135. What can we learn about language from computers?

Welcome to the Influency Podcast. I'm Hadar and this is episode number 135. And today we have a special guest that you are going to love because we are going to talk about English fluency, the English language industry, and AI artificial intelligence. And it is going to be so, so interesting. So stay tuned.

Foreign.

Hey, everyone. Welcome back. Thank you so much for tuning in for another episode of the Influency podcast. Today we have a guest that is going to talk to us about language processing and artificial intelligence. Sounds intimidating or fascinating? Probably both. So my guest today, Jazzy, is an English teacher as well as a linguist. She's probably a linguist first, but she's an English teacher at heart and she loves and so passionate about the field of natural language processing and artificial intelligence and the difference between language processing in humans and in computers.

So.

So she will share with us what we can learn from computers when it comes to language processing. And I promise you, it's going to be so interesting. We also talked about my work with Google, helping Google create the pronunciation tool called Pronto, where you can see the phonetic transcription of a word as well as practice your pronunciation, where you click on practice and then you say the word and then you get feedback. So she was asking me questions about that. I was asking her questions about her work. We also talked about the language learning industry, something that we love doing. Jazzy is also a member of English Teachers Academy, my membership for teachers. And this is a hot topic always.

So if you're passionate about improving your.

Communication skills, your pronunciation, and you love using technology for that, and especially, especially if you're an English teacher and you want to know how technology can help you help your students, then this episode is for you. You can also watch the video version. I'm going to post the link to it in the show notes. And today you'll get the best parts out of those interviews as we brought them to you here. So let's go ahead and listen.

Hello, Yazi, how are you doing?

Hi. I'm very happy to be with you here today. Thank you for inviting me.

I'm super excited to have you here. We've known each other for a while and I've been actually waiting for a good opportunity to let you talk to the beautiful people here and share all your beautiful knowledge and everything that you have to share. How are you doing today?

I'm doing fine. I was actually waiting for this conversation a long time ago because I've been following you way longer than you think, and I think that by talking to you, maybe I can also talk to other pronunciation coaches, other people who teach English and who feel that they are very insecure about teaching pronunciation because, you know, it's a difficult topic. It's a difficult thing to do. So maybe I can provide some tools. And while I'm talking to you, I will also talk through you to other people out there directly.

You can speak directly to all the people, the teachers and the learners trying to make sense of teaching pronunciation in a simple way. And since we have such incredible tools out there to help us, there's no reason why they shouldn't know about this and use it in their teaching. So if you're a teacher, you should definitely stick around to learn more about what we have in store for you. So tell us a little bit about yourself.

Well, I'm Mexican. I've been a teacher for almost 20 years. I never want to say that because people start guessing my age. So, yeah, I started when I was 10 years old, of course.

Of course.

And then three, actually. Three, you know, three years old. Yeah, of course. And, yeah, after being a teacher and, you know, getting certifications and being trained and an BA and then a master's in teaching English or teaching with technology, I thought that I needed something else. I didn't think that it was enough, and I wanted to know what it was like to learn a language when you travel to another country. So I decided to travel to Germany and study another ema. I know it was a drastic change because I didn't speak any German at all. And, yeah, you know, like those cliches that people go like, hey, if you travel to the other country where people speak the language, you're gonna learn it the best. And that's not always the case, by the way. So. Right. And, yeah, I love all those beliefs.

Like, you know, if you do this, then that. It's like, no, doesn't work for me.

Yeah, that's not always the case. And then I was. As I was in German, I had this class about pronunciation instruction and everything, and I learned that there's a lot of out there. If you are not a native speaker, maybe you're not trained or you don't have the gift or you don't have enough experience and knowledge to teach pronunciation. And the first thought that came to my mind was like, okay, maybe I cannot do it, but will I be able to bring a native speaker to my small community? And there's remote places in the world where they don't have this lifestyle, they don't have the things they're used to. Will they actually be willing to come and teach pronunciation to my students? And then I thought, hey, technology is out there for me. Maybe I cannot do it, but technology will help me. So I conducted a series of experiments and research throughout my master working with automatic speech recognition and artificial intelligence. It was two year experience and I've been working with it after that for four years. So I've been able to observe the behavior of computers and the behavior of learners. And I think that these insights are going to be helpful for many people. Also, when I was in Germany, I was also a part of the linguistic laboratory of the university. So I had the chance to help other researchers, PhDs, MA students with their linguistic data. And, you know, the more you're in contact with it, the more insights you get and the more you make relationships between the phenomenon and. Yeah, that's, that's why I came from being a teacher to being very interested in what computers can do to help teachers. Right. And then jumping into artificial intelligence. And now my job as a linguist also has to. Well, it comes from there.

Yeah.

It's like, you know, having all that knowledge is so it's like having superpowers because, you know, all aspects, it's not just like the intuitive human part of pronunciation, but you also understand first of all, like how technology can help, which is amazing. And not a lot of people have access to it, like we said at the beginning. But I think that the research really gave you a lot of insights as to how the brain works. Would you say that that's true?

Yeah. Well, just sitting there in the laboratory and talking to the people, my peers or my teachers, who had a bunch of data that they collected and they wanted to analyze. Like even just sitting down in there and helping them learning what they wanted me to do. Like, I'm not an expert and I have never said I'm an expert at anything, but I've been with experts and I know what they look for. And sometimes you don't even realize there are things that you have to look for when you were talking about languages. So there are so many things out there that have to do with language and linguistic analysis that we are not aware of and we just take for granted or yes, we have the feeling that something's going on, but we don't have or we don't have the data to prove that is right or wrong. So just being there, it was an amazing experience. I fell in love with research.

I can imagine, I can imagine. Even though it requires a lot of attention to detail, I would say.

Yeah, like, and every time people are at the lab, you. I also had some wiping napkins in there because, yeah, people get really frustrated. And. Yeah, I was in there. I've been there as well. So I know, it's amazing. I respect research because it's a hard process. It's a really.

Yeah, absolutely. And it seems like an incredible experience to live abroad and to kind of like, immerse yourself in a different culture, working and. And communicating in English, just like, on a personal level. I. I'm assuming that that was really worthwhile as well.

Yeah. And well, before being to Germany, I had the opportunity to travel to the US Also to get the teacher certification as well. But Europe is a different thing. People in Europe speak so many languages, and it's hard to find someone who speaks only one language. So when you arrive in Europe and you go like, hey, I speak two languages. That's my superpower.

Exactly. Seriously, who cares? Yeah, I speak five.

So. Yeah. And the ones you speak are so generic, Everyone can speak themselves. What's the new thing you bring to the table? And, yeah, it was very eye opening. And also it gave me a lot of confidence because before being to Europe, I thought that my English was not intelligible enough. Maybe I still had to work on that. But then when I was in Europe, I met people from all over the world, and just finding out that all of them understood what I wanted to say was enough, and I was happy. I was like, I'm ready to go.

I love that. So let's begin by the first question. What is artificial intelligence?

Yeah. Well, there are many definitions out there, but I will try to give you, like, the simplest one that everyone likes and that everyone understands. Artificial intelligence is the presence in which humans can make machines behave like humans. I mean, that's a very broad one. Right. But if, for example, if you have a camera and you shoot and you shoot a picture and you take a picture, the camera will do processes that humans don't do. Right. I don't. I adjust my eyeball to focus or anything. I mean, it just does it. My eye does it.

Luckily, luckily, we don't have to do that every day we look, wait, let me adjust it.

I have to wear my glasses. So maybe cameras can do it better than that. But anyway, so when a camera can recognize shapes and focus and do what we do naturally, that's when we call it artificial intelligence.

Right, I see.

Yeah. And then artificial intelligence intends to give the machines the processes or provide all the steps so that machines can do things that humans do. For example, see speak here. And they are working on touch and smell.

Wow.

They cannot work. And still they have a lot of trouble working with taste because machines just don't care. But, but yeah, yeah. There's so many aspects of the human nature that are trying to be mimicked by artificial intelligence. Now that, yeah, that's what makes it so amazing. It's like, how do you have a machine do what you do? The thing is that the combination of all these processes is what makes it more interesting. So it's not the same to just have the camera like you. You're an Instagram, and Instagram can just recognize your features and know where your eyes are. And then, you know, apply a filter. But then do that. Do do that and still answer or say your name, recognize your face and then say, hi, Hadar. That's like, oh, God. That's what it makes you feel that this is impressive. Like, the camera is not only recognizing my features, but it's also saying my name. Because that's something we do, right?

Yeah.

When I see you, I say, hey, hi, Hadar.

So a lot of people have like this fear, maybe an unexplained fear around artificial intelligence where, okay, the robots are going to take over the world or our job. And, you know, can you speak to that, That a little bit to that? Why do people, I would say, resist, like, if you've seen that in your field. Right. Whether it's in language or artificial intelligence in general. And what you think about that?

Yeah, well, yes, there, there are so many people out there who feel that first, their privacy is not being respected by AI and they also fear that they are giving away so much information that AI can do something. Them and also the other people who feel that AI is going to get to a stage where it's going to be so intelligent that it's going to be way more efficient than human beings replacing.

I mean, we already have that if we compare it to a few, like years ago.

Yeah, exactly. So there are international organizations right now working against that. So, for example, in Europe, there are very strict regulations on the use of private data. So companies cannot just go and use whatever they want. You have to give them access. But it's not only the user, because sometimes we users don't know what we are actually giving access.

Absolutely.

Yeah. So the institutions, organizations are looking after our rights and they're going like, no, you're not using this. By no means. Not even if the user says, yes, you're not using this. That and also, artificial intelligence hasn't reached the level to be dangerous yet. What artificial intelligence does right now, which I think is the matter, is that it reflects the actual world, so it amplifies what is already there. So there were so many people, so many, like, years ago thinking of. Yeah, but Google translation will translate bad words or chatbots. They will say bad words at you. And it's like, it's not the chatbot, because the chatbot doesn't know what is uttering or what is writing or typing. But it's just a reflection of all the data that is out there online for free, all that, you know, the banks of data. So the machine just looks for the most frequent sequences, the most common, the frequent patterns, and it will bring you that. So you can either use it to your advantage, but we can also see that there's a lot of inconsistencies, but it's just reflecting the world.

It says so much about us, you know, and about. Yeah, yeah, yeah, yeah. Wow, that's really. That's really cool. I never thought about that. So talk to me about artificial intelligence and language processing and learning. So how do you. How does that work? I know that this is what you do, so I know you can't talk a lot about what you do, but a little bit about how it works. Like, how do you teach a computer. Computer to understand a language?

Okay, so as I was telling you, we are trying to teach computers how to hear and how to speak. Okay, so you are a pronunciation coach, and I'm going to test you today.

Yeah. So you have a little presentation also.

Prepared for us for that. Yes, but just like in a general view. Yeah, so computers can extract sound, decode it, get the gist, codify. Like, what is this person asking for? So they can get the keywords. Right. And then they look for those keywords in banks of dictionaries that are built by linguists and computer engineers. And then computers grab the meaning from there. And then the engineers and the linguists told the computer, hey, when you grab this, reply this. So then the computer will look for the answer and try to generate an answer to give you. Like, it can be an action if you ask a virtual assistant to do something for you. Or it can be a reply just in a conversational artificial intelligence. So there are different paths, like, of what language is used for, if it's for you to give directions to a virtual assistant or to have a conversation. So there are different branches in there. But what linguists do with computer engineers is to do that, like codify all the banks of data and annotate and go like, okay, this is a noun, this is a verb. The person is asking you for a thing. The person is asking you for information. So we find which words from a language convey asking for something. Oh, okay, all these, this and that. And then linguists give all this data to the computer scientists and they code it in such a way that the computer can retrieve it whenever it's necessary and just bring it to you on display or do whatever they want. This process is called natural language processing. And it has natural language understanding, which is the part of the computer that will learn to hear, and natural language generation, which is how the computers will.

The production.

Exactly, exactly.

Have we reached the level where a person can have a real conversation, full conversation with a computer?

Yeah, but actually, yeah, I was reading a paper. There were some latest releases from Google. Let me tell you something. Ten years ago, computers were really bad at understanding and replying. Five years later, they improved a lot. And then two years later, they amplified their databases. And now they are bringing many more nationalities, accents, data, linguistic information. They are hiring linguists now. So companies are competing really, really hard to bring the latest, the best advances in language processing. So right now there are some robots out there, like very famous robots that can have a conversation with you. But we also need to analyze the type of conversation. Like, how likely are these conversations to happen? So for example, if I go like, hey, Heather, how are you? And you say, hi, Jesse, I'm fine. Well, that's such a frequent conversation that everyone says in all the languages, it's not surprising that a robot can do it, right? But when we talk about other very skeptical, philosophical, difficult topics that are not very frequent, that's when the virtual assistant or the va. The robot will likely give you weird or somewhat not very accurate responses. This is machine learning. So it's a matter of how frequent something is and the sequences, a frequency of a topic in a language. And that's what the. The artificial intelligence just goes out there, brings what is out there, what people already do, and just brings it and provides all that knowledge to a computer. And the computer will just look for sequences and frequences and. And just reply what the most common answers are.

So the more exposure to natural language, the better, the better it becomes.

Yeah, and that's something that I really, really like because, well, it's so interesting to me that many people go like, oh, no, I want to learn English with a native speaker because I want to have their accent, because I want to Be able to understand them. So let me tell you that artificial intelligence, the biggest banks of data out there are produced in English. So because everyone out there in the world, if they want to say something, they most of the times translate it into English. So that is for everyone around the world. Right. So the biggest banks of data out there are in English. And then you go like, I want to learn and sound and speak this specific variety of English. And then artificial intelligence learned many years ago that that was not enough because you lose a lot of people. Because if computers are only able to understand native speakers and there are so many non native speakers of the language out there, that's a lot of market, that's a lot of people who vas cannot understand. So what did they do? Well, they exposed the computers to so many different accents out there so that the computers could become more effective to give an answer or to provide a service. So why is it that computers and companies are investing so much money to do that, but we limit ourselves to one specific variety or one specific thing? So learners out there.

Such a good point.

Yeah. So learners, people. If you want to be more trained than a computer to understand people, I suggest try to understand so many accents out there and try to. Yeah, that will make you more valuable for the market, for the job market in this case. Because if companies are spending so much money, that's. Because that's a trend. Because in the future we all will be one.

Right. I can definitely recognize a big difference in the voice to text features. Let's say 10 years ago when I was just. You started teaching and using it, it was so strict, which was, you know, if I tried to teach a proper sound, then there was no deviation. But now, which I love, you know, there's a lot more freedom and it doesn't recognize it word by word. So like it really understands the entire meaning of the sentence too, which is incredible to me. That's mind blowing because I was deliberately mispronouncing words in the context of the other word to see how it behaves. And it definitely detects my intention. So I can definitely see that it's much more open to sounds and mistakes and definitely accents. So I think it's great.

It's amazing. Yeah, yeah. Okay, now let me go on. You know, as a teacher that this is the way street. You need to train perception and you need to train production. Some people are very good at perceiving sounds, but they don't have to have it, so they don't produce the sound. Right. Some other people can Produce the sound, but they're not aware of the existence of the sound. So here is what Natural language processing can help you a little bit. So there are tools online. There are so many tools out there and this one is the one I want to talk about. Google's how do you pronounce? When I was working on my thesis and I was doing all this research, you hadn't issued the Google how do you pronounce tools by then, so you actually released it right after I defended my thesis. So it was like too late. Hadar.

Yeah.

Why didn't you tell me to wait?

Took a long time to start working with Google on this. So all the bureaucracy.

Yeah, but how do you pronounce is a great tool. I think that your, your point there was to work on perception. That's why you have the lips, that's why you have the sound. Right.

You mean to teach the computer perception? Yes.

Well, what, what I mean is that, yeah, what you did was to work with engineers so that, yeah, they could code the, the pronunciation in such a way that the computer could reflect it.

Yes.

So you also have work with artificial intelligence?

Oh, absolutely. Like big time. Because I had explain to them how I think. So for those of you who don't know, I was consulting Google when they produced this feature, the how to pronounce, where you type in how to pronounce and then a word, then you can, it shows you this phonetic transcription, but you can also record yourself and get feedback. It's not perfect, it doesn't always detect your voice properly, but generally what I was doing there, I had two hats there. One was basically to explain how I think and how I work and what feedback I would give. Right. So like all of that, I was literally teaching the computer how to listen. So I would listen to sounds and transcribe it, explaining what I'm hearing.

Right.

So all of that process, I think they were learning and of course, other things that I can't talk about and then in addition to that, like how to present it to people in a way that is clear. Right. Because that is also. That also needs to be intuitive and clear to understand what a person needs to focus on. If there are several things, for example, and what feedback they get.

Exactly. I remember that when Google released it, it didn't have the option for the person to record their mouse. It took it a while. And yeah, because I was going to use it with my students instead of the tools that I had, but it didn't have the option to produce. So it's okay when you train someone to perceive, but also when you link it to production. And then Google had this little microphone there where you could recorded and it came like, I think it was a year after or some months after that.

Three months after.

Yeah, it took it a while, but it was. I mean that's the perfect connection. And that's like when you close the cycle and that's the important thing. And another question that I had for you, since, since we are actually talking about how important it is for people to see what their voice is producing is, why didn't you? Well, I know that there are many opinions about the IPA out there, but I, I also couldn't use the tool because it used somewhat English orthography and you didn't use the ipa. And I wanna, I wanna know what the conversation was like.

Okay. I think they in particular were looking for something that is not ipa, so it's more accessible and that the spelling for people who use the Roman language, like the Alphabet, that familiar Alphabet, it's not as intuitive for all speakers. Right. Like it really depends of what your native language is. But to use a language, the English language also it has to do with what they can present, right? Because up until that point they had these features and characters that were very unintelligible. That means they had like double O, but this with little curvy line above it. And you know, people did not know how to use it. So they wanted to do something that is a bit more intuitive and clear for people to understand how to read it even without the ipa. Because ipa, everyone can go to the dictionary and find out, but you know, they wanted to do something that is more accessible because that's what Google is about, right? To make it accessible for everyone. Because to read IPA you need to know ipa and we're talking about a very small percentage of the people using this. And also I think the hardest thing here was really to represent. It was easy representing consonants, but the hardest thing was representing vowels, especially because they change. Because they change. Yeah. And then, you know, a lot of people see double O and they already associate it with their native language, not their native language or the common spelling in English. So double O is usually the lax cook look book. Right. But then when you use it for something else, so it could be very, very confusing. Which was a constant conversation that we had there.

Yeah, I, I knew that this was a conversation definitely. And if you're really nice, if you could add the IPA besides what you already did just for other people.

I agree. Yeah, I agree. I think Having both is great also, so that you can. Can compare it. But yeah, and I think also, like, they couldn't use the characters of the IPA to show as easily, you know, so because the characters of the ipa, that's like a different script and. Yeah, yeah, yeah.

Those are the technical issues that we don't want to go into. And also because those technical issues that actually make some virtual assistant or some app more or way easier to understand is what make them have a cost, an extra cost, right?

Yes, yes.

This is why I advocate for the use of technology that can be inclusive. But also, I'm not saying that technology can replace a teacher. So you understand how if you are a teacher, if you're an expert and you can decode what the computer is doing or predict how the computer will behave, you can use it as a tool and you can. Yeah. For example, I know that many people, many teachers, for example, they have an audio of their students, and the students produce a complete paragraph and they don't know where to start, right? They go like, oh, okay, we can start with vowels, consonants, and there's usually a pattern. And then automatic speech recognition can show you the patterns, or you can see in the paragraph the words that are most commonly misspelled. And then you can tell, okay, this is a highly frequent word, and the sound is a sound that actually makes a difference in meaning. So you can, as a teacher or as a pronunciation coach, you can prioritize and you can actually see how you're going to work first and what you're going to tackle later, etc. And in such a way that the computer or the student can dictate the same paragraph three months after and see a high improvement. And it will see it because you tackled the sounds that were the most problematic. And if the student can see what they dictate at the first month, and then after six months, three months of training, they see a difference, then it's because, I mean, it's not easy to go like, hey, yeah, I taught you how to pronounce things. And to my perspective, you sound like a native speaker or you sound great. It's not the same as if the student actually can see what a computer or AI can do for them. Now, I'm not saying also that artificial intelligence is like the greatest thing, because I know that it still has a long way to go and it still has some mistakes that it has to correct. But there are so many people working on that, me included. I'm working with virtual assistants that have to generate an answer for the user's utterances. So if the virtual assistant does not understand what the person wanted, but the person wanted to zoom in, then I'm going to tell the computer a. This pronunciation zoom in may not look like zoom in, but computer, even though you don't like it, the person wants you to zoom in. So if the person is saying jewelry. Ghibli. Ghibli. If that sounds like to you, maybe the person wants you to zoom in. So the computer will take that as another option. Right. And then it will increase its database in such a way that, yeah, maybe in 10 years computers will be able to understand us all in a better and more effective way.

Yeah. But then they become smarter than people in a way or less biased, let's say computers. Less biased. Yes. Which on one hand is, you know, an ideal world, and on the other hand it's just. It creates a false. False. Yeah, exactly. Representation of how things really are and how people are very, very rigid and how they hear, especially if they weren't exposed to a certain accent or dialect. Right. And I wish we. I wish we could turn on the button and teach people how to listen like we do with computers. That could be amazing.

Yeah, but that's another thing that Jonathan texted me and he was like, hey, can you please talk to Hadar and talk about testing like this toefl ielts and those standardized tests which are using automatic speech recognition and artificial intelligence to process the speaking part. And it's like, okay, it's a good thing because you take the bias out and we all have a filter. And even if you are a highly trained examiner and you have to give a grade to the speaking of a person, if you don't have like strict guidelines out there that take you out of the bias, and it's like, yeah, the first thing is not hearing the accent. Maybe. Maybe if you don't hear the accent and you only read what the computer was able to retrieve, then as Yuka dynamics. I mean, you didn't tell me the nationality of the people, but you can tell what the L1 is and you can tell what the interference is. Maybe you can speculate, but you don't have a bias. You didn't see color of skin, you didn't see eyes. So I think this is what we can rescue from the current stage of artificial intelligence. Did I convince you?

So beautiful. Absolutely.

Yeah.

I mean, with that last sentence, of course.

Yeah, I know, I know. So I've been using Pratt for linguistic analysis and speech analysis, and I have also a tutorial, a free Tutorial on my website, how to use Pratt. But teachers, for example, if you want to visualize what students are saying, and maybe you find it hard to perceive because we are all biased by our own perception skills. So when I started to use artificial intelligence, my claim was that, okay, if science, because, yeah, I found so many quotes out there in literature, scientific research saying, yeah, maybe non native teachers are not a good model. I was like, okay, if I cannot be a good model, and then everything my students say will be. Will have to be filtered by my own bias, then how am I going to be objective? How am I going to be able to help? But then when I have automatic speech recognition and AI and computers to my side, they help me take a little bit of that bias out if I use them in such a way that they help me do my work or do my job so that students don't feel that, hey, the teacher just doesn't want to help me because she says this sound is not important. Like, for example, when I was doing the research, I was working with vowels. This is something important. Research and literature says that we work with minimal pairs. So, for example, in Spanish, the kid vowel and the fleece vowel are a problem, right? But then students, instead, the kid vowel, if you tell them, hey, it's different from the fleece vowel, then they will go all the way to the dress vowel. And then you have to teach the dress vowel. So literature won't often tell you they. They go minimal pairs. But you know that sometimes we have to bring an extra ally so that students can create a new category or just distinguish, understand?

Because it's like a game where you have holes in a ball and you have to fit it in a hole. So if it doesn't go here and it doesn't go here, they'll find a way. Like a hole to put it.

Yeah, right.

Yeah, they'll automatically go to a different sound. But what you're saying is the ultimate solution to create a new category, like, or at least know that it exists.

Yeah, exactly. Go like, hey, it's not this one, but it's not this one either. So you as the expert, you provide the student with enough samples so that they know. But it was also difficult for me because my perception is already biased by my L1. So I was like, okay, let me look at that. And then when Pratt showed me the vowel chart and how close the kid vowel was to the dress vowel or to the Spanish, A was like, oh, shoot. Like, even, you know, hey, they are super close.

They're even closer. Than to the high e. So there was a research done. Israeli speakers also have five vowels, just like in Spanish, and learners heard the eh sound in isolation, and they were asked, which one is it closer to the e, the Israeli e or e? And like 98 said e. However, in pronunciation, everyone pronounces it as an e because spelling plays a significant component here. So if you listen to it in isolation, they analyze it as e, but when they read it, they'll say, situation, kid, lift. You know, so. So definitely, like, all these elements play a significant role in determining how they place it in what category.

Yeah. And when Pratt can tell you what to see or why is it that they are so hard even for you to distinguish, then you go like, oh, you see the Bible chart and you see how close they are. They were like, this is why. Yeah, now I get it there.

Exactly close.

So, yeah. And sometimes you as a teacher, as a pronunciat coach, you have the feeling. And if you are a native speaker of one language, but you are not aware of what's going on in all of other languages, you go like, hey, why did the student say jewelry instead of saying zoom in? And then you go, oh, how can I help this person?

Absolutely.

See what is there. And then you start tackling in order. You get your priority according to your own process and how you decide to tackle it. Right.

So, Yazzie, how can people get access to all those resources?

Well, yeah, YouTube, Facebook. I have a website that is, well, a page that is Jazzy Addis. And in Instagram, Jazzy Addis. You know, I'm not very creative with marketing names, but my website.

Your name is your brand. Yeah. Yeah. Jazzy is also an ETA member in English Teachers Academy, our community of teachers and membership. So we've known each other for all aspects, not just the teaching aspect. Yes.

Yeah. And my website, you're going to see some English courses, but I also have Pratt Elan. And I am planning to update the Pratt tutorial that is for free over there. I don't charge it because I know how hard it is to work with Pratt if you are so hard, if you're not familiar with it. But I also. Sometimes I open courses when I have like many people who have actual data and they don't know how to process it and they want specific things. Then I open a course and then we go over each ones and stuff.

Amazing. So teachers, pronunciation teachers, if you want to learn how to use those tools, then definitely contact Jesse and and go to her website and find out more about what she offers. This was an incredible conversation. I was so happy. I. I could have. You could have continued for another hour or so.

I think I have so many other things that people ask me for, but we will have to have another life.

We. We will have a follow up for sure.

Yeah.

Amazing. So thank you so, so much. And. And that last thing you want to share with people.

Well, first, thank you, Hadar, because these questions that I asked you and everything. I had wanted to have this conversation way before this. Thank you so much for having me. And yeah, people out there, just the thing that I get from the current state of AI is first, being classive and try to learn and expose your ear to as many accents as possible, because that's the trend. And another thing is that pronunciation coaches, you can visualize data. You don't have to always rely on your intuition and be afraid if you're doing it right or wrong. Just you can visualize data and, and tackle it more effectively or quicker. Or maybe find and introduce the use of automatic speech recognition and AI in your process so that the students can also see. I know that some pronunciation coaches use Pratt and then they produce a vowel chart and then they show the students, hey, you're pronouncing it in here. You have to open your mouth. They use the vowel chart and personalize vowel chart. And sometimes it helps, sometimes it doesn't. It depends on the speaker. It depends on the specific necessities of the client. But you guys are experts. So pronunciation coaches, you know what that person needs. And, and if you can get tools to visualize and help them see their process, you will sound more reliable. Or maybe you will take the intuition and subjective part out and your bias.

Yeah.

And try to be more objective of what you're teaching.

Yeah.

And. And confidence is everything. You know, we talk about it in language learning. Definitely in teaching, when you have that something else to rely on, I think that really helps you build your confidence. And you know, it's a confirmation for whether or not you're on the right track. So definitely use that.

Yeah, yeah. Because there are so many people out there. I was listening to a podcast and, and these people, they are experts in tefl. And the presenter, she says that she receives lots of hate comments because people go like, hey, I don't even understand your accent. So she might feel afraid and she might feel that her accent is not good enough, but when other hundreds of thousands of people understand them because she's in a podcast. Right. But if you're not in a podcast and you want to know if your speech could be understood. And you don't want to feel afraid of judgment. Just dictate to Google something and then you will see. Like if the computer is able to understand you, then you shouldn't feel bad and then it's not your problem. The problem is the listener who hasn't exposed him or herself to enough accents and they are not efficiently trained for the new trend coming. And maybe computers will take their jobs if they are not willing to. They don't step up other teachers or accept other accents. Yeah, because what if, what if like.

Global companies stop training their employees and like pronunciation or accent training or let's call it what they actually call it, accent reduction. Yeah, right. Lose your accent. Okay. What should I speak with an accent as an accent and instead train people to listen better?

Right?

Yeah. The enterprise says we only hire native teachers. You have to be aware that that will be a problem in the future. If you're only used to native speakers and there are so many non native speakers out there and the computers are able to understand them better than you, then you will not be ready to be effective in the job market. So be aware. Just take that into account. Companies are spending so many millions and billions of dollars so that computers can understand everyone. Then you should better try to understand everyone as well.

That's it. That's it. I'm not gonna add anything and I'm just going through it. Yes. Okay. Jesse, thank you so, so much. It was such a pleasure. Thank you for all your wisdom and insights and I. I will see you soon. Thank you so much, everyone. Thank you so much. And I'll see you next week.

Bye, Sam.