speaker 2: around eighteen years old, and I had a short but
Speaker 2: exciting singing career. I wrote pop folk music. We had
Speaker 2: a bend, and we toured. We had an album out
Speaker 2: in two thousand and twelve. Yeah, and I mean it
Speaker 2: was a lot of fun. And actually the reason I
Speaker 2: was able to have that short and exciting career was
Speaker 2: because I met a speech pathologist when I was fifteen.
Speaker 2: So I was taking singing classes and one day my
Speaker 2: teacher looked at me and she said, there's something wrong
Speaker 2: with your voice.
Speaker 3: Go get checked.
Speaker 2: And I met a laryngologist who put his camera down
Speaker 2: and she said, you have nodules on your vocal cords
Speaker 2: and you might not be able to sing again if
Speaker 2: you don't take this seriously. And I went to see
Speaker 2: a speech pathologist. I did rehabilitation with my voice for
Speaker 2: six months, and I was able to sing again. And
Speaker 2: I mean that's what led me to then become a
Speaker 2: speech pathologist growing up, and then eventually go to med
Speaker 2: school and then decide to become a laryngologist.
Speaker 3: So it was kind of all interconnected.
Speaker 1: So I know that your research now and most of
Speaker 1: what I'm really interested to talk with you about is
Speaker 1: is around acoustic biomarkers. So just to start, I mean,
Speaker 1: what's an acoustic biomarker?
Speaker 2: Very good question. So what is a biomarker? First, A
Speaker 2: biomarker is something that indicates the presence of a disease, right,
Speaker 2: So if you think about a biomarker for a cancer,
Speaker 2: so different cancers have different types of biomarker. For example,
Speaker 2: for ovarian cancer, we're looking for a specific thing you know,
Speaker 2: called ca in your blood. For different types of cancers,
Speaker 2: they could take a blood draw and find a specific biomarker.
Speaker 2: It's an indicator of a disease. An acoustic biomarkers is
Speaker 2: something that can indicate a presence of a disease, but
Speaker 2: that you can hear. So that's the definition of an
Speaker 2: acoustic biomarker. So I always say, you know, when you
Speaker 2: have people in your family that are not well, you
Speaker 2: will always notice first and you'll say you don't sound
Speaker 2: good right, or you sound funny. And I have the
Speaker 2: luxury to know that because I'm a voice doctor. So
Speaker 2: then people will bring me their family members or people
Speaker 2: will come saying, I don't know what's wrong with me,
Speaker 2: but my wife told me to come because my voice
Speaker 2: is not good. And sometimes it's because their vocal cords
Speaker 2: are not working, but a lot of times it's because
Speaker 2: they can have a neurological issue or a cardiac issue
Speaker 2: that is affecting their voice.
Speaker 1: So, more broadly, what's going on with AI and acoustic biomarkers.
Speaker 2: Yeah, so, so many exciting things are going on. I
Speaker 2: think that's the first answer. There are so many startups,
Speaker 2: so many companies, industry researchers, academic researchers that are working
Speaker 2: and looking into voice AI. And the reason is it's
Speaker 2: really cheap to collect. Right to think about this, If
Speaker 2: you have a phone, it's really cheap to collect Compared
Speaker 2: to this.
Speaker 1: You don't have to pick a blood sample. You have
Speaker 1: exactly just you've got the phone. You've got the device
Speaker 1: literally in your hand already. All you have to do
Speaker 1: is talk, and you're talking already.
Speaker 2: And you're talking already, so it's cheap to that's why
Speaker 2: pharmaceutical industries are also very interested, and there's a lot
Speaker 2: of pharmaceutical projects around it. So there are a lot
Speaker 2: of projects that are going on and the state or
Speaker 2: the The current landscape is that there's tons of people
Speaker 2: working on very similar things and very interesting and various disease.
Speaker 2: So I always I kind of categorize them in three
Speaker 2: categories of diseases that are being studied. One is the
Speaker 2: disease that affects the voice box. Okay, so vocal court paralysis, absolutely,
Speaker 2: it's intuitive. There's going to be vocal biomarkers in that
Speaker 2: voice box cancer, right, that's easy. Then there's a voice
Speaker 2: and speech affecting disorders, so disorders that don't affect the
Speaker 2: voice box, but that have an impact on the voice
Speaker 2: and the speech. Parkinson is one of them, right, Alzheimer's
Speaker 2: is one of them. A stroke somebody having a stroke,
Speaker 2: they don't have a problem with their voice box, but
Speaker 2: their speech is going to be altered. So these are
Speaker 2: voice and speech affecting conditions. So lots of work is
Speaker 2: being done in that field. And the third one is
Speaker 2: diseases that you don't think would affect speech, but still
Speaker 2: people are doing research on that. So there was a
Speaker 2: really interesting study on diabetes. They're saying that there was
Speaker 2: a group that published that they could diagnose people that
Speaker 2: were diabetic versus non diabetics based on their speech and this.
Speaker 1: So this third group is one presumably where there's at
Speaker 1: least the potential for AI to detect differences that even
Speaker 1: experts like you cannot detect, right, I mean, is that
Speaker 1: what's going on there? What?
Speaker 2: So AI is not magical, you know, I think it's
Speaker 2: It does a lot of things. But what AI does
Speaker 2: that the layperson doesn't do is that it can analyze
Speaker 2: a lot more data faster.
Speaker 1: Yeah.
Speaker 2: Right, So AI has the possibility, if you have a
Speaker 2: large data set, to then find small differences in these
Speaker 2: data sets that we don't have. I mean, I would
Speaker 2: have to listen to, you know, thousands and thousands of
Speaker 2: voices and compare them statistically.
Speaker 1: It might it might, right. It might also be able
Speaker 1: to detect differences that are not even audible.
Speaker 2: It could exactly. I can give it an example. There's
Speaker 2: a company looking at atrial fibrillation, and I cannot validate
Speaker 2: their data because that's one of the limitations that we're
Speaker 2: going to talk about. But obviously their data set is
Speaker 2: not public. But they're saying that they can diagnose atrial
Speaker 2: fibrillation based on the voice. And their explanation is that
Speaker 2: our voice vibrates to the sound of our heartbeats.
Speaker 1: Big if true? Fun if true?
Speaker 2: I mean you know, again, the limitation here is that
Speaker 2: it's there's a lot of things you can't validate. But
Speaker 2: they say that they've been validating it with EKGs and
Speaker 2: that they can see it. They can hear a difference
Speaker 2: in the voice between patient patients with a.
Speaker 1: Fib atrial fibrillation. It puts you at risk for a stroke, right,
Speaker 1: it can go undiagnosed. So like, if if this works,
Speaker 1: that would be very helpful to many people, right, absolutely, absolutely.
Speaker 1: So you're mentioning like that's super interesting. It's it's interesting
Speaker 1: more generally. So, so you're building a giant database, right,
Speaker 1: and I find that interesting for a lot of reasons.
Speaker 1: It happens. I don't have you come across the work
Speaker 1: of faith A Lee. Absolutely, yeses, So I talked to
Speaker 1: faith A Lee for this show not long ago. Wow. Right,
Speaker 1: she's like nerd famous, right yeah, And so you know,
Speaker 1: as you know, she built this giant database of images
Speaker 1: about ten years ago a little more now called image net.
Speaker 1: And that was that giant database was what allowed these
Speaker 1: early machine learning models AI models to you know, start
Speaker 1: recognizing images, right, and so the database was this necessary tool,
Speaker 1: necessary thing for the AI to really work, right, And
Speaker 1: so are you building the acoustic biomarker version of that?
Speaker 2: So the first the short answer is yes, but I'd
Speaker 2: like to start by saying that I am not building
Speaker 2: it's our distortion.
Speaker 1: Yes, yes, are you all are?
Speaker 3: Actually, I'll just.
Speaker 2: First start by recognizing here that it's it's a it's
Speaker 2: a huge team. So we're the Bridge to Way I
Speaker 2: Voice Constortium is a team of fifty investigators across the
Speaker 2: US and Canada. We're funded by the NIH through the
Speaker 2: Bridge to Way I program and the goal absolutely this
Speaker 2: is the first time I hear the analogy to the
Speaker 2: image net database.
Speaker 3: I like it.
Speaker 2: I usually give the example of the genomic database, the
Speaker 2: Human Genome Project, huge.
Speaker 1: Project, more famous, more famous, they're.
Speaker 3: Both very famous. But I like this analogy.
Speaker 1: Well. Image net is maybe a little bit closer of
Speaker 1: an analogy, but maybe less Yeah yeah, sexy, yeah.
Speaker 2: Well, but I mean it's interesting because the genome project
Speaker 2: has also very interesting ethical particularities like voice, right, the
Speaker 2: image has a little bit less of the ethical constraints.
Speaker 3: For is, when we talk about whole genome.
Speaker 2: Sequencing or genomics data people kind of understand that voice
Speaker 2: has similar concerns in terms of process.
Speaker 1: We want to get to the concerns, but I want
Speaker 1: to first talk about what you're doing and and then
Speaker 1: we can talk about you know, not doing anything wrong. Yeah.
Speaker 1: So broadly, if it becomes the thing you hope it
Speaker 1: will be, what, what is it going to be? What
Speaker 1: is the bridge to AI voice database going to be?
Speaker 2: So it's going to be this large database of thousands
Speaker 2: of human voices linked to other health information that are
Speaker 2: going to be available to researchers and potentially people other
Speaker 2: than researchers as well, to be able to make discoveries, right,
Speaker 2: to learn to use a voice AI, to train you know,
Speaker 2: the next generation of people on how to learn to
Speaker 2: build models on voice AI, to help pharmaceutical companies develop
Speaker 2: products or learn even to to develop products, right, And
Speaker 2: the other really important thing is to teach people what
Speaker 2: type of standards we need right right now, a lot
Speaker 2: of different projects, there's really a lack of standards. People
Speaker 2: collect voice in different ways. That's why it's really hard
Speaker 2: to pull data together. So our dream was really to say, like, hey,
Speaker 2: you want to do voice research, here's a manual, my friend, right,
Speaker 2: like here is how you collect the voice to make
Speaker 2: it accurate. This is the protocols with the task that
Speaker 2: we think, based on our studies, give the best biomarkers. Right,
Speaker 2: These are the type of biomarkers you can look for
Speaker 2: and this is the data you can train, so really
Speaker 2: create a manual of operations also for people to be
Speaker 2: able to make discoveries, and that's the goal to have
Speaker 2: the most impact on patient care.
Speaker 1: So what are the biomarkers? What are you asking people
Speaker 1: to do? What are you collecting?
Speaker 2: So I separate things between. So there are respiratory biomarkers,
Speaker 2: voice biome markers, speech biomarkers, and linguistics biomarkers, and they're
Speaker 2: all different. So let's go about why these are different.
Speaker 2: So respiratory is easy, right, So we ask people to breathe,
Speaker 2: to cough, to take big breaths in and that has
Speaker 2: a lot of information on our pulmonary capacity, on how
Speaker 2: our windpipe is shaped. Okay, that's respiratory. Then voice and
Speaker 2: speech what's the difference. So voice is really the sound
Speaker 2: that we make when our vocal cords come together. So
Speaker 2: when we say, like birds can voice, but they can't speak.
Speaker 2: If you have a bird that speaks, then you'll be very.
Speaker 1: Rich or you have a parent.
Speaker 2: So when we when we do voice tasks, we ask
Speaker 2: patients to say E or.
Speaker 3: Ah or I.
Speaker 1: Get the difference.
Speaker 2: Birds and voice biomarkers will be impacted when our voice
Speaker 2: box is changed or our resp is changed. Right, So
Speaker 2: somebody with pneumonia probably cannot hold a note for very long,
Speaker 2: So that's voice biomarkers. When we talk about speech biomarkers,
Speaker 2: then you go into articulation. So some people, for example,
Speaker 2: who have neurological deficits or their mouth is not working correctly,
Speaker 2: they're going to have trouble articulating. They're going to have
Speaker 2: trouble saying some words. So these are biomarkers we can extract.
Speaker 2: And then lastly there's linguistic biomarkers. So what type of
Speaker 2: words are people using, what type of semantic how fast
Speaker 2: do they speak for example? These are all different types
Speaker 2: of biomarkers that.
Speaker 3: That we can extract.
Speaker 2: So to give you a very tangible example, I was
Speaker 2: reading a paper from a group looking at biomarkers of depression,
Speaker 2: and rate of speech was one of the important biomarkers
Speaker 2: they found. So people who are sad or depressed will
Speaker 2: speak at a slower pace, so words per second is smaller.
Speaker 2: So that's simple when you think about it, it's a
Speaker 2: simple by marker, right, So that's to give up tangible examples.
Speaker 2: So in terms of I think I didn't answer your
Speaker 2: question fully, So what are we asking patients? So we
Speaker 2: ask people to do all these tasks so coughing, breathing,
Speaker 2: a e. Then we make them read those validated passages,
Speaker 2: and we also ask open questions. And then when we
Speaker 2: ask open questions, we have to ask about questions that
Speaker 2: make them emotional and some that don't make them emotional,
Speaker 2: because if you trigger emotion, that causes a bias on
Speaker 2: how your voice will sound.
Speaker 1: What what question do you ask to make people emotional?
Speaker 3: So it's really interesting.
Speaker 2: So at first we would ask, you know, our first
Speaker 2: question was, you know, can you talk to me about
Speaker 2: something that makes you sad? It could be somebody that
Speaker 2: died in your family or you know, So that was
Speaker 2: our prompt. And then our question without emotion was tell
Speaker 2: us about your disease and.
Speaker 1: Only a doctor. What'd think that's for that emotional question?
Speaker 3: Exactly?
Speaker 2: I mean, but it's like when you think about it,
Speaker 2: like Our consortium is like tons of experts that put
Speaker 2: their minds together to develop.
Speaker 1: Tell me about having Parkinson's. That's the unemotional question we're
Speaker 1: going to ask.
Speaker 3: And then we I mean, we like, why are you here?
Speaker 2: I think it was not that obvious, but it's like,
Speaker 2: tell us about why you're here to see your doctor today.
Speaker 2: And then analyzing the data, because we do pilots, right,
Speaker 2: we audit our data. We realized that people were starting
Speaker 2: to tear up, like we had people crying while talking
Speaker 2: about why they were coming to the doctor today, which is.
Speaker 1: Supposed to be the example of unemotional.
Speaker 3: Sure, correct, So we had to change that.
Speaker 1: Yes, interesting, So okay, this is great. So you're getting
Speaker 1: a lot of auditory information from every patient. What other
Speaker 1: information you're getting from each person? So much?
Speaker 2: So to give you an idea, our full protocol is
Speaker 2: about one.
Speaker 1: Hour okay, so of the patient with the patient.
Speaker 2: With an ipassion it's an iPad, So everything is based
Speaker 2: on an iPad and there's a helper right now or
Speaker 2: research assistant. So we collect data. We collect very extensive
Speaker 2: demographics in terms of you know, age, race, geographical location.
Speaker 2: We collect language, So what language do you speak? How
Speaker 2: many languages is do you speak, what languages do you write?
Speaker 2: You know, what part of the world are you from?
Speaker 2: That's really important. Then we collect about disabilities. Are you
Speaker 2: hearing compared are you visually impaired? Because that makes a
Speaker 2: change in your voice, your smoking status, your hydration status,
Speaker 2: your fatigue status, because that's so we're we kind of
Speaker 2: thought about anything that could affect voice, right, your socio
Speaker 2: economical status because if you think about it, that's going
Speaker 2: to affect you know, your linguistics as well. And then
Speaker 2: so other that extensive demographics, then we collect confounders, so
Speaker 2: we think about anything that could change your voice. Do
Speaker 2: you have allergies? Do you do you have dental issues?
Speaker 2: Do you wear braces? So everybody gets a basic test
Speaker 2: about if they are depressed. So no matter what disease
Speaker 2: you have, you kind of get the basic tests for
Speaker 2: all the other disease to measure if it's possible that
Speaker 2: you have concurrent diseases at the same time.
Speaker 1: Because presumably because people are in fact complex, and there
Speaker 1: are many people who have depression and Parkinson's and you
Speaker 1: want to understand what's going on there.
Speaker 2: I mean, most people are complex, right, It's really rare
Speaker 2: to have and people that go to the doctor are
Speaker 2: not twenty year old and healthy. Right, most of the
Speaker 2: people who will use our technology or will benefit from
Speaker 2: these database will be your typical sixty year old chronic
Speaker 2: disease patient that comes into the doctor and they're not
Speaker 2: they don't have a sterile bill of health.
Speaker 1: How many people do you want to have in the database? Like,
Speaker 1: is there a final number you're going for?
Speaker 2: So at the beginning, we were aiming for thirty thousand,
Speaker 2: which is extremely it's extremely ambitious, I think to be fair,
Speaker 2: I mean, if after four years we get to ten thousand,
Speaker 2: I think it'll be a huge success. Okay, And you
Speaker 2: know the data collection. I think what we're learning is
Speaker 2: that data collection is very resource intensive. To have good
Speaker 2: data is very resource intensive.
Speaker 1: So what happened that made you realize that thirty thousand
Speaker 1: was maybe harder than you thought?
Speaker 2: So? I think we thought that we wanted to collect
Speaker 2: as much data as possible, and our original plan was
Speaker 2: to collect a lot shorter protocols, you know, like shorter clips.
Speaker 2: But as we started working with patients, we realized that
Speaker 2: by getting more data from the same patients, we can
Speaker 2: actually have a lot more information and it provides a
Speaker 2: lot of interesting you know biomarkers. So we're focusing more
Speaker 2: on getting more data from a smaller amount of patients
Speaker 2: and really with the right data, kind of right data
Speaker 2: with a lot of clinical information attached to it.
Speaker 1: After the break, what the world will look like in
Speaker 1: a few years if everything goes well. So this is
Speaker 1: a big project that yeah, Elle and her colleagues are
Speaker 1: embarked on. It's a four year project. They're about a
Speaker 1: year in and there will be interim data releases along
Speaker 1: the way. So I asked her, how long will it
Speaker 1: take for this project to advance the state of the
Speaker 1: science in acoustic biomarkers.
Speaker 2: Yeah, I would say to say at the end of
Speaker 2: the four years would be a probably the best answer.
Speaker 2: I think at the end of the four years. But
Speaker 2: I think that you know, you can just say, oh,
Speaker 2: we'll just start training models at the end of the
Speaker 2: four year once we have all the data. Right, It's
Speaker 2: not just about you know, building one model that I'll
Speaker 2: answer your question, is about continuously training models to understand
Speaker 2: which biomarkers to extract the then build products that walk.
Speaker 1: So, so, if things go well, what will this world
Speaker 1: look like in whatever five years?
Speaker 2: Yes, So, I mean there's there's a few things that
Speaker 2: this can help with in general, voice biomarkers. Let's not
Speaker 2: talk about just our project. Diagnosis is one thing, right,
Speaker 2: early diagnosis, but that's probably the hardest thing, Huh. Screening
Speaker 2: is most more important. So when we think about screening,
Speaker 2: it means you, let's say you live really far you
Speaker 2: don't have access to a doctor, but your doctor has
Speaker 2: an iPhone and you can talk into the iPhone and
Speaker 2: it can say, hey, something's wrong. You know, you need
Speaker 2: a neurological specialist, for example. So to help screen and triage.
Speaker 2: I think this probably we're looking at in the next
Speaker 2: five years, something definitely possible. The other product that I
Speaker 2: think will be very possible within five years is tracking
Speaker 2: of diseases. If you want to monitor the evolution of
Speaker 2: parkinson or how people respond to drugs. That's why pharmaceutical
Speaker 2: companies are very interested.
Speaker 1: Right. So the acoustic biomarker is not just a binary
Speaker 1: signal of disease, no disease. It can tell you a
Speaker 1: lot about the status of disease. Is it getting better,
Speaker 1: is it getting worse?
Speaker 2: Evolution, especially if you train it on your own voice. Right,
Speaker 2: it's even easier to detect changes in somebody's voice as
Speaker 2: they progress, like your sory for example, or Alexa that
Speaker 2: learns listens to your voice. So that's going to be
Speaker 2: a really good tool for pharmaceutical companies. That's why they're
Speaker 2: investing in it, right, to see how you respond to
Speaker 2: a drug, how you respond to a treatment. And when
Speaker 2: you think about telehealth at home, right, so more and
Speaker 2: more we're going to talk about remote monitoring people. There
Speaker 2: were just too many people on this earth to all
Speaker 2: be in hospitals when we're sick.
Speaker 1: Well, and if you can stay out of the hospital
Speaker 1: when you're sick, that's better, Right, You don't want to
Speaker 1: go to the hospital unless you have to do yeah, or.
Speaker 2: That you're you know, your Lexa detects when your voice
Speaker 2: starts detailor rating and sends you a nurse before you
Speaker 2: need to go to the hospital.
Speaker 1: So there's a more general version of that one, right
Speaker 1: that you could imagine, which is you get your whatever,
Speaker 1: your iPhone, your Android phone, and you have a choice
Speaker 1: when you're setting up your phone, like do you want
Speaker 1: to opt into to the phone listening and to tell
Speaker 1: you if you need to go talk to your doctor, right,
Speaker 1: just like a very broad based thing that you could
Speaker 1: opt into, like I would probably opt into that. I mean,
Speaker 1: is that a thing that you think about?
Speaker 2: So, I mean yes, I'm sure that you know Apple
Speaker 2: is working on that already.
Speaker 3: They are, you know.
Speaker 2: The question is there has to be technology that's being
Speaker 2: developed as well to ensure privacy of not only you,
Speaker 2: but your environment. Right, because when it's your phone, then
Speaker 2: it's your environment as well.
Speaker 1: So you brought up privacy in that context, we can
Speaker 1: knock out private see in the context of the database
Speaker 1: as well. Here, how could it go wrong? Building a
Speaker 1: database of thousands of people's voices with tons of data
Speaker 1: about them sort of answers itself.
Speaker 2: Yeah, it can go wrong in many ways. And I
Speaker 2: just came out of like two hours of meetings of this.
Speaker 2: So add the Bridge to ay I program. We have
Speaker 2: a huge group of bioethicists and one of our big
Speaker 2: aim as a group is really to ensure patient privacy
Speaker 2: and to answer these questions of how do we protect
Speaker 2: patient privacy in the context of open data. Right, So,
Speaker 2: you are absolutely right, tons of things can go wrong.
Speaker 2: People can be potentially reidentified through their voice. So one
Speaker 2: of our biggest goals this year is determined what part
Speaker 2: of the voice is identifiable and which part is not okay,
Speaker 2: And all of this is based on the Hippo law.
Speaker 2: Hippo law is from the nineteen nineties.
Speaker 1: Hippola the that governs sharing and security of people's medical information.
Speaker 2: Correct protected health information PHI, we call that, and that
Speaker 2: law was made in nineteen nineties, right and back then
Speaker 2: they listed a list of things of what they called
Speaker 2: PHI or identifiers that cannot be shared openly and that
Speaker 2: should stay in the hospital. And voice prints are listed.
Speaker 2: When you go into what a definition of a voice
Speaker 2: print is, it's very nebulous. It's you know, we don't know.
Speaker 2: So because of that nebubularity.
Speaker 1: If I have that word, if that's nebulosity, I'm fred,
Speaker 1: I don't know.
Speaker 2: Because it's so nebulous. A lot of institutions, a lot
Speaker 2: of hospitals will say, well, you know, voice is not
Speaker 2: is not an identifier as long as you don't say hi,
Speaker 2: I'm John Doe and I live at four twenty five,
Speaker 2: blah blah blah. Other universities will say, no, no, no,
Speaker 2: voice is always an identifier. You can never really least
Speaker 2: voice data. So what our group is doing right now
Speaker 2: is really looking at why the hippo law says this,
Speaker 2: what are the actual legal implications of sharing voice? And
Speaker 2: we always grade it in terms of risk, Right, if
Speaker 2: I talked about all the things that we collect, you
Speaker 2: can think that the respiratory sounds are probably very safe
Speaker 2: to share versus a speech sample. As we say free speech,
Speaker 2: it's probably the most identifying if you have to grade it, right,
Speaker 2: And we're kind of looking at, well, where is the balance?
Speaker 2: How much can we release? And also we can transform
Speaker 2: the data, so for example, we can change the data,
Speaker 2: the audio data and what we call visual spectrograms.
Speaker 1: Like a waveform.
Speaker 2: Yeah, it's a sort of waveform that machine learning can use.
Speaker 2: We can extract acoustic features, right, like loudness, frequency, stuff like.
Speaker 1: That, and basically trying to figure out how to make
Speaker 1: a person be not identifiable based on their voice without
Speaker 1: messing up the database. Like that's the balance, right, Like
Speaker 1: if you monkey with their voice too much, then your
Speaker 1: monkey with the data, the database that we care the
Speaker 1: most about. Like that seems like a hard trade off.
Speaker 1: So if we go farther out into the future, you
Speaker 1: solve all these problems, you build your giant database, the
Speaker 1: models get really good. All of these things seem like
Speaker 1: things that may well happen. I'm curious about, you know,
Speaker 1: AI doing some chunk of what you do now. Right,
Speaker 1: we see this happening, say in radiology already. AI is
Speaker 1: clearly very good at doing some of the technical work
Speaker 1: that radiologists do in diagnosing scans of patients. Right, how
Speaker 1: do you think about the future of AI, you know,
Speaker 1: using acoustic biomarkers to make diagnosis in a way that
Speaker 1: is similar to what you do now as a human being.
Speaker 2: Yeah, So, I mean, I I don't think I'm going
Speaker 2: to lose my job yet because I would say that
Speaker 2: my primary goal as a doctor is not to necessarily
Speaker 2: do that, right, Like, my primary goal is, yes, to diagnose,
Speaker 2: but it's to treat patient. So for now, AI is
Speaker 2: not going to treat the patient. So I think what
Speaker 2: it's going to do is it's going to support a
Speaker 2: lot of the workforce. So for example, I'm an academic laryngologist.
Speaker 2: I'm a super a super specialist.
Speaker 3: Right.
Speaker 2: For people to get to me, they often see like
Speaker 2: five different doctors.
Speaker 1: So instead of going through for specialists who can't figure
Speaker 1: it out, you go to your primary care doctor or
Speaker 1: even you just talk to your phone, and your phone
Speaker 1: says you better talk to your primary care doctor, and
Speaker 1: your primary care doctor sends sends the patient directly.
Speaker 2: To you, correct, correct, right to say like hey. Because again,
Speaker 2: most of what we do for a very long time
Speaker 2: will will need a gold standard right diagnosis. So often
Speaker 2: you know it's it's a it's a biopsy, or it's
Speaker 2: a it's and imaging.
Speaker 3: You need a gold standard.
Speaker 1: The acoustic biomarker is not a clear enough diagnostic technique.
Speaker 1: You need something more reliable.
Speaker 2: So I don't think it's going to you know, no
Speaker 2: doctor will say, oh, well, based on this, this is
Speaker 2: your diagnostics, start chemotherapy. That's not where we're going. I
Speaker 2: wouldn't take chemotherapy based on an acoustic biomarker. But it's
Speaker 2: hopefully going to support a lot of primary care and
Speaker 2: access to care to get to the right person faster.
Speaker 1: Great anything else we should talk about.
Speaker 2: The one thing we didn't talk about, I guess I
Speaker 2: talk about this all day, so sometimes it's hard to
Speaker 2: remember what I've said in iowha haven't said. But the
Speaker 2: implication for probably all this new telehealth you know, online
Speaker 2: world that we live in, a lot of industries are
Speaker 2: already integrating tools. So, for example, Canary Speech is a
Speaker 2: startup that sold a product. I think they're working with
Speaker 2: teams to capture if there's signs in your voice of depression.
Speaker 1: Teams meaning Microsoft teams, like Microsoft's version of Zoom.
Speaker 2: Yeah, yeah, so I think And don't quote me on
Speaker 2: the particular. Maybe I'm you know, I'm not giving, but
Speaker 2: but I know there's a few startups that are starting
Speaker 2: to integrate products in Zoom or in teams to let
Speaker 2: employers know that, hey, your employee is not doing well
Speaker 2: based on his voice, for example, Right, and.
Speaker 1: What is your view of the efficacy of those?
Speaker 2: So, I mean, I I the easy the quick answer
Speaker 2: is it probably works partially. Yeah, But the question is
Speaker 2: not if it works full you're not. The question is
Speaker 2: does it make a difference? Right, So let's say let's.
Speaker 1: Do what they say it does. Is a question that
Speaker 1: matters to me, right, like does it are the claims valid?
Speaker 1: Seems like a reasonable starting Yeah.
Speaker 3: I think so.
Speaker 2: So. I just I just reviewed one an article of
Speaker 2: one of the startups Fantastic that's looking at like depression,
Speaker 2: and I mean their numbers look great. I do think
Speaker 2: that's that the results that a lot of these projects
Speaker 2: are getting are definitely positive and promising.
Speaker 1: Absolutely, we'll be back in a minute with the light.
Speaker 3: M h.
Speaker 1: Now, as promised, we're back with the lighting around. What
Speaker 1: was your band called?
Speaker 2: Ha, My chase stage name was Ella Bence Ella Bence
Speaker 2: because my last name is Ben Susan so that was
Speaker 2: too long.
Speaker 1: And yeah, al became Ella.
Speaker 3: Yeah, I could say my first name.
Speaker 1: What did you have a hit song in French? What
Speaker 1: was it called?
Speaker 2: I wouldn't call it a hit song. It was called
Speaker 2: annalis samp means I guess in English, it's like a
Speaker 2: one way flight.
Speaker 1: Can you sing a line?
Speaker 3: No, that's my previous life.
Speaker 1: Can you just say a line? Yeah?
Speaker 3: I was Uh, it's in French, though I know it'll
Speaker 3: sound great. Yeah, And nalisam.
Speaker 2: Means get me a one way flight for the other
Speaker 2: side of the world. I hope people are really happy there.
Speaker 1: Well you're here, now, you're you're you're in Tampa. Now
Speaker 1: did it work out as hoped?
Speaker 2: Oh? Yeah, I mean I have I have the best
Speaker 2: job in the world, you know. I get my my
Speaker 2: mom raised us me and my brother saying you guys
Speaker 2: need two jobs, one that make money, makes money and
Speaker 2: the other one that makes you really happy. And if
Speaker 2: you manage to have both in one job, then you'll
Speaker 2: have made it, you know. And I get to be
Speaker 2: a surgeon and work with voice and voice professional and
Speaker 2: you know, it's been my passion pretty much all my life.
Speaker 3: So yeah, do.
Speaker 1: You work with professional singers as a physician?
Speaker 2: Absolutely? I mean I love treating my professional singers, so yeah,
Speaker 2: I love that part.
Speaker 3: Of my job.
Speaker 1: Have you treated anybody famous? Yes?
Speaker 3: But I can't tell.
Speaker 1: What's Taylor Swift really like?
Speaker 3: Oh that I don't know. I wish No.
Speaker 2: She sounds fine, though she probably doesn't need a laryngologist.
Speaker 1: What's the best cure for a sore throat?
Speaker 2: Voice rest and advil? It takes the inflammation away.
Speaker 1: Uh huh? Advil? Just ibuprofen and don't talk and voice rest.
Speaker 3: Yes?
Speaker 1: Okay? Are you just always involuntarily diagnosing people based on
Speaker 1: their voice all the time? Okay?
Speaker 3: So funny funny fun fact.
Speaker 2: Two months ago, my girlfriend from residency called me. I
Speaker 2: hadn't spoken to her in like a year and a
Speaker 2: half and she called me and she said hi, And
Speaker 2: I said, you're pregnant?
Speaker 1: Really?
Speaker 2: And I could hear it because pregnancy gives you like this,
Speaker 2: you know, you get stuffy in a certain way in
Speaker 2: your nose, like we call it rhyanidis of pregnancy.
Speaker 3: And I knew her voice very well.
Speaker 2: She was my girlfriend for a long time, you know,
Speaker 2: we studied together, and she just I knew it, you know.
Speaker 2: And and I think she says, hey, how are you.
Speaker 2: I wanted to talk to you, and I'm like, you're pregnant,
Speaker 2: and She's like, how.
Speaker 3: Did you know? So?
Speaker 1: Yes, that's amazing people.
Speaker 2: I mean I was listening to the political debates, you know,
Speaker 2: and I'm like, ooh, this guy needs a laryngologist. I
Speaker 2: could I'm diagnosing people all the time.
Speaker 1: Well, they should give you a call. Yeah, absolutely, Okay,
Speaker 1: lovely to talk with you.
Speaker 2: It was one of the funnest interviews I've done.
Speaker 1: Yeah, Albin Susan runs the Health Voice Center at the
Speaker 1: University of South Florida. She's also a principal investigator on
Speaker 1: the Bridge to AI Voice Project. Today's show was produced
Speaker 1: by Gabriel Hunter Chang, edited by Lydia Jeane Kott, and
Speaker 1: engineered by Sarah Bruguer. Just a quick note, We're going
Speaker 1: to be taking a break for the next couple of weeks,
Speaker 1: but we will have an episode in our feed next
Speaker 1: week from our colleagues over at the Happiness Lab that
Speaker 1: is timed not coincidentally to World Happiness Day, which I'm
Speaker 1: informed is on March twentieth. I'm Jacob Goldstein and we'll
Speaker 1: be back soon with more episodes of What's Your Problem