ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: Why Can’t We Cure Cancer Yet?
เลือกโหมดฝึก
แต่ละโหมดจะเปิดห้องฝึกแบบเต็มจอ
243 ประโยค
หากประโยคสั้นหรือยาวเกินไป กดที่ Edit เพื่อปรับแก้
1
We declared war on cancer in 1971.
2
Not metaphorically, literally.
3
The United States National Cancer Act was signed into law by
4
Richard Nixon with the explicit intention of doing to cancer what the space program had done to the moon landing problem.
5
Throw enough money, enough scientists, and enough national will at it until it surrendered.
6
The logic seemed sound.
7
We had just put humans on the moon.
8
Surely a disease was easier than that.
9
It has now been more than 50 years.
10
Cancer kills approximately 10 million people per year globally.
11
It is the second leading cause of death worldwide.
12
And while the treatments have improved,
13
meaningfully, genuinely improved, the word cure still doesn't appear in most conversations about most cancers without significant qualification attached to it.
14
So what happened?
15
Was the science wrong?
16
Were the researchers incompetent?
17
Did we simply not spend enough money?
18
Because the global cancer treatment industry is now worth hundreds of billions of dollars annually so it isn't obviously a resource problem?
19
Or is cancer something fundamentally different from the kind of problem that throwing resources at actually solves?
20
The answer is the last one.
21
And understanding why requires understanding what cancer actually is, which turns out to be one of the most important things most people have never been told clearly.
22
Here is the thing that changes everything when you really absorb it.
23
Cancer is your own cells.
24
Your own DNA.
25
A. Your own biology, running a program that your body itself wrote, using machinery that your body built, following rules that evolution installed.
26
It is not a pathogen, not a bacterium, not a virus that has entered your body from outside and is attacking your cells from a foreign position.
27
This is the fundamental reason why curing cancer is categorically harder than curing an infection.
28
When a bacterium enters your body, it is a foreign object with foreign proteins that your immune system can learn to recognize and attack.
29
The boundary between self and not-self is clear.
30
The target is identifiable.
31
The strategy, kill the foreign thing without killing the host, is at least conceptually straightforward.
32
Cancer collapses that boundary.
33
The target is you.
34
Specifically, it is a version of you that has stopped following the rules that keep the rest of you functional.
35
Killing cancer without killing the host is one of the hardest targeting problems in the history of medicine, because the thing you're trying to destroy is made of the same materials,
36
running on the same biochemistry, and protected by the same structures as everything you're trying to preserve.
37
This is why chemotherapy, the dominant cancer treatment for decades, works the way it does, and fails the way it does.
38
Chemotherapy drugs target rapidly dividing cells, which cancer cells are.
39
But rapidly dividing cells also include hair follicles, gut lining, bone marrow, and immune system cells.
40
The drug doesn't know the difference.
41
It targets the behavior, rapid division, and the identity problem is what makes cancer so persistently hard to solve.
42
Let's go deeper into the biology because this is where the real complexity lives and where most popular explanations stop short.
43
Your body contains roughly 37 trillion cells.
44
Every one of them carries a complete copy of your genome, about 3 billion base pairs of DNA.
45
Every time a cell divides, it has to copy all three billion of those base pairs.
46
And copying is imperfect.
47
Every cell division introduces somewhere between one and ten random mutations.
48
Your body performs trillions of cell divisions over your lifetime.
49
The math is not reassuring, but your body has defenses, multiple layers of them, tumor suppressor genes that apply breaks to the cell division process,
50
proofreading mechanisms that catch and correct copying errors,
51
apoptosis, programmed cell death, that kills cells that are too damaged to repair safely,
52
and the immune system, which patrols continuously for cells behaving abnormally and eliminates them before they can proliferate.
53
Cancer happens when enough of these defenses fail simultaneously.
54
A typical cancer cell carries hundreds of mutations, accumulated over years or decades, each one slightly loosening the constraints on cell behavior,
55
until a cell emerges that grows without limit, resists death signals, recruits blood vessels to feed itself, and eventually spreads to other tissues.
56
This is cancer as an evolutionary process.
57
And this framing, cancer as evolution happening inside your body, is the most important conceptual shift in cancer biology of the last 20 years.
58
Cancer cells compete with each other.
59
The mutations that help one cell grow faster than its neighbors get selected for, the same way beneficial mutations get selected for in populations of organisms.
60
The tumor is an ecosystem of competing cell populations,
61
each slightly different, each under selection pressure to outgrow the others and resist whatever the treatment is trying to do to them.
62
This is why cancer develops resistance to treatment.
63
When you hit a tumor with a drug, you kill the cells most vulnerable to that drug.
64
But some cells, by random mutation, happen to be slightly less vulnerable.
65
Those cells survive.
66
They reproduce.
67
The tumor that grows back is descended from the survivors, the ones that already had some resistance.
68
You have, through treatment, accidentally run a selection process for drug resistant cancer.
69
Evolution at work, inside a patient, during therapy.
70
This consequence of the underlying biology cannot be fully escaped without solving the evolutionary dynamics of the tumor.
71
Which is a much harder problem than simply finding a drug that kills cancer cells.
72
Now let's talk about why the phrase curing cancer is itself part of the problem.
73
Cancer is over 200 distinct diseases unified by the characteristic of uncontrolled cell growth but differing in their cell type of origin,
74
their genetic mutations, their growth patterns, their metastatic behavior, their response to treatment, and their prognosis.
75
Lung cancer and leukemia and breast cancer and glioblastoma and melanoma are different problems that share a broad category.
76
Treating them as variations on a single disease is like treating pneumonia
77
and tuberculosis and asthma as the same problem because they all affect the lungs.
78
Even within a single named cancer type, the variation is enormous.
79
Two patients with stage 3 lung cancer may have tumors with completely different genetic profiles,
80
different immune microenvironments, different metabolic characteristics, and different responses to the same treatment.
81
What works for one may fail completely for the other, because the specific cancer being treated is biologically different at the molecular level.
82
This is why clinical trials are so difficult, and why results that look promising in the lab so frequently fail to translate to patients.
83
The patient population is not uniform.
84
The disease is not uniform.
85
The drug
86
that works beautifully on a cell line in a dish may work on 30% of patients with a particular cancer type
87
and do nothing for the other 70%.
88
And the difference between those groups may not be obvious until after the trial.
89
Personalized medicine, tailoring treatment to the specific genetic profile of each patient's specific tumor, is the direction the field is moving,
90
and it has produced real results in specific cancer types.
91
But sequencing a tumor's genome, and identifying the actionable mutations, and designing a treatment that targets those specific mutations in that specific patient is expensive,
92
slow, and complicated by the fact that the tumor evolves during treatment, changing its genetic profile as it responds to selective pressure.
93
The target moves in real time.
94
Let's talk about the immune system, because this is where the most exciting recent progress has happened and where the complexity of the problem is most visible.
95
The immune system should be killing cancer.
96
It should be the body's primary defense against tumor development, detecting abnormal cells and eliminating them before they become established tumors.
97
And in fact, it does this continuously.
98
Most cancer cells are killed by the immune system before they ever become a tumor, you'd notice.
99
The cancers that become clinical problems are, by definition, the ones that have evaded immune detection.
100
Cancer cells evade it through a variety of mechanisms, many of which involve actively suppressing immune function in their local environment.
101
They express surface proteins that signal to immune cells not to attack.
102
They recruit immune cells into the tumor and reprogram them to suppress anti-tumor activity.
103
They create a local immunosuppressive environment that disables the very cells that should be destroying them.
104
Immunotherapy, the approach of reactivating the E, immune systems attack on cancer has produced some of the most dramatic results in cancer treatment in recent history.
105
Checkpoint inhibitors, which remove the molecular breaks that cancer cells apply to immune function have produced long-term remissions in patients with melanoma,
106
lung cancer, and other previously lethal cancers who had no other options.
107
Patients who would have died in months are alive years later, but checkpoint inhibitors work in approximately 20 to 40 percent of patients with responsive cancer types
108
and often don't work at all in others.
109
The tumors that don't respond have found other ways to suppress immune function that the checkpoint inhibitors don't address.
110
Some patients develop severe autoimmune side effects when the immune breaks are removed, because those breaks exist for a reason,
111
which is to prevent the immune system from attacking normal tissue.
112
The immune system is an extraordinarily complex system with multiple competing functions, evolved to balance aggression against foreign threats with tolerance of self-tissue.
113
Cancer exploits the tolerance mechanisms to hide.
114
that tolerance to attack cancer also risks attacking everything else.
115
The trade-off has no clean solution.
116
By the way, if you want to go even deeper into The Secret Lives of Ancient Humans, I just launched a full course called The Secret Lives of Ancient Humans.
117
Ten modules, three plus hours of content covering everything we never talk about on YouTube.
118
What they feared, how they they communicated, what hunted them, and much more, plus an exclusive PDF guide included.
119
Link in the description.
120
See you inside.
121
Now let's talk about metastasis, because this is where cancer becomes lethal and where treatment becomes most difficult.
122
The primary tumor, the original site of cancer, is rarely what kills.
123
Surgery can remove it.
124
Radiation can target it.
125
The problem is when cancer cells break away from the primary tumor, enter the bloodstream or lymphatic system,
126
travel to distant organs, and establish secondary tumors.
127
By the time metastasis is detected, it has usually already happened in multiple sites.
128
A patient whose cancer has spread to the liver, lungs, and bone simultaneously has three separate tumors to treat, each potentially with a different genetic profile,
129
each in a different tissue environment, each potentially developing resistance independently.
130
Cancer cells completing metastasis have to accomplish an extraordinary series of steps.
131
They detach from the primary tumor, survive in the hostile environment of the bloodstream, evade immune attack while circulating,
132
arrive at a distant organ, penetrate its tissue, survive in an entirely different microenvironment, and then reactivate growth.
133
Each step is a potential point of intervention.
134
None of them is easy to block without disrupting normal biological processes that use similar mechanisms.
135
And then there is dormancy.
136
Cancer cells can enter a metabolically quiet state, not dividing, invisible to treatments that target dividing cells and nearly invisible to imaging,
137
and remain dormant in distant tissues for years or decades before reactivating.
138
A patient declared cancer-free after treatment may harbor dormant cancer cells that reactivate years later under conditions not fully understood.
139
This is why oncologists use five-year survival rates, rather than the word cured.
140
The dormancy problem means that being cancer-free today does not guarantee being cancer-free in 10 years.
141
Let's talk about the gap between laboratory results and patient outcomes, because this is one of the most frustrating recurring patterns in cancer research,
142
and one that the public rarely sees clearly.
143
A drug works in a mouse.
144
Researchers publish the results.
145
Headlines Americans announce a potential breakthrough.
146
The drug enters human trials.
147
It fails.
148
This cycle has repeated hundreds of times across cancer research history, and its repetition is not evidence of incompetence or fraud.
149
It is evidence of a genuine biological gap between the experimental models used to test drugs and the actual human disease.
150
Mouse tumors are genetically uniform, implanted in immunocompromised animals in controlled laboratory conditions.
151
Human tumors are genetically heterogeneous, arising in immunologically complex individuals with distinct genetic backgrounds,
152
varied exposures, and decades of accumulated biological history.
153
A drug
154
that clears a tumor in a mouse is operating on a
155
fundamentally simpler biological system than the one it will encounter in a human patient.
156
The simplification that makes laboratory research tractable also makes it systematically misleading about what will happen in a real clinical setting.
157
This problem is known.
158
Researchers work to improve their models, better mouse models with more intact immune systems, organoids grown from patient tumor tissue, computational models of tumor evolution.
159
Progress is real.
160
The gap has not closed completely, and may never close completely, because the full complexity of a human patient cannot be replicated in any experimental system.
161
Let's talk about early detection, because this is the dimension of cancer control where where the potential for impact is highest
162
and where the science is advancing most rapidly in ways that don't always make headlines.
163
The single most powerful predictor of cancer survival is how early the disease is detected.
164
Stage 1 breast cancer has a 5-year survival rate above 90%.
165
Stage 4 breast cancer has a 5-year survival rate below 30%.
166
The biology of the cancer may be the same.
167
The outcome is determined largely by how much time the cancer had to grow and spread before it was found.
168
Liquid biopsy, the detection of cancer DNA shed by tumors into the bloodstream, represents one of the most promising current approaches to early detection.
169
A blood test that could detect cancer before symptoms appear, across multiple cancer types from a routine blood draw.
170
Several such tests are in clinical development and early deployment.
171
The challenge is sensitivity and specificity.
172
Detecting enough cancer signal to find early-stage tumors
173
while generating few enough false positives to be useful in screening populations where most people don't have cancer,
174
multi-cancer early detection tests are being evaluated in large clinical trials right now.
175
The results will take years to fully emerge because demonstrating
176
that a test improves outcomes requires following patients long enough to see whether early detection actually translates to lives saved.
177
But the direction is promising in ways that treatment alone has never been.
178
Finding cancer earlier does not cure it in the biological sense, but it dramatically changes the odds of the treatments we already have working well enough to matter.
179
Let's talk about money, because the economics of cancer treatment create incentive structures
180
that affect what gets researched and what gets developed in ways that are important and underappreciated.
181
Drug development is extraordinarily expensive.
182
A new cancer drug costs an estimated $2-3 billion to bring from early research through through clinical trials to approval.
183
This investment requires a return, which means pharmaceutical companies focus development efforts on drugs that can be sold at prices that justify the investment.
184
This creates a structural bias toward treatment rather than prevention.
185
A treatment can be sold repeatedly to a large market of patients.
186
Prevention is harder to monetize because the patient who doesn't get cancer doesn't know they were supposed to.
187
It also creates pressure toward drugs that extend survival by measurable periods, months or years, even when they don't cure.
188
A treatment that gives a patient six additional months of life is demonstrable and reimbursable.
189
A treatment that theoretically prevents recurrence in a patient already declared cancer-free is much harder to sell,
190
because demonstrating prevention requires following patients for decades to show the recurrence didn't happen.
191
Publicly funded research fills some of this gap,
192
but public research funding operates on grant cycles and publication incentives that create their own structural pressures toward incremental,
193
publishable results rather than high-risk work that might not produce results within the grant period.
194
The system around the science shapes what the science investigates and how fast it moves, independently of what the biology actually requires.
195
Now let's talk about what is actually working, because the picture is not uniformly bleak, and the progress deserves precise acknowledgement.
196
Childhood leukemia, once nearly universally fatal, is now curable in approximately 90% of cases.
197
Hodgkin lymphoma has been transformed from a death sentence to a highly treatable disease.
198
Certain types of breast cancer caught early have 5-year survival rates above 90%.
199
CAR T-cell therapy, a technique that engineers a patient's own immune cells to recognize and attack cancer,
200
has produced remarkable results in certain blood cancers, with some patients achieving complete remissions after every other treatment had failed.
201
The pattern in these successes is informative.
202
The cancers most successfully treated tend to be genetically simpler, more accessible, to the immune system or to circulating drugs,
203
and have identifiable genetic drivers that can be specifically targeted.
204
The cancers that remain most resistant tend to be genetically complex,
205
immunologically evasive, and anatomically difficult to access.
206
The progress is real.
207
The remaining problems are genuinely, deeply, biologically hard.
208
Both are true.
209
Let's talk about where the field is actually heading, because the next decade of cancer research looks different from the previous five decades, and the difference matters.
210
Artificial intelligence is changing the speed at which researchers can analyze the enormous datasets that modern cancer biology generates.
211
A human researcher cannot simultaneously hold in mind the mutational profiles of 10,000 tumors, the treatment responses of those patients,
212
the genetic backgrounds of the individuals involved and the thousands of potential drug interactions relevant to those profiles.
213
Computational systems can, at least partially, and are producing genuine insights about cancer subtypes,
214
treatment response prediction, and drug target identification that would not have emerged from human analysis alone.
215
MRNA technology, which became widely known through COVID vaccines, has potential applications in cancer treatment that researchers have been exploring for years.
216
The same platform that can instruct cells to produce a viral protein can potentially instruct immune cells to recognize cancer-specific proteins,
217
producing personalized cancer vaccines tailored to the specific mutational profile of each patient's tumor.
218
Early trials in melanoma and pancreatic cancer have shown promise.
219
The convergence of better biological understanding, better computational tools, better early detection,
220
and better immunological approaches is producing a different landscape than the one that existed when Nixon signed the National Cancer Act.
221
Progress is not a straight line, and will not be, but the direction is real.
222
And this is ultimately the answer to the question in the title.
223
We cannot cure cancer yet because cancer is one of the hardest problems that biology poses to medicine.
224
A moving target built from the body's own machinery, evolving under treatment, distributed across 200 distinct diseases each with its own biology,
225
operating through mechanisms intertwined with the normal cellular processes that keep us alive.
226
Every drug that kills cancer cells risks killing normal cells.
227
Every immune activation risks autoimmunity.
228
Every targeted therapy produces resistance.
229
Every success in one cancer type reveals the unique complexity of the next.
230
Cancer will not be defeated by a single cure.
231
The biology makes that impossible.
232
What will happen, what is already happening,
233
is that specific cancers will be understood and controlled one by one through the specific biology of each disease.
234
Childhood leukemia first, then Hodgkin lymphoma, then certain breast cancers.
235
The list will grow.
236
It will grow slowly by the standards of news cycles and quickly by the standards of how medicine has historically moved.
237
The war Nixon declared was never really one war.
238
It was 200 separate wars fought simultaneously against enemies that keep changing their tactics.
239
Some of those wars are already won.
240
Others are being won now.
241
Others will take longer than any of us would like.
242
But the people fighting them are not losing.
243
They are learning, and in medicine, learning and winning are eventually the same thing.
ดาวน์โหลดแอป
ทุกอย่างที่ต้องใช้เพื่อพูดได้คล่อง
AI ให้คะแนนการออกเสียงให้คะแนนทุกประโยค
ฝึก IPAเชี่ยวชาญทุกเสียง
คำศัพท์สร้างคลังคำของคุณ
เกมคำศัพท์เล่นไปเรียนไป
FOR YOU
วิดีโอแนะนำ
C13
40 ประโยค
The Reason Why Cancer is so Hard to Beat
IELTS PrepTOEFL PrepUniversity Students
C13
259 ประโยค
We've Been Using The Wrong Science In Court For 50 years
IELTS PrepTOEFL PrepUniversity Students
C11
312 ประโยค
Personalised Cancer Care - Prof Mark Middleton
IELTS PrepTOEFL PrepUniversity Students
C12
76 ประโยค
The Nucleus: Crash Course Chemistry #1
IELTS PrepTOEFL PrepUniversity Students
C17
31 ประโยค
SUMMARIZE SPOKEN TEXT - DNA & RNA
IELTS PrepTOEFL PrepUniversity Students
C112
143 ประโยค
What Happens When Science Clashes with the Public?: Crash Course Scientific Thinking #7
IELTS PrepTOEFL PrepUniversity Students
C15
64 ประโยค
Why The War on Drugs Is a Huge Failure
IELTS PrepTOEFL PrepUniversity Students
C17
493 ประโยค
Essentials: Understanding & Healing the Mind | Dr. Karl Deisseroth
IELTS PrepTOEFL PrepUniversity Students
C110
37 ประโยค
Unit 1 Louis Pasteur Learn English via Listening Level 3
IELTS PrepTOEFL PrepUniversity Students
C11
271 ประโยค
The Nearest Worlds to Earth Are Dead. Why Aren't We?
IELTS PrepTOEFL PrepUniversity Students
C15
224 ประโยค
We're Not Ready for Biocomputing
IELTS PrepTOEFL PrepUniversity Students
C18
159 ประโยค
Introduction to the immune system
IELTS PrepTOEFL PrepUniversity Students
คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้
บทเรียนฝึกพูดระดับ C1 นี้ใช้วิดีโอ “Why Can’t We Cure Cancer Yet?” เป็นสื่อ คำที่ถูกพูดซ้ำบ่อยที่สุด: cancer, cell, tumor, treatment, patient วิดีโอนี้มี 243 ประโยค และ 3321 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 21:48 ผู้พูดพูดด้วยความเร็วเป็นธรรมชาติ ประมาณ 152 คำต่อนาที ใกล้เคียงกับบทสนทนาในชีวิตประจำวัน มีเพียง 77% ของคำที่อยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ คำศัพท์จึงค่อนข้างยาก
คำศัพท์สำคัญในวิดีโอนี้
คำที่ยากที่สุด 14 คำในวิดีโอ พร้อมคำอ่านและความหมาย:
| คำศัพท์ | คำอ่าน | ความหมาย |
|---|---|---|
| tumor คำนาม | /tuː.məɹ/ | เนื้องอก |
| mutation คำนาม | /mjuˈteɪʃən/ | การกลายพันธ์, การผ่าเหล่า |
| researcher คำนาม | /ˈɹiˌsɝ.t͡ʃɚ/ | นักวิจัย |
| checkpoint คำนาม | /ˈt͡ʃɛkˌpɔɪnt/ | ด่านตรวจ, จุดตรวจสอบ |
| leukemia คำนาม | /luːˈkiːmi.ə/ | มะเร็งเม็ดเลือดขาว |
| accumulate คำกริยา | /əˈkjuːmjʊˌleɪt/ | เก็บ |
| bacterium คำนาม | /bækˈtɪəɹ.ɪəm/ | แบคทีเรีย, บัคเตรี |
| circulate คำกริยา | /ˈsɝ.kjʊˌleɪt/ | หมุนเวียน |
| lymphoma คำนาม | /lɪmˈfoʊ.mə/ | มะเร็งต่อมน้ำเหลือง |
| remission คำนาม | /ɹəˈmɪʃ(ə)n/ | ใบบุญ |
| vaccine คำนาม | /vækˈsin/ | วัคซีน |
| headline คำนาม | /ˈhɛd.laɪn/ | หัวเรื่อง, หัวข่าว |
| invisible คำคุณศัพท์ | /ɪnˈvɪzəb(ə)l/ | ล่องหน |
| symptom คำนาม | /ˈsɪm(p)təm/ | อาการ |
ไวยากรณ์ในวิดีโอนี้
โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:
| โครงสร้าง | ในวิดีโอ |
|---|---|
| Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน | has now been · have improved · have never been told |
| Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ | was signed · been told · is made |
| Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ | ones that have · return, which means |
การออกเสียงที่ควรระวัง
ผู้พูดใช้รูปย่อและรูปลดเสียง 15 ครั้ง เช่น don't, doesn't, you're ให้พูดแบบสั้นตามที่ได้ยิน
- เสียง “th”: chemotherapy /ˌkiːməʊˈθɛɹəpi/, lethal /ˈliː.θəl/
- เสียง “sh” และ “zh”: mutation /mjuˈteɪʃən/, detection /dɪˈtɛk.ʃən/, computational /ˌkɑmpjʊˈteɪʃənəl/, remission /ɹəˈmɪʃ(ə)n/, evolutionary /ˌiː.vəˈluː.ʃən.ɛɹ.i/
- คำยาว — ลงเสียงหนักให้ถูกพยางค์: genetically /d͡ʒəˈneɾɪkli/, complexity /kəmˈplɛk.sɪ.ti/, inhibitor /ɪnˈhɪbɪtɚ/, leukemia /luːˈkiːmi.ə/, melanoma /mɛləˈnoʊmə/
เสียงที่คนไทยมักออกเสียงยาก:
- เสียงท้ายคำ — ออกเสียงให้ครบ ไม่เปลี่ยนเป็นตัวสะกดแบบไทย: metastasis /məˈtæstəsəs/, computational /ˌkɑmpjʊˈteɪʃənəl/, tolerance /ˈtɑ.lə.ɹəns/, personalize /ˈpɜː.sə.nə.laɪz/, recurrence /rəˈkɝ.əns/
- /v/ — ไม่ใช่ /w/ ให้ฟันบนแตะริมฝีปากล่าง: evade /ɪˈveɪd/, evolutionary /ˌiː.vəˈluː.ʃən.ɛɹ.i/, vaccine /vækˈsin/, invisible /ɪnˈvɪzəb(ə)l/
- /z/ — เสียงก้อง ไม่ใช่ /s/: personalize /ˈpɜː.sə.nə.laɪz/, resistant /ɹəˈzɪstənt/, invisible /ɪnˈvɪzəb(ə)l/
วิธีฝึกกับวิดีโอนี้
- ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
- เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
- อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง tumor, mutation, researcher เป็นพิเศษ
เทคนิค Shadowing คืออะไร?
Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ