Hybrid TTS Linguist: Text-to-Speech Quality

Localization Academy Inc.; Dublin; Hybrid; EUR 38,000 - 55,000; Full time

Benefits offered by this job

Hybrid-working model

Extensive Training & Development.

Internal career progression

Employee assistance programme (EAP).

Hive medical cash plan.

PRSA Scheme.

Employee engagement initiatives

Bike to work scheme.

Job summary

Covalen is seeking a detail-oriented Support Linguist for Text-to-Speech (TTS) to enhance speech technologies. You will evaluate linguistic and audio quality, contribute to text normalization, and identify language-specific challenges using datasets and guidelines for accuracy.

Ideal candidates have strong linguistic knowledge, sharp attention to detail, and experience with Pandas/Regex/Python to support daily data-processing tasks. Hybrid work model and global client projects await.

Qualifications

• Native or near-native speaker of the target language with fluent English (C1).
• Degree in Linguistics or related field or demonstrated IPA knowledge.
• Working knowledge of Pandas, Regex, and text-processing tools.

Responsibilities

• Evaluate linguistic and audio quality of Text-to-Speech outputs.
• Support improvements in the TTS pipeline, including text normalization and pronunciation prediction.
• Identify language-specific challenges and propose test scenarios to evaluate them.
• Perform data quality checks and improve linguistic datasets.
• Analyse text and linguistic datasets to identify patterns, errors and improvement opportunities.
• Use text-processing and data-analysis techniques to analyze linguistic data.

Skills

Strong analytical thinking

Attention to detail

Excellent communication

Fluent English

Language evaluation

Education

Linguistics degree

Bachelor’s degree or equivalent

IPA knowledge

Tools

Pandas

Regex

Python

Job description

Covalen is seeking a detail-oriented Support Linguist for Text-to-Speech (TTS) to enhance speech technologies. You will evaluate linguistic and audio quality, contribute to text normalization, and identify language-specific challenges using datasets and guidelines for accuracy.

Ideal candidates have strong linguistic knowledge, sharp attention to detail, and experience with Pandas/Regex/Python to support daily data-processing tasks. Hybrid work model and global client projects await.