# Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition

arXiv: https://arxiv.org/abs/2102.09106 · HTML: https://mlsanity.com/paper/2102.09106/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2021.

## Built on by (most cited first, 9 of 9)

- [Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation Models](https://mlsanity.com/paper/2406.10507/) (2024)
- [Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults](https://mlsanity.com/paper/2309.07927/) (2023)
- [Adaptation of Whisper models to child speech recognition](https://mlsanity.com/paper/2307.13008/) (2023)
- [A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-Tuning Results for Child Speech Synthesis](https://mlsanity.com/paper/2203.11562/) (2022)
- [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)
- [Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition](https://mlsanity.com/paper/2202.12326/) (2022)
- [Evaluation of Speech Foundation Models for ASR on Child-Adult Conversations in Autism Diagnostic Sessions](https://mlsanity.com/paper/2409.16135/) (2024)
- [Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home](https://mlsanity.com/paper/2306.00482/) (2023)
- [Improved Child Text-to-Speech Synthesis through Fastpitch-based Transfer Learning](https://mlsanity.com/paper/2311.04313/) (2023)

## Abstract (arXiv)

Automatic speech recognition (ASR) systems for young children are needed due to the importance of age-appropriate educational technology. Because of the lack of publicly available young child speech data, feature extraction strategies such as feature normalization and data augmentation must be considered to successfully train child ASR systems. This study proposes a novel technique for child ASR using both feature normalization and data augmentation methods based on the relationship between formants and fundamental frequency (fo). Both the fo feature normalization and data augmentation techniques are implemented as a frequency shift in the Mel domain. These techniques are evaluated on a child read speech ASR task. Child ASR systems are trained by adapting a BLSTM-based acoustic model trained on adult speech. Using both fo normalization and data augmentation results in a relative word error rate (WER) improvement of 19.3% over the baseline when tested on the OGI Kids' Speech Corpus, and the resulting child ASR system achieves the best WER currently reported on this corpus.