Hello everyone. Welcome to Big Data and Language course. Let's finish the other features of spoken data. Do you remember what the eight features of spoken data that I've talked about in the previous lectures? I hope you understand and remember all of the features. If not, then feel free to go back and review the previous lectures. Are you ready? Let's get started. Let's move to the next spoken feature which is monophthong. The vowel sound that is pure in the beginning and the end of the vowels are more and less the same. So for example this is by the contrast of diphthong which glides from one position to another. So for example the word goat. That one is a pretty long vowel, O-A, goat. However when you speak something your conversation you just say very shortly. You don't have to or you don't need to or you might not want to pronounce goat really like with a long vowel sound. Instead, you might want to say just goat. Yeah. Very short and maybe sometimes like short vowel. Okay. So before we move to the next feature, why do we want to use monophthong? Because you might want to say a lot of words and a lot of information like in the speedy conversation, that's why you might want to use monophthong in order to save your energy or you want to move to the next word and next information. Okay. Now, let's move to another spoken features. Another spoken feature is you might want to find a lot of non-standard grammar in the conversation. Even if English is not your first language, think about your native language in the conversation. Even when you speak your native language, you might use it unintentionally. You might use a lot of ungrammatical and non-standard grammar. That's not because you do not know about your native language because this one is one of the feature of spoken data and spoken language. So for example, let me give you, if you say, "I done it," instead of, "I have done it." You just say I done it because not really you don't know the function of have plus PP. No, because you just want to move on and you're more focused on the meaning instead of the grammar. However, when you write something, you might more focus on grammar. Let's move to the next feature. This one is non-fluency features. Then what are the examples of non-fluency features. Non-fluency features such as silent pauses, you just think about something the next, but you might also want to use filled pauses. If you are silent, do not speak. Then maybe the listener misunderstand that you want to stop the conversation. However, if you are thinking but you want to say something you need a time. Then you might want to say, "Um, uh." So those kind of filled pauses, and also you are thinking something, you repeat something several times. Also, sometimes you have the false starts which means like there is no subject and you just start the verb only which is rarely occur and happen in the writing. So let me give you an example. This a conversation between two people and Barry said, "Sorry about this. So what have you been up to?" Then Rachel said, "Oh, not much. I- I got a job." So Rachel said I twice. This one is a repetition. Barry said, "Oh, that's great." Rachel said, "Why are- why are you so tanned?" Yeah. So why are again, repetition. Barry said, "Oh, I, uh- I went to Aruba." Yeah. So, uh, that one is the filled pauses. These are all the filled pauses or repetitions. They are very common in conversation. Okay. Now let's move to slang. Slang is the last features of spoken data in this lecture. There will be more spoken features. Don't get me wrong. However, I just want to point it out the major 12 features but maybe in the future I'll will talk about more. So these 12 features I would say, the major features of spoken data. So the slang, I don't want to say any slang in this lecture but you might notice that like F words. So those kind of things you probably hear a lot in the action movie or Hollywood movie. So this one is another feature of spoken data. Why do we use slang? Maybe creating the solidarity or you might want to emphasize your feeling. So the slang is another feature of spoken data. Okay. How was all the spoken data features? You already probably notice that or knew before or you just realize some certain features during this lecture. So now let me give you time to do your own task. Let me explain one by one. The first one as you see in the handout on the blackboard, you will see that you need to write down your name and also you need to write down the source types such as it could be drama, interviews. So any kind of source type of spoken data. You can choose any source. Then also it's better to include the source information such as title, URL so other people can see or watch and understand what you found as a spoken data. Then also you need to include the excerpts, especially which part you find the which feature. Finally, the fifth one you need to write down the features of spoken language and also elaborate the functions, why that spoken features are used because of which intention. I will share one of the example about this task so you may have the better understanding. Let me share one example of the task. The source type is interview and the source information, the title was English people try Korean chicken and beer. So in Korea, chicken and beer is a pretty popular food and it's a great combination that Koreans enjoy. So interviewer actually tried to let the interviewer in the video ask English people to try this chicken and beer. You will see all the different reactions. One of the spoken feature that we can find is discourse marker. Then the time, like 23rd seconds from in this video, the excerpts that, "Oh weird." So what's the function of oh weird? Oh, so this one is the first at the beginning of the interviewee's reaction. This one, oh, showing that the interviewee actually starts their conversation, their reaction. The second feature is hedge. So at the 29th second, you will see that basically you just want to get a bit of greens and a bit of what's underneath. So a bit. A bit means hedge that we've learned in the first video of the first lecture this week and a bit actually modifies the greens. So this one hedge showing that English people were not sure what that green exactly. Yeah. So it seems like, but it was a green. So a bit green and then a bit of what's underneath. So they want to tone down because they do not know about what that the green vegetable exactly. So something like that. Feel free to find any video clips and any spoken data and analyze watching it and find any five features of spoken data. Also, once you list it, think about why these features are used in this certain spoken data. All right. Good luck with your task and thank you for your attention.
