# TLT-school: a Corpus of Non Native Children Speech

arXiv: https://arxiv.org/abs/2001.08051 · HTML: https://mlsanity.com/paper/2001.08051/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2020.

## Built on by (most cited first, 12 of 12)

- [speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment](https://mlsanity.com/paper/2104.01378/) (2021)
- [Proficiency assessment of L2 spoken English using wav2vec 2.0](https://mlsanity.com/paper/2210.13168/) (2022)
- [Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback](https://mlsanity.com/paper/2412.11986/) (2024)
- [Automatic Pronunciation Assessment -- A Review](https://mlsanity.com/paper/2310.13974/) (2023)
- [Low Resource German ASR with Untranscribed Data Spoken by Non-native Children -- INTERSPEECH 2021 Shared Task SPAPL System](https://mlsanity.com/paper/2106.09963/) (2021)
- [Mixtures of Deep Neural Experts for Automated Speech Scoring](https://mlsanity.com/paper/2106.12475/) (2021)
- [Improving Children's Speech Recognition by Fine-tuning Self-supervised Adult Speech Representations](https://mlsanity.com/paper/2211.07769/) (2022)
- [Enhancing conversational quality in language learning chatbots: An evaluation of GPT4 for ASR error correction](https://mlsanity.com/paper/2307.09744/) (2023)
- [Building a Non-native Speech Corpus Featuring Chinese-English Bilingual Children: Compilation and Rationale](https://mlsanity.com/paper/2305.00446/) (2023)
- [Error-preserving Automatic Speech Recognition of Young English Learners' Language](https://mlsanity.com/paper/2406.03235/) (2024)
- [AraYoungVoices: A Diverse L1/L2 Corpus of Arabic Child and Adolescent Speech](https://mlsanity.com/paper/2610.05044/) (2026)
- [Child ASR Adaptation with Adult Retention: An Empirical Study](https://mlsanity.com/paper/2610.08827/) (2026)

## Abstract (arXiv)

This paper describes "TLT-school" a corpus of speech utterances collected in schools of northern Italy for assessing the performance of students learning both English and German. The corpus was recorded in the years 2017 and 2018 from students aged between nine and sixteen years, attending primary, middle and high school. All utterances have been scored, in terms of some predefined proficiency indicators, by human experts. In addition, most of utterances recorded in 2017 have been manually transcribed carefully. Guidelines and procedures used for manual transcriptions of utterances will be described in detail, as well as results achieved by means of an automatic speech recognition system developed by us. Part of the corpus is going to be freely distributed to scientific community particularly interested both in non-native speech recognition and automatic assessment of second language proficiency.