# Speech Recovery for Real-World Self-powered Intermittent Devices

arXiv: https://arxiv.org/abs/2106.05229 · HTML: https://mlsanity.com/paper/2106.05229/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Phase-aware Speech Enhancement with Deep Complex U-Net](https://mlsanity.com/paper/1903.03107/) (2019)
- [Speech Denoising with Deep Feature Losses](https://mlsanity.com/paper/1806.10522/) (2018)
- [Audio-Visual Speech Inpainting with Deep Learning](https://mlsanity.com/paper/2010.04556/) (2020)
- [Deep speech inpainting of time-frequency masks](https://mlsanity.com/paper/1910.09058/) (2019)
- [SERIL: Noise Adaptive Speech Enhancement using Regularization-based Incremental Learning](https://mlsanity.com/paper/2005.11760/) (2020)

## Built on by (most cited first, 2 of 2)

- [An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition](https://mlsanity.com/paper/2210.05614/) (2022)
- [Robust Multi-Modal Speech In-Painting: A Sequence-to-Sequence Approach](https://mlsanity.com/paper/2406.00901/) (2024)

## Abstract (arXiv)

The incompleteness of speech inputs severely degrades the performance of all the related speech signal processing applications. Although many researches have been proposed to address this issue, they controlled the data missing conditions by simulation with self-defined masking lengths or sizes. Besides, the masking definitions are different among all these experimental settings. This paper presents a novel intermittent speech recovery (ISR) system for real-world self-powered intermittent devices. Three contributive stages: interpolation, enhancement, and combination are applied to the ISR system for speech reconstruction. The experimental results show that our recovery system increases speech quality by up to 591.7%, while increasing speech intelligibility by up to 80.5%. Most importantly, the proposed ISR system improves the WER scores by up to 52.6%. The promising results not only confirm the effectiveness of the reconstruction but also encourage the utilization of these battery-free wearable/IoT devices.