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Pronunciation Annotation - Tag Words in Text with Phonetic Metadata

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Interview Experience

Round 1 Coding

Problem

Given a sentence and a dictionary mapping words to their phonetic annotation (e.g. IPA or a simplified pronunciation string),

return the sentence with each recognized word wrapped in an annotation tag. Unrecognized words are left as-is. The matching is case-insensitive; the original casing must be preserved in output.

python
def annotate_pronunciation(
    sentence: str,
    phoneme_dict: dict[str, str]
) -> str:
    pass

Example


**Input**:
sentence = "The quick brown fox"
phoneme_dict = {"the": "dh-ah", "fox": "f-aa-k-s"}

**Output**:
"[The|dh-ah] quick brown [fox|f-aa-k-s]"

Example 2


**Input**:
sentence = "Reading is fun"
phoneme_dict = {"reading": "r-ee-d-ih-ng"}

**Output**:
"[Reading|r-ee-d-ih-ng] is fun"

Follow-ups

  1. How do you handle multi-word phrases in the dictionary, like {"New York": "n-y-oo-y-aw-r-k"}?
  2. What if the same word has different pronunciations depending on part of speech (e.g. "read" present vs past)?
  3. How would you build the phoneme_dict automatically from a corpus using a text-to-speech API?
  4. How do you handle punctuation attached to words (fox, should still match fox)?

Full Details

Round 1 Coding

Problem

Given a sentence and a dictionary mapping words to their phonetic annotation (e.g. IPA or a simplified pronunciation string),

return the sentence with each recognized word wrapped in an annotation tag. Unrecognized words are left as-is. The matching is case-insensitive; the original casing must be preserved in output.

python
def annotate_pronunciation(
    sentence: str,
    phoneme_dict: dict[str, str]
) -> str:
    pass

Example


**Input**:
sentence = "The quick brown fox"
phoneme_dict = {"the": "dh-ah", "fox": "f-aa-k-s"}

**Output**:
"[The|dh-ah] quick brown [fox|f-aa-k-s]"

Example 2


**Input**:
sentence = "Reading is fun"
phoneme_dict = {"reading": "r-ee-d-ih-ng"}

**Output**:
"[Reading|r-ee-d-ih-ng] is fun"

Follow-ups

  1. How do you handle multi-word phrases in the dictionary, like {"New York": "n-y-oo-y-aw-r-k"}?
  2. What if the same word has different pronunciations depending on part of speech (e.g. "read" present vs past)?
  3. How would you build the phoneme_dict automatically from a corpus using a text-to-speech API?
  4. How do you handle punctuation attached to words (fox, should still match fox)?
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About This Question

This is a candidate experience report from a heygen interview during the phone round.

It covers the following topics: Coding, Phone, Onsite, Strings .

Topics

Coding Phone Onsite Strings
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About Heygen Interview Reports

This question was reported by a candidate who interviewed at Heygen. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.

Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Heygen are the higher-signal extractions to take from this report.

For broader preparation context, the Heygen interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.

How To Practice This Type of Question

Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.

Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Heygen reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Heygen Round

Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.

The single most predictive failure mode in Heygen reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.