跟讀練習: this is not financial advice... it's AI - 通過影片學習英語口語
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130 句
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Back in the day, Wall Street was run by the people.
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You know the type: suits, nice watches, and egos the size of small countries.
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It was the Gordon Gekko and Jordan Belford archetypes.
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Because for most of modern financial history, investing with money managers and financial advisors was pretty straightforward.
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You hired a person, that person read through filings, listened to earnings calls, analyzed trading multiples, and then ignored all of it because he had a hunch about a biotech startup.
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It was a truly poetic system, because sometimes they were right.
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More often, they were wrong, but at least it felt human.
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And that's because there was always a story involved.
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Whether that story was the management looks strong, the balance sheet is stock feels cheap, which, if we're being honest,
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is just astrology for men who wear cufflinks.
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And for decades, crafting stories was the norm.
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Markets were inefficient, data moved slowly, and information advantages actually existed.
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It was the peak of capitalism and financial markets.
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But then, something happened that changed everything.
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The math nerd showed up, Guys named Yang, with PhDs from Harvard, three math Olympiad medals, and dreams that exclusively consist of probability distributions.
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And in the late 1980s, these nerds banded together because they realized something: markets weren't entirely random.
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They were statistical.
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Patterns exist, human behavior repeats, and market inefficiencies open the door for opportunities.
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And this is where quantitative investing began.
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Because instead of asking, "Is this a good company?" the quants were asking, "Given these variables, what's the expected outcome?" It was rules over narratives,
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probabilities over opinions, math over gut feelings.
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And it worked.
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Quantitative hedge funds began popping up out of nowhere like acne on a teenager, and the most infamous of which was Renaissance Technologies, and more specifically, the Medallion Fund.
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Renaissance Technologies' flagship hedge fund, and it was founded by mathematician Jim Simons.
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He believed in using purely mathematical and statistical models to predict market movements.
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Meaning no fundamental stock picking, no CNBC interviews, and no magic genie hiding inside a Bloomberg terminal.
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It was just purely math, algorithms, and machine learning.
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And it worked.
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Obscenely well.
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Because for more than two decades, the Medallion Fund generated over 60 % annualized returns before fees, and roughly 39 % after fees.
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And for context, Warren Buffett averaged around a 19 % annualized return over his career.
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you'd end up with around $324 ,000.
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But if you invested that same $10k with the Medallion Fund's returns, that $10k would have grown to a little over $7 million.
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So yeah, you could say the Medallion Fund was pretty successful.
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and it transformed the industry.
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Because instead of humans making all the judgment calls, now people leveraged systems that dynamically reacted to things like price movements,
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order flow, volatility, and countless other variables, and these algorithms operated at speeds that no human could ever compete with.
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And while Quant Funds were quietly becoming one of the favorite investment vehicles for institutions and high net worth individuals, there was something else unraveling with the everyday investors.
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do have the ability to outperform through discretionary stock picking.
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These people are very few and far between, and by the early 2000s, the data proving this was starting to pile up.
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Because it turns out that these active fund managers actually kinda stink at picking stocks.
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And not just occasionally, and not just during bad years, but consistently.
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And study after study showed that roughly 90 % of active equity fund managers underperform managing your money,
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it's even worse.
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So investors began asking questions like, "Why am I paying extra to underperform?" And this is where index investing enters the story.
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It's low -cost, passive, and designed to replicate the entire market's performance.
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And it was gaining popularity among the average everyday investors.
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Instead of searching for the needle in the haystack, investors simply began buying the whole haystack.
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So while retail investors were moving away from money managers and financial advisors, and towards index funds and ETFs, institutions were doing something similar.
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They were reallocating capital from traditional active fund managers toward other alternatives like quant firms, private markets, and in -house teams.
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Either way, we began seeing the traditional stock -picking money manager role slowly becoming obsolete.
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Capital was flowing out of the industry, fees were compressing, and careers evaporated.
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But even though this was a dying industry, there was still a demand for portfolio management and the benefits it includes.
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Things like rebalancing, asset allocation, and tax optimization.
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The boring stuff.
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And that's where we are today, and where robo -advisors come into the story.
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But before we go any further, let's clarify something first.
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If you hear that robo -advisors use algorithms to manage portfolios, you'd be right.
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But this is not in the same way that quantitative hedge funds use algorithms.
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Because quantitative hedge funds, like Renaissance Technologies, or Two Sigma, use algorithms to exploit micro -sigmas microsecond market inefficiencies,
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statistical arbitrages, and other active trading strategies, all in the pursuit of generating alpha.
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Comparing how quant firms use algorithms to how robo -advisors use algorithms is like comparing a Michelin star chef to Chef Boyardee,
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because robo -advisors are essentially just automated investment platforms designed to deliver low -cost market returns with minimal human involvement.
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They're not trying to compete with quant firms, they're involved with having a money manager.
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Robo -advisors handle the asset allocation, rebalancing, tax optimization, and the rest of the portfolio busywork.
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It's all the boring stuff your financial advisor would charge you 1 % a year to do in Excel.
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And investors love this value proposition.
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Platforms like Wealthfront, Betterment, and other robo -advisors began exploding in popularity.
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Because 10 years ago, Wealthfront had just $2 billion in assets under management.
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And if you fast forward to today, well, that number has grown 40 -fold to $80 billion in the world in total client assets.
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And it's not just Wealthfront.
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Globally, RoboAdvisors managed around $187 billion in 2017.
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And by 2023, that number had grown to nearly $2 .8 trillion.
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And by 2027, it's estimated to reach $4 .7 trillion.
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This isn't just a niche trend in finance.
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It's a structural shift in a long, stagnant industry.
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But it's not all sunshine and rainbows either, because robo -advisors have some blatant issues.
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But before I jump into that, a quick pause.
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Because this video is sponsored by Surfshark.
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Surfshark is a VPN service, and the reason I like it is pretty simple.
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It's built for people like me who don't want to constantly have to manage their privacy online.
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With Surfshark, you just turn it on, and your internet traffic is encrypted, your IP is hidden, and your data stays private.
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And the best part?
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You don't need to count how many devices you have, because with Surfshark, you can set up one account and use it on unlimited devices.
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It also comes with features like the Email Scam Checker, which helps detect phishing attempts and protect your personal information.
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And if your data does ever show up in a breach, Surfshark Alert notifies you right away.
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And that's why Surfshark is more than just your standard VPN.
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It's an all -in -one solution for online privacy.
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Because whether you're working from home or traveling abroad, Surfshark helps keep your information secure wherever you are.
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And they're so confident in their service, they offer a 30 -day money -back guarantee.
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If you want to check it out, you can go to surfshark .com /casualfinance, or just use code casualfinance at checkout, and get four extra months of Surfshark VPN.
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Thank you to Surfshark for sponsoring this video.
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And now back to robo -advisors and the issues that nobody seems to be talking about.
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Because on the surface, robo -advisors sell personal personalized investment solutions.
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But while they may feel like a sophisticated, tailor -made solution for your investment strategy, the truth is, they're nowhere near as personalized as they appear.
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Because they start by asking you to complete a questionnaire.
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It covers things like your age, income, time horizon, goals, and a few other questions.
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It feels intentional, thoughtful, and crafted specifically for you.
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But in reality, it's not.
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Because after you answer the questionnaire, instead of being crafted an individual investment plan for yourself,
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you're actually categorized into a bucket and slotted into a model portfolio that's shared by thousands of other people with similar answers.
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And that is where a lot of people misunderstand robo -advisors.
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They aren't some hyper -intelligent AI crafting a unique portfolio specifically for you.
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They were designed to replace the operational role of a traditional financial advisor.
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All the stuff that used to require meetings, paperwork, and a guy named Gary to explain beta with a laser pointer is no more.
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Because for decades, financial advisors weren't really paid for generating alpha or outperforming in the markets.
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They were paid for access.
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Access to markets, access to investment products, and access to information.
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And for a long time, that made sense.
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Markets were opaque, data was expensive, and investing without guidance felt like trying to assemble IKEA furniture without instructions.
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But then the internet showed up.
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Information asymmetry disappeared, market data became free, and research tools went mainstream.
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And suddenly financial advisors weren't guarding the gates anymore, and paying someone 1 % a year started to feel questionable.
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And that's the real draw of robo -advisors.
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And travel agents didn't disappear because people stopped traveling.
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They disappeared because booking a flight stopped requiring a phone call.
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Stockbrokers didn't disappear because people stopped trading.
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They disappeared because clicking a "Buy" or "Sell" button online was faster than calling some guy named "Rick" on the trading floor.
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technology.
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And that's exactly what's happening with robo -advisors.
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They're automating the middleman layer of the investment management world.
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Which is why this isn't a story about robo -advisors being good or bad.
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It's a story about what happens when technology finally reaches a stagnant industry.
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So now I'm curious to hear where you land on things.
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Have you ever used a robo -advisor?
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Would you trust one with your money?
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Drop it in the comments below and let's see how the investment landscape is changing.
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Because whether it's quant firms replacing discretionary managers or robo -advisors replacing traditional financial advisors, the direction is the same.
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When technology touches a stagnant industry, it doesn't make small tweaks.
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It forces fundamental changes.
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本課的詞彙與口語要點
這節 B2 級別的口語課以影片“this is not financial advice... it's AI”為素材。 影片中反覆出現的詞有：advisor, robo, fund, surfshark, financial。 這段影片共有 130 個句子、1722 個單詞可供跟讀。 講話部分時長為 10:28。 說話人語速自然，每分鐘約 164 個詞，接近日常對話。 只有 74% 的單詞屬於英語最常用的 3,000 詞，詞彙難度較高。
影片中的重點詞彙
影片中最難的 15 個單詞，附發音和釋義：
| 單詞 | 發音 | 釋義 |
|---|---|---|
| advisor 名詞 | /ædˈvaɪ.zə(ɹ)/ | 顧問 /顧問 |
| algorithm 名詞 | /ˈælɡəɹɪðm̩/ | 演算法, 演算法 |
| investor 名詞 | /ɪnˈvɛs.tə(ɹ)/ | 投資者 /投資者, 投資家 /投資家 |
| quantity 名詞 | /ˈkwɑn(t)ɪti/ | 數量 /數量, 量 |
| disappear 動詞 | /dɪsəˈpɪɹ/ | 消失 |
| quantitative 形容詞 | /ˈkwɑn.təˌteɪ.tɪv/ | 量的, 數量的 /數量的 |
| hedge 名詞 | /ˈhɛd͡ʒ/ | 籬牆 /籬牆, 樹籬 /樹籬 |
| mathematics 名詞 | /mæθ(.ə)ˈmæt.ɪks/ | 數學 /數學, 算學 /算學 |
| stagnant 形容詞 | /ˈstæɡnənt/ | 停滯的 /停滯的 |
| automate 動詞 | /ˈɔ.təˌmeɪt/ | 自動化 /自動化 |
| haystack 名詞 | /ˈheɪˌstæk/ | 草垛 |
| personalize 動詞 | /ˈpɜː.sə.nə.laɪz/ | 個性化 /個性化 |
| questionnaire 名詞 | /ˌk(w)ɛstʃəˈnɛə/ | 問卷 /問卷, 調查表 /調查表 |
| underperform 動詞 | 表現不佳 /表現不佳 | |
| optimization 名詞 | /ˌɑptəmaɪˈzeɪʃən/ | 最佳化 /最佳化 |
影片中出現的短語動詞
| 單詞 | 釋義 |
|---|---|
| show up 動詞 | 露面 |
| turn out 動詞 | 原來 /原來 |
需要注意的發音
說話人用了 23 次縮略和弱讀形式，例如 they're, weren't, didn't。請按聽到的簡短形式來說。
- “th” 音: algorithm /ˈælɡəɹɪðm̩/, mathematics /mæθ(.ə)ˈmæt.ɪks/, mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/, thoughtful /ˈθɔːtfəl/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/
- “sh” 和 “zh” 音: discretionary /dɪsˈkɹɛʃəˌnɛɹi/, optimization /ˌɑptəmaɪˈzeɪʃən/, allocation /ˌæl.əˈkeɪ.ʃən/, arbitrage /ˈɑɹbɪˌtɹɑʒ/, inefficient /ˌɪn.ɪˈfɪʃ.ənt/
- 長單詞——注意重音位置: quantitative /ˈkwɑn.təˌteɪ.tɪv/, portfolio /pɔːtˈfəʊ.liˌəʊ/, mathematics /mæθ(.ə)ˈmæt.ɪks/, statistical /stəˈtɪs.tɪ.kəl/, discretionary /dɪsˈkɹɛʃəˌnɛɹi/
中文母語者容易讀錯的音：
- /v/ — 不要讀成 /w/，上齒輕觸下唇: advisor /ædˈvaɪ.zə(ɹ)/, investor /ɪnˈvɛs.tə(ɹ)/, quantitative /ˈkwɑn.təˌteɪ.tɪv/, variable [ˈvæɹ.i.ə.bl̩], evaporate /ɪˈvæp.ə.ɹeɪt/
- 詞尾子音 — 要讀清楚，後面不要加母音: quantitative /ˈkwɑn.təˌteɪ.tɪv/, hedge /ˈhɛd͡ʒ/, mathematics /mæθ(.ə)ˈmæt.ɪks/, stagnant /ˈstæɡnənt/, automate /ˈɔ.təˌmeɪt/
- /r/ 和 /l/ 的區別: algorithm /ˈælɡəɹɪðm̩/, purely /ˈpjʊɹli/, variable [ˈvæɹ.i.ə.bl̩], astrology /əˈstɹɒlədʒi/, replicate /ˈɹɛplɪkeɪt/
如何用這段影片練習
- 先完整聽一遍影片，不要開口，記下不認識的單詞。
- 先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
- 錄下自己的聲音並與原聲對比，特別注意 advisor, algorithm, investor 這類單詞。
什麼是跟讀法？
跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。