🔊 Listening Script（約20行）
Good afternoon, everyone. Today, I’d like to briefly go over our company’s upcoming transition to machine learning-based systems.
As you know, we’ve been relying on manual processes for quality inspection for many years.
However, starting in October, we will begin implementing image recognition models to automatically identify product defects.
These models are trained using thousands of labeled images collected from past production lines.
Early tests show a 92% accuracy rate—significantly higher than the current manual inspection accuracy of 78%.
This change will not eliminate human jobs, but instead allow our staff to focus on more complex tasks.
Our data science team is currently working with the manufacturing department to fine-tune the algorithm for different product types.
The full rollout is expected by the end of the year, and we’ll provide training sessions for affected departments in early November.
If you have concerns about how this change will affect your daily work, please speak with your department manager.
Finally, we’ll host a demonstration session next Tuesday at 2 p.m. in Lab Room B.
You’ll be able to see the system in action and ask questions directly to the developers.
Please register for the demo by this Friday through the internal portal.
Thank you for your time, and we look forward to this exciting step toward smarter operations.
📘 Questions
Question 1: What is the main purpose of the talk?
(A) To report a budget increase
(B) To announce a change in the inspection system
(C) To introduce new staff members
(D) To request feedback on a software bug
Question 2: What is said about the accuracy of the new system?
(A) It is lower than manual inspection
(B) It is the same as human accuracy
(C) It reaches over 90 percent
(D) It hasn’t been tested yet
Question 3: What are employees asked to do by Friday?
(A) Submit training reports
(B) Complete a feedback survey
(C) Register for a demonstration
(D) Install software updates
📝 Explanation（解説）
Question 1 解説：
スピーカーは「machine learning-based systems」の導入を説明し、特に「image recognition models」の使用開始を通じて品質検査を自動化する方針を述べています。目的は「検査システムの変更の通知」です。
✅ 正解：(B)
Question 2 解説：
スクリプトには「Early tests show a 92% accuracy rate」とあり、これは「90%以上の精度」という意味です。人手による検査よりも精度が高いと明言されています。
✅ 正解：(C)
Question 3 解説：
最後に「Please register for the demo by this Friday」と明確に指示があり、金曜日までに求められている行動は「デモへの登録」です。
✅ 正解：(C)
🔊 Listening Script（約20行）
Hello everyone, and thank you for joining today’s briefing.
I’m excited to share some updates about how we’re using machine learning to improve our business operations.
Specifically, we’re introducing a new system for analyzing customer feedback.
In the past, we had staff manually review survey responses, which took a lot of time and sometimes caused delays.
Now, we’re implementing a sentiment analysis tool that uses natural language processing to classify feedback as positive, negative, or neutral.
This model has been trained on more than 50,000 comments from various sources, and it’s already showing an accuracy rate of over 90 percent.
By automating this task, we can respond to customer concerns faster and improve our services more efficiently.
It also helps our customer service team focus on solving more complex issues rather than reading every comment.
Starting next Monday, the tool will be used in a trial phase with the marketing and support teams.
A training session will be held this Friday at 3 p.m. in Meeting Room 2.
Please make sure to register for the session by Thursday using the internal system.
If you have questions, feel free to contact the data science department.
We look forward to seeing how this tool enhances our decision-making.
📘 Questions
Question 1: What is the main topic of the talk?
(A) A change in office policy
(B) A new customer service procedure
(C) The launch of a machine learning system
(D) An update to a product line
Question 2: What task will the machine learning model perform?
(A) Translating user reviews
(B) Scanning product barcodes
(C) Classifying customer feedback
(D) Scheduling meetings automatically
Question 3: What are employees asked to do by Thursday?
(A) Submit a report to the manager
(B) Register for a training session
(C) Fill out a feedback form
(D) Attend a product demo
📝 Explanation（解説）
Question 1 解説：
スピーカーは「machine learning」を使った「顧客フィードバックの分析システム導入」について話しており、その目的や仕組みを説明しています。
✅ 正解：(C) The launch of a machine learning system
Question 2 解説：
スクリプト内に「sentiment analysis tool」や「classify feedback as positive, negative, or neutral」と明言されており、モデルの役割は顧客コメントの分類です。
✅ 正解：(C) Classifying customer feedback
Question 3 解説：
「Please make sure to register for the session by Thursday」と明確に述べられているため、木曜日までに必要な行動は「トレーニングの登録」です。
✅ 正解：(B) Register for a training session