Clear spoken English: pace, structure and filler words
30 min · Beginner
Need help? Ask a tutor on WhatsApp (opens in a new tab)Interviewers and GD evaluators hear dozens of candidates a day. They are not marking your accent or your big words; they check whether they understood your point the first time.
Point-reason-example structure for answers
Point-reason-example (PRE) is a three-part way to answer any question: your point first in one sentence, then the reason, then one example. PREP adds the point again at the end as a close.
You say your point first in one sentence, then the reason behind it, then one example that proves it. Some people add the point again at the end, which is called PREP. Most unclear answers put the point last, after a long story.
Example 1 · A rambling answer versus a PRE answer
Question: "Why did you choose Python?"Rambling: "So, in first year we had C, and then, um, my friend told me about Python, and there are many jobs, and I also tried C++ once, but then in third year I did a project, so basically I think Python is good."PRE: Point: "I chose Python because it is used widely for backend systems, which is the work I want to do." Reason: "Its short, readable code let me finish projects quickly, and the libraries I needed already existed." Example: "In my library app, a ready-made library let me build the search in two days and spend the rest on testing."Rambling: point comes last and is vague ("Python is good")
PRE: point in the first sentence, one reason, one real example
Length: about 20 seconds, easy to followWhat this code does
- The rambling answer has some real content (a project, job demand) but hides it in a timeline.
- The PRE answer can be cut after any sentence and still makes sense.
- The example is specific (a wrong return type) and comes from the student's own project.
PRE puts the point first, so even if the interviewer stops you after ten seconds, they already have your answer. Revise: Tell me about yourself: your first PRE answer (opens in a new tab)
Order of execution
Build a PRE answer
Tap the lines in the order you would say them.
Pace, pauses and clear pronunciation
Words per minute (wpm) is the usual measure of speaking pace: the words you speak divided by the minutes you took. A comfortable interview pace is roughly 120 to 160 wpm.
A common guide for interviews is roughly 120 to 160 wpm: slower sounds unsure, faster is hard to follow, especially on a phone or video call. A pause is a short silence of about a second.
Example 2 · Measuring your speaking pace
def verdict(wpm): if wpm < 110: return "slow - add energy" elif wpm <= 160: return "comfortable" else: return "fast - add pauses"
answers = ["Tell me about yourself", "Explain your project", "Why this company"]words = [210, 180, 95]seconds = [75, 80, 60]
for answer, w, s in zip(answers, words, seconds): wpm = w * 60 // s print(f"{answer:<24} {wpm:>3} wpm {verdict(wpm)}")Tell me about yourself 168 wpm fast - add pauses Explain your project 135 wpm comfortable Why this company 95 wpm slow - add energy
What this code does
- Words per minute = words × 60 / seconds. Count the words from a written copy of your recording.
- The "Tell me about yourself" answer is fast because it is the most rehearsed, a common pattern.
- The bands (below 110, 110 to 160, above 160) are a practical guide, not a strict rule.
Pause after your main point and before a number or name, so it lands. For pronunciation, say each word fully, including endings like "-ed" and "-s", and slow down on technical names. Both common forms are fine for some terms: SQL can be "S-Q-L" or "sequel".
Reducing filler words (um, like, basically)
A filler word is a sound or word that fills silence but adds no meaning, such as "um", "uh", "like", "basically" or "you know".
A few fillers are normal, but many make you sound unsure and hide your point. The fix is to replace each filler with a short, silent pause. First record yourself and count your fillers, so you know your number.
Example 3 · Counting filler words in a transcript
import re
FILLERS = ["um", "uh", "basically", "actually", "like", "you know"]
def count(transcript): clean = " " + " ".join(re.sub(r"[^a-z ]", " ", transcript.lower()).split()) + " " counts = {} for filler in FILLERS: # (?= ...) looks ahead, so two fillers in a row are both counted n = len(re.findall(rf"(?= {filler} )", clean)) if n > 0: counts[filler] = n return counts
transcript = ( "Um, so basically my project is, like, a library app. Uh, it basically " "stores books, you know, and, um, users can, like, borrow them. " "Actually it uses Python and MySQL.")counts = count(transcript)for filler, n in counts.items(): print(f"{filler}: {n}")words = len(transcript.split())print(f"Fillers: {sum(counts.values())} in {words} words")um: 2 uh: 1 basically: 2 actually: 1 like: 2 you know: 1 Fillers: 9 in 30 words
What this code does
- Punctuation is replaced by spaces and every word is padded with spaces, so " um " matches "Um," but not the "um" inside "museum".
- The look-ahead pattern
(?= um )matches without using up the text, so two fillers in a row are both counted. - The counter cannot tell a filler "like" from a real one ("I like Python"); read the flagged lines yourself.
Then practise the same answer with pauses, and count again.
Find the bug
Find the filler words
Tap the line where fillers hide the point.
Which line has the most filler words?
Round 1 of 3Explaining a technical project simply
A simple project explanation says what the project does for a user before how it works inside: the problem and the user first, then the main feature, then one technical detail.
Start with the problem and the user, then the main feature, then one technical detail you are proud of, in that order. Avoid a list of technologies as your opening ("Python, Django, MySQL, JWT, Docker...").
Example 4 · A jargon-first explanation versus a simple one
Jargon-first: "My project uses Python 3.12, Django, MySQL, SQLAlchemy, JWT auth, REST APIs, Docker and a React frontend."Simple: "I built a library app for my college. Students can search for a book, see if it is available and reserve it from their phone, so they do not walk to the library for nothing. The backend is in Python with Django, and the part I am proudest of is the reservation logic: two students cannot reserve the last copy at the same time."1. Problem and user: students walking to the library for nothing 2. Main feature: search, check availability, reserve 3. Technology: named once, briefly 4. One detail you are proud of: no double reservation
What this code does
- The listener knows what the app is for before hearing any technology names.
- The proud detail invites a good follow-up question ("How did you stop double reservations?").
- A list of eight technologies invites eight questions, some of which you may not want.
Name a technology only when you can say why you used it, in one plain sentence.
Daily speaking practice routine
A practice routine is a short, fixed set of speaking exercises done every day, for example 15 minutes of recorded answers, so that clear speaking becomes a habit.
Fifteen minutes daily for four weeks usually helps more than a two-hour session once a week, because speaking is a habit. Record everything on your phone.
Example 5 · A 15-minute daily routine
Minutes Exercise0-3 Read one news paragraph aloud, slowly, with full word endings3-6 Answer one HR question with PRE; record it6-9 Listen back; count fillers and note your pace9-12 Answer the same question again, with pauses in place of fillers12-15 Explain one concept (for example, what an API is) to an imaginary first-year student in under one minuteDate | Question | Fillers (try 1 -> try 2) | Pace (wpm) Aim: fewer fillers on try 2 than try 1, pace near 120-160 wpm
What this code does
- Each day has a first try and a second try, so you can see improvement within 15 minutes.
- Explaining to a first-year student forces simple words, which also helps in interviews.
- Keeping a small log for four weeks turns "I think I improved" into real numbers.
Listening to yourself feels strange at first, but it is the fastest way to hear fillers, speed and unclear words that you do not notice while speaking.
Next step
Check your understanding
12 quick questions