Mastering Voice Technologies in Healthcare
Episode 501

Rupal Patel, Founder and CEO at VocaliD

Mastering Voice Technologies in Healthcare

Leveraging advances in machine learning and voice analysis to create inclusive, diverse and brand-aligned voices for individuals with special needs

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Mastering Voice Technologies in Healthcare

Episode 501

Recommended Book:

Voice Technology in Healthcare

Best Way to Contact Rupal:

LinkedIn

Company Website:

VocaliD

Mastering Voice Technologies in Healthcare with Rupal Patel, Founder and CEO at VocaliD transcript powered by Sonix—easily convert your audio to text with Sonix.

Mastering Voice Technologies in Healthcare with Rupal Patel, Founder and CEO at VocaliD was automatically transcribed by Sonix with the latest audio-to-text algorithms. This transcript may contain errors. Sonix is the best audio automated transcription service in 2020. Our automated transcription algorithms works with many of the popular audio file formats.

Welcome to the Outcomes Rocket podcast, where we inspire collaborative thinking, improved outcomes and business success with today’s most successful and inspiring health care leaders and influencers. And now your host, Saul Marquez.

Saul Marquez:
Welcome back to the podcast, Saul Marquez here, and today I have the privilege of hosting Dr. Rupal Patel. She is the Founder and CEO of VocaliD, a voice tech company that provides AI-generated voices with personality. VocaliD’s Award winning technology leverages the latest advances in machine learning, signal processing and voice analysis, along with the company’s crowdsourced voice bank to create inclusive, diverse and brand aligned voices, organizations and individuals with special needs. I know I hate that voice, that robotic voice, when you dial a number or you try to connect here your physician’s office, or even if you’re doing a training video, it’s annoying and it feels like it lacks effort. Rupal and her team didn’t like it either, and they felt there was an opportunity to get beyond just content and focus on delivery and the technologies here. We just have to create the awareness. She began her career as a speech clinician where she became fascinated with the potential of using speech tech and technologies for assistive communications, which then led to a doctorate in speech science at her Interdisciplinary Research applies empirical evidence about speech motor control to develop novel communication technologies. She’s currently on leave from Northeastern University, where she’s a Tenured Professor, and also she does computer science in the Department of Communication of Sciences and Disordered. She’s been named the Top 11 Visionaries in Voice in 2019 by a Voicebot.ai and Fast Company’s 100 Most Creative People in Business. She’s been featured on TED, NPR, and major international news and technology publications and today on the Outcomes Rocket. So it’s such a privilege to have you here. Rupal, so glad that you’re able to join us.

Rupal Patel:
Thank you so much Saul, thank you.

Saul Marquez:
So tell us, Rupal, what is it that got you focused on unvoice and the audience that we have here today in health care, voice in health care?

Rupal Patel:
Yeah. So I am speech scientist, have been interested in what people with severe speech disorder can still actually do with their voice. I find it is one of the ways. So think about this is when people have speech disorders. It’s you look at the glass half full or if you look at it half empty. And I think that there’s a lot of residual motor control that these individuals still use and want to communicate through a voice no matter how impaired their speeches. And so we really started to think about what can we harness that capability even if you can’t understand what the words are, can you harness their individuality and tailor their assistive communication devices such that they don’t know have to sound the same because voices identity, it’s such an important part of who we are that we wanted to capitalize on that and give people a unique voice for their communication modality.

Saul Marquez:
I think it’s such a such a powerful work that you’ve dedicated your life to Rupal and I’m curious how you and what applications there are in the in the medical sector.

Rupal Patel:
There actually are a number of applications. So what we started off with in is creating unique synthetic voices for people who have to rely on assistive communication devices. So imagine a child on the autism spectrum or someone who has cerebral palsy who is unable to use their natural articulators to communicate and have to type out messages using either their fingers or switches or their eyes. Whatever motor control they have to build together messages that will then be spoken aloud by the on board speech synthesis. Many of those devices are sort of hardware or software combinations are clunky. Devices will have an on board voice and up until recently there’s very few voices that people could choose from. And so what we were trying to do is make those unique to that individual, to the recipient by taking whatever speech that they still could produce and then finding a surrogate talker for them and blending what we call a bespoke voice for them that they could use on these devices. Because if you go into a classroom of a number of kids who have these special needs, you’ll often hear the same voice coming from multiple directions, you know. And it wasn’t until recently where it was common practice, actually, to just have an adult male voice on people’s devices, regardless whether they were a young girl or a grown up boy or whatever. Right. And so the problem was that, you know, you’re giving them a prosthesis to communicate and yet you’re kind of making it just as functional box. Whereas if you think about differentiation, what our voices do for us in terms of our our social identity, they didn’t really have that access. And so that was our first group of individuals that we wanted to serve in the health care domain as we started developing this technology within within the company VocaliD. So VocaliD, it was initially a research project with in my lab at Northeastern University. And then in 2015 we spread it out too, because we felt like that we could help more people too, if we could really commercialize the technology behind it. And we’ve gotten to a certain level within the university and research that we were doing to be able to craft a voice. What we couldn’t do was scale it where we weren’t able to really make a voice affordably and as as we’ve developed the tech more within the company, we have been able to make major gains along those lines. So our first population was people who never really had a voice or had very limited speech communication from birth. But what we found is that as we created these online platform plate to record your voice for healthy talkers to donate their voice for people who couldn’t speak and so on. We found that some people were coming to us say, well, I know I’m going to lose my voice to head neck cancer or I have a progressive speech disorder and I want to make my voice for myself should I need it in the future. And that’s become a verilli. I think we didn’t really think about that as a major application of space because we felt like that already existed in some way. It was it really was really rocket science. So we weren’t pursuing that initially. But I feel like that’s one thing we’ve been able to really scale out because we can have people record their voice in their home in a quiet environment. And we built up the platform to do that voice breaking in such a way that it’s it’s very simple to do so now. Twenty seven thousand people from around the world have contributed their voice, the Voice Bank, many of whom are also losing their voice. So it’s not all 27000 are those individuals in those conditions. But I do think that we’re helping that population, too.

Saul Marquez:
Yeah. I mean, just real quick Rupal and I think it’s it’s a fascinating journey and starting with the kids in the classroom to people losing their voice. Voice is a very interesting thing. What would you talk to the listeners about as far as things that maybe they don’t know? Like, you know, we had a chance to connect before the podcast. You talked about voice strategy. You know how people maybe aren’t thinking about that. I mean, what are some nuggets that you can leave the listeners with around voice?

Rupal Patel:
Yeah. So within the last couple of years as the voice technology, the synthesis technology has been improving. We’ve also been thinking about broader market applications within healthcare. So let’s say you have a health care application where you’re talking to with an app, will be speaking to this system, will be speaking to someone with dementia. Now, if that voice was a familiar voice, maybe a family member’s voice, the ability to trust the information that is being relayed to the patient with dementia might be increased. Right. Or if you think about a pharmaceutical application where potentially there’s the doctor or the pharmacist that is providing the the content. So I think when you think about voice now, these days, we’re often thinking about how is it that the content within health care is being is reaching the customer at the at the end points. It’s either through text and more and more it’s through voice because often these applications are relayed through a smart speaker or a device that speaks and so on. The voices cannot be generic because voice is identity, voice is trust, voice is convenience, I mean, there’s a lot of things around voice that companies need to be thinking about from the get go. Often I think not just in health care, but many times people think that it’s the content that matters. Like you find the right information that has to be relayed. That’s true. Content does matter. But how it’s delivered is critical to whether the consumer is going to believe it, understand it, trust it. All of those things are really important to the strategy. And I think I hope that people are starting to think about that now.

Saul Marquez:
Oh, my gosh. Yeah. And, you know, the voice is here to stay as far as technology. I mean, you know, the other day, Rupal, I was giving my son a bath. He’s 2 and he grabbed one of his little characters and he is like Forkie sing a Christmas song. Buddy it’s not Alexa right..

Rupal Patel:
He thought everything is gonna be talking to him.

Saul Marquez:
He’s like, how come voice doesn’t come out of this thing?

Saul Marquez:
The reality is that we’re gonna have more and more of that. It’s become an expectation and how we handle voice for the populations that we’re caring or the individuals that we’re caring or at the at the physician level and even at the enterprise level. If you’re if you’re building a solution that’s based of voice is going to differentiate you.

Rupal Patel:
Absolutely. Absolutely. And I think that that’s really important. If you think about it, why do human beings have different voices? Right., we could all had the same voice, because if you just think of that voice as a functional thing, just an output, then we don’t all need different voices. But we do because it actually helps us indicate everything from the hunter-gatherer phase to where we are today. There’s a differentiation between the size of your body will change your voice, whether you’re gender, socioeconomic class, your usage habits of your voice. All this differentiation actually then ends up being part of the social norms around how we think about voice too Right.. So I think there is, I feel like voices untapped really in terms of the healthy ecosystem. The other thing about voices is also a bio marker. So, right. It’s just that voice coming at you, but also thinking about as we consumers interact with health based technologies, our voice is saying something about our physical health, about our mental health, about our psychosocial health. There’s a lot that is within voice that we don’t fully utilize today.

Saul Marquez:
I totally agree with you. And more companies are coming out in that space as well. Ginger.io, for example and there’s other slide. Yeah. You’re so right. It’s untap. And what we could do with it is super interesting. So tell me a little bit about maybe a health care application that you guys have launched or or something that you guys have done in health care. Give me a story and how it’s made a difference in outcomes or quality of life or even improving business for for one of your customers.

Rupal Patel:
So we’re really early on with health care applications. I feel like health care in many ways is, I think, more lagging than the other verticals in terms of the adoption of unique and custom voice. Right now, we’ve had many conversations that would often boils down to is that customers will think that, well, we’ll just use Google or an Amazon poly voice because really it’s all about the AI behind whether we can find the right symptoms or tell people about whatever the condition is. And we’ll deal with voice later. This sort of thinking about voice as this functional thing that they’ll slap on later is really missing this opportunity to connect. So health care, at the end of the day is about building a trust relationship with the customer. And you can’t just do that with them, giving them their correct information or the appropriate information. It really matters. Like what’s it why does bedside manner matter for physicians and for clinicians to develop over time? You can have the same clinician, have the same amount of knowledge, but the one with the better bedside manner is the one that the patient’s going to keep going back to and comply with and so on, so forth. It’s really important stuff. Skills matter, and I think it’s about time that in health care we start thinking about these soft skills as as vital as opposed to just the accuracy of the content.

Saul Marquez:
Well, I think it’s it’s it’s a great call out. And what would you say today is is one of the proudest accomplishments that you and the team have done with the business and the technology?

Rupal Patel:
I think our proudest accomplishments has been really to be focused, laser focused, on improving the quality of the synthesis. So there’s a lot of asks when you talk about building out a synthetic voice and you embedded within this device that is four year old technology or can you put it within our calls that are calling system or IVR systems Right. and can it integrate? Oftentimes, this is reverse engineering into whatever legacy software that they’re already using. But we’ve said, you know what? That will come. That’s engineering. Our focus is on making these voices realistic sounding, making sure that the quality is high enough, because until that bar is met, the rest of it, it really doesn’t matter. You can integrate all you want, but it needs to be something that is acceptable and usable. The other thing I’d say is with population of people with with speech disorder that we’ve been helping, we have had to do the integration. And so they can’t use it unless they can use it on their phone or near assistive communication device. And one of the things is we’ve challenged ourself with trying to really do this at scale. And I think that helps us bring cutting edge technologies to people with disabilities, which is core thing about our mission. We don’t think that people with disabilities should have access to second class technologies. We think that they should be able to have access to the bleeding edge. And I’m proud of that. I think that we’re we also need to have applications in the broader market in order to continue to sustain the kinds of development that we’ve done. But I think our team, small and nimble, has been really focused on improving the quality of the synthesis.

Saul Marquez:
Love it, Rupal, and I’m a big audio-book fan and I also enjoyed podcasts. And I’m wondering just kind of as a as a side thought on applications like can you give this technology the ability to quote unquote, read a book to make it an audio book without somebody having to stand in front of a microphone?

Rupal Patel:
So I think there are good applications like better sweet spot applications for synthetic speech as it is today. So the newest methods of synthetic speech we would call in the realm of AI voices rather than traditional synthetic speech. So let me just give a high level primer about this.

Saul Marquez:
Sure.

Rupal Patel:
So synthetic speech as we know it and the stuff that we are interacting with today is usually concatenative, meaning that it’s glued together bits of pre-recorded speech. And of course, in the case of Siri and Alexa, they’re really high end versions of glued together synthetic speech. So you have someone record a lot of speech and then you cut their speech up into the elemental components. And then when you hear or when someone types in a word that is unknown or it hasn’t been seen before, such as my name, Rupal, and you want to figure out how to say Rupal, you’ll have to find the Ru from Rabbit and the u from Uber and the perf from somewhere else and glue those bits together and make it not sound like it’s been glued together. That’s what concatenative speech synthesis is. And most of the speech synthesis that we hear in our world today is concatenative speech synthesis. In the last 10 years or so, maybe a little bit longer, there’s been a gradual development of new methods flying machine learning methodologies to speech synthesis. And again, you start with human recorded speech. Actually, you need a lot less of it in this case. But what you’re doing is mathematically modeling that speech, you not gluing together a little bit speech. You’re trying to recreate or emulate that speech. So, again, when you encounter a novel word, you can say it. But it’s not from the concatenation of the elements of speech. It’s actually from thinking about what is the R sound like in the or the sound like in these different contexts and kind of modeling that. So when you think about what the capabilities are in that way, you now ar also the modeling not just how the sounds are, but also the melody of speech, the cadence of someone’s voice and so on. So what you can do with the newer methods of speech synthesis now is that, you can build models of speech with less data and more economically than we’ve been able to do in the past. That also means we can have a variety of voices for applications rather than just saying we only need one voice for all of our IVR system. Think about what we’ve been doing. I mean, I think right now we’re saying there is one voice associated with one brand. Problem is that your customer base is is much more very different. Right.. So if you’re talking to all your customers in the same way, you’re not necessarily resonating with all of them. So that’s one thing that with these newer methods, we have the opportunity to change speaking styles, change the rate of speech. Your elderly people might benefit from this the voice speaking to them a little bit more slower way with more exaggerated prosody, whereas someone was younger and millennial can’t wait for that thing to be finished saying what it means to say that it have a lot of wait to hear that prompts slowly and deliberately. So I think it’s important that we think about who we’re talking to and how we’re talking to them.

Saul Marquez:
Yeah, I think that’s a very insightful. And the features is bright to different ways of doing it through mathematics. Or I came. I don’t even know how to say it concatenate.

Rupal Patel:
Concatenate, yeah the concatenation you know.

Saul Marquez:
Fascinating piecing it together or through machine learning. There’s some really neat things going on in the space. What would you say is is an example of of a setback you’ve had fallen and what did you learn from it?

Rupal Patel:
Yeah, I mean there’s so many. It’s a roller coaster when you’re develop a company or in this path. I think there is one of the biggest setbacks is really thinking about how we go from this niche application that we started with to then bringing that to the broader market. And it’s also a new area. So often people don’t know how they’re going to use it. So there’s a lot of tire kicking to figure out, well, can I use this voice application or will I just use something from that already exists? And so you invest in trying to educate someone about what the capabilities of the new technology are. And then there’s still kind of like, well, we’ll just go with the default right now. So that’s challenging. I think we’re learning, though. I think those are very, very important lessons for us because they help us tell the story better, to craft the narrative better and to understand the value proposition that we’re proposing. This is really new frontier. So patience’s is going to be really important to get us to where we want to be.

Saul Marquez:
Agreed. What would you say on the other side of that coin? One of your proudest experiences or accomplishments with the technology?

Rupal Patel:
I think in the last I would say the last year, the cadence with which the improvements in the technology have been have been realized within within the company, have really helped us differentiate what we can do. And so I think recently I created a sound sample as a demo which had both the synthetic voice of the individual as well as their prerecorded recordings that were used to create this person’s voice. We made a medley of a sample that included synthesized voice and pre recorded sample in a paragraph and I sent it to a customer. And the response I got that was why are you sitting in this file? It’s prerecorded sample like is now recorded. It’s actually partly synthesized, partly prerecorded. And that was the first time they went.. Oh, that’s what possible now. And more recently, as I’ve been when I give my talks, I kind of start the talks with playing samples and asking people if they can tell the difference between which one’s synthesized and which one is prerecorded. And the answers recently have been kind of like fifty fifty people. People don’t know. So that certainly wasn’t the case a few years ago, even like 2 years ago, I would say that there was more disappointment than excitement, and we would we would be excited because we’d made this voice out of such little data and for such a economical scale that it was, you know, when you like looking at the work that you do yourself and you’re so proud and self-congratulatory and then you send to someone and they’re like, oh, that’s it. So it’s great now to go have that wow factor. And then I think also, you know, we’re seeing that people are understanding it more like what they could use it for. So that’s exciting.

Saul Marquez:
Yeah, some big wins there Rupal and the journey continues, what would you say is the most exciting project that you guys are focused on today?

Rupal Patel:
Oh, how can you pick one amongst all your favorite children? Right. What is the most exciting? It’s really hard. I think some of the work that we’re doing around building a brand voice for companies where they’re really thinking about everything from what should a persona be or what should the personas be for their different market segments. I think that’s a real opportunity for us not to be only thinking about what we bring to the table in terms of the technology, the building of the AI voice in that at the end but this set of AI voices. But I think we’re we’re very involved these days in the voice strategy and the early part of the designing of the personas, trying to understand, applying our knowledge of the psychology of different voices for different populations and for different segments of the market. I think that we’re using our expertise both in the technical realm but also in the scientific as well as the clinical realms that we have expertise in already. So I think that’s really exciting to me. It’s the combination of the skills that the the domain expertise, but also the technical expertise that we’re bringing to the table with a couple of the newer projects that we’re doing these days. These are bigger projects that will take some time to roll out. But I’m excited about that sort of seeing the full scale of that. Everything from testing our voices with the customer market segments prior to building out the voice personas I think is thinking about this from beginning to end.

Saul Marquez:
Now, that’s really neat. And how about the rise in invoicing like passcodes? You know, I find that banks are doing this now. What are your thoughts on that? And I’d like to hear what you think about that area.

Rupal Patel:
Yeah. So actually, a couple years ago, we were pursued by a large bank to create some voices to see whether we could do some white hacking. So basically decent penetration test to see if our voices, our AI voices were going to be capable of spoofing the system because all these banks have started using voice authentication as a way to do accessing this system. And what they were worried about is because we were one of the vendors that was really creating synthetic voice, so economically as well as quickly from very limited amounts of data. The worry was could you just scrape together voice content from the Internet, create a voice, and then use that to then break into someone’s voice bank account. So we took that on as as a way to see where was the technology at that point. We actually were able to demonstrate for some voices that we were able to get through the voice authentication system because those systems are meant to be barriers to the concatenative methods of synthesis, but couldn’t deal with the fact that newer methods of synthesis are approximating the speech signal in a way that they didn’t expect right. So we showed that and actually for about 8 months we had this we pursued trying to be able to create an offering for that for the financial market, which would not only do the pen testing, but also prevent the voices from being misused. So counter-measure technologies. We built a countermeasure technology and so on and pitched it to these various different banks. And after 8 months of sort of doing that, all of them wanting to have conversations with us because they knew that we were showing them something that they didn’t know. But they have risk tolerance ratios in these banks where or asset management companies where unless the fraud level is high enough, they’re not going to invest in this. And so if I go back to answer one of your questions earlier, it was a big setback. What setbacks was following this pivot for a while, thinking that it was going to be our broader market application that we could use to then commercialize some of the health care applications that we wanted to work on. It didn’t pan out so well. And it was it then led to some funding that we weren’t able to get. So that was actually a step back for us. What we realized, though, that we actually had to build in technologies that were a ways in which to protect our voices from being misused. And that’s been a really great learning. In fact, from there we’ve developed this myself or vocal idea, and a company named Modulate AI would say also a synthetic media company in the Boston area have built this consortium called AIthos, the AI ethos. What we’re trying to do is create a consortium of companies that are in the synthetic media space that are proactively thinking about misuses of our technologies and building new technologies that can prevent that, creating awareness campaigns, working with government and policy makers. All of these things in order to make sure that these new technologies don’t have these unintended consequences that we have to deal with down the road. We can proactively think about managing them as we go. So AIthos has come out of that work, which is great. It’s a very new consortium that we’re building out. But I think that there’s going to be a lot of ethics when we think about voice specially for health care. We are gonna be needing to balance privacy, innovation and the ethical complications of what happens when the technology starts becoming more realistic sounding without erode trust, will that complicate things? What happens to cognitively challenged individuals when they’re trying to consume this information.

Saul Marquez:
Damn, that’s a really big challenge and the need that you guys have started to tackle it. So then you guys pursued the bank route. So what was the I guess, what was the gap that led to it not happening? I guess I’m not clear on what exactly was it that threshold wasn’t met high enough threshold or what? Why did it not work?

Rupal Patel:
Yeah. So it worked, I mean, we were able to we were able to demonstrate that there was a gap in current voice authentication technologies for the newer methods of speech synthesis. So we were able to demonstrate that the reason that it didn’t work in terms of a commercialization or a plan for that for us was because there isn’t sufficient voice fraud right now for banks to implement the counter-measure solution. So I mean, we can obviously do the penetration testing for them and show that here’s the latest and greatest technology can get through it. It can build all these different stopgap measures so that they don’t get hacked. But for them, they’re like, well, how realistic are voice hacks? Right. How many people are trying to hack the insurance companies? Voice out systems, right. And the answer at that time this is early 2018 was not enough.

Saul Marquez:
Yeah, we’re clear on it now. Makes a lot of sense.

Rupal Patel:
Yeah.

Saul Marquez:
So it was, it was more of a product to prevent. A countermeasure.

Rupal Patel:
Yeah. As a countermeasure. And they’re like well.

Saul Marquez:
Thanks for clarifying that.

Rupal Patel:
Yeah. We know that we’re going to need it someday, but we don’t need it quite yet. You know, and I think if were talking about health care data though. So it’s one thing to have your your bank been broken into, obviously, that’s not a great thing,right. Your bank account being hacked. But now if someone has access to your health records because they can impersonate your voice. I think that there’s the you can’t get some of this data back. I have.

Saul Marquez:
Right. Once it’s shared, it’s shared.

Rupal Patel:
Yeah. And your voice is your fingerprint in the way that they’re using it for the voice systems. Right. Voice is your password. I mean think about all the marketing that’s being used around it. So it’s not like you just changed your password. You can’t just change your voice. It’s that part of what’s being used.

Saul Marquez:
So you mean you can’t you can’t change your voice, right? Or can you?

Rupal Patel:
I mean, can you we manipulate our voice to a certain degree. But, you know, those these authentication systems are robust to the way you say it is a signature of your voice, the fingerprint of your voice, right, the voice print.

Saul Marquez:
Your voice is your voice.

Rupal Patel:
Yeah. And so I think that it’s really I don’t know that many insurance companies or banks right now that are thinking about this, but it’s got to be on their radar.

Saul Marquez:
It better be.

Rupal Patel:
It better be because otherwise I can be caught off guard. And that’s going to be a huge loss, not just monetarily, but, you know, think about what are we going to do for these people whose voices have been hacked?

Saul Marquez:
My gosh. Yeah, man, that’s crazy, Rupal, OK. So we could talk about this for hours, so let’s reel it back in. And so I’ve got a lightning round for you and we’ll follow that with a favorite book and we’ll conclude. Does that sound good?

Rupal Patel:
Sounds great.

Saul Marquez:
Awesome. What’s the best way to improve health outcomes with voice?

Rupal Patel:
I think you think about the dimensionality of voice. And think about where the touch points are with voice. I think that including a nuance version of voice or thought, a strategy around voice on what it can do for you, I think would be really important.

Saul Marquez:
What is the biggest mistake or pitfall to avoid with voice in health care?

Rupal Patel:
Thinking about it at the end. Voice strategy should be part of the beginning of the design of the product.

Saul Marquez:
How do you stay relevant as an organization despite constant change? And I’m thinking Rupal like using voice. What do you do to differentiate yourself as a company?

Rupal Patel:
We’re constantly thinking about what if? What are the latest and greatest open source tools. What are the applications. What are the newer methodologies. What a ways its gonna be used for, but still being laser focused on what we need to accomplish in the next 3 months,6 months and year. How do you think big but still stay focused.

Saul Marquez:
Yup yup, love that. And what’s the one area of focus that drives everything at your company?

Rupal Patel:
Quality. Quality of synthesis.

Saul Marquez:
Doesn’t get any better and clearer than that. What book would you recommend to the listeners? Rupal.

Rupal Patel:
Just a fun book or a book around voice.

Saul Marquez:
You name it. Maybe one and one of each, a fun one and a voice one.

Rupal Patel:
Ok, so in terms of voice, there’s a new book coming out called Voice Technology in Health Care, and the editors are Terry Fisher, Harry Pappas, David Metcalf and Cynthia Portray. And I think that that is going to be a really nice compilation of a variety of different technologies, but also applications for voice and health care. And then in terms of just other reads, I love this book by Jhumpa Lahiri called Interpreter of the Maladies. It’s a little old, but it’s one of my favorites as well as The Alchemist, if I could recommend to.

Saul Marquez:
Love it. Some great recommendations. Haven’t, I love The Alchemist, but The Interpreter of Maladies, that sounds like an interesting one. Is it kind of like same feel as as the alchemist?

Rupal Patel:
No. So the Interpreter of the Maladies is some short it’s short stories. It’s this brilliant writer, Jhumpa Lahiri, who brings the sense of the individuals in the stories just to life with the amount of detail that she creates. And I think it’s what I love about it is that each of the different characters has this what I think of as I could hear their voice as she is, she’s describing them. So I think it’s it’s great. As you’re reading different texts these days or books, thinking about what that voice would sound like to you in your mental concept of the voice.

Saul Marquez:
I love it. Great recommendations. And folks, you know where to go for the show notes just go to outcomesrocket.health in the search bar, type in Rupal and you’ll be able to find all of the things that we’ve discussed today. Rupal, leave us with a closing thought and the best place where the listeners could get in touch with you or learn more about what you do.

Rupal Patel:
So closing thought for me would be that we think about voice and identity in a way that is sort of all encompassing. Don’t think about just voice as a signal that’s that output. But think about all of the different things that voice conveys. And I think that there is going to be more tension to voice in the future around this concept. Get in touch with us at VocaliD.ai. If you’re interested in bringing your voice or sharing it with someone in need. Go to VocaliD.ai/voicebank. And if you’re looking for more applications for your product or strategy around voice, email us at hello@book.data

Saul Marquez:
Outstanding Rupal, I will I really appreciate you and and the work that you’re doing and look so excited to stay in touch with you.

Rupal Patel:
Thank you so much Saul, this was fun. Thank you.

Thanks for listening to the Outcomes Rocket podcast. Be sure to visit us on the web at www.outcomerocket.com for the show notes, resources, inspiration and so much more.

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