# Weighted Speech Distortion Losses for Neural-network-based Real-time Speech Enhancement

arXiv: https://arxiv.org/abs/2001.10601 · HTML: https://mlsanity.com/paper/2001.10601/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 19 of 19)

- [Real-time Denoising and Dereverberation with Tiny Recurrent U-Net](https://mlsanity.com/paper/2102.03207/) (2021)
- [McNet: Fuse Multiple Cues for Multichannel Speech Enhancement](https://mlsanity.com/paper/2211.08872/) (2022)
- [Accelerating RNN-based Speech Enhancement on a Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization](https://mlsanity.com/paper/2210.07692/) (2022)
- [Training Speech Enhancement Systems with Noisy Speech Datasets](https://mlsanity.com/paper/2105.12315/) (2021)
- [End-to-End Complex-Valued Multidilated Convolutional Neural Network for Joint Acoustic Echo Cancellation and Noise Suppression](https://mlsanity.com/paper/2110.00745/) (2021)
- [Noise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancement](https://mlsanity.com/paper/2309.01164/) (2023)
- [Diffusion-based Signal Refiner for Speech Enhancement and Separation](https://mlsanity.com/paper/2305.05857/) (2023)
- [Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models](https://mlsanity.com/paper/2110.05968/) (2021)
- [Deep Audio Waveform Prior](https://mlsanity.com/paper/2207.10441/) (2022)
- [Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading](https://mlsanity.com/paper/2210.15448/) (2022)
- [CheapNET: Improving Light-weight speech enhancement network by projected loss function](https://mlsanity.com/paper/2311.15959/) (2023)
- [Hold Me Tight: Stable Encoder-Decoder Design for Speech Enhancement](https://mlsanity.com/paper/2408.17358/) (2024)
- [An Enhanced Adversarial Network with Combined Latent Features for Spatio-Temporal Facial Affect Estimation in the Wild](https://mlsanity.com/paper/2102.09150/) (2021)
- [Efficient Audiovisual Speech Processing via MUTUD: Multimodal Training and Unimodal Deployment](https://mlsanity.com/paper/2501.18157/) (2025)
- [Task-aware Warping Factors in Mask-based Speech Enhancement](https://mlsanity.com/paper/2108.12128/) (2021)
- [Acoustic Echo Cancellation using Residual U-Nets](https://mlsanity.com/paper/2109.09686/) (2021)
- [Performance optimizations on deep noise suppression models](https://mlsanity.com/paper/2110.04378/) (2021)
- [Leveraging Local and Global Knowledge Integration with Time-Frequency Calibrated Distillation for Speech Enhancement](https://mlsanity.com/paper/2506.13127/) (2025)
- [Cleaner Speech, Weaker Generalization: Revisiting Pitt-Derived Benchmarks for Alzheimer's Disease Detection](https://mlsanity.com/paper/2609.00276/) (2026)

## Abstract (arXiv)

This paper investigates several aspects of training a RNN (recurrent neural network) that impact the objective and subjective quality of enhanced speech for real-time single-channel speech enhancement. Specifically, we focus on a RNN that enhances short-time speech spectra on a single-frame-in, single-frame-out basis, a framework adopted by most classical signal processing methods. We propose two novel mean-squared-error-based learning objectives that enable separate control over the importance of speech distortion versus noise reduction. The proposed loss functions are evaluated by widely accepted objective quality and intelligibility measures and compared to other competitive online methods. In addition, we study the impact of feature normalization and varying batch sequence lengths on the objective quality of enhanced speech. Finally, we show subjective ratings for the proposed approach and a state-of-the-art real-time RNN-based method.