# Resource-Efficient Speech Quality Prediction through Quantization Aware Training and Binary Activation Maps

arXiv: https://arxiv.org/abs/2407.04578 · HTML: https://mlsanity.com/paper/2407.04578/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 32 references that are in the map.
- First posted on arXiv 5 Jul 2024.

## Builds on (most cited first, 25 of 25)

- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors](https://mlsanity.com/paper/2010.15258/) (2020)
- [NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets](https://mlsanity.com/paper/2104.09494/) (2021)
- [SuperSpike: Supervised learning in multi-layer spiking neural networks](https://mlsanity.com/paper/1705.11146/) (2017)
- [The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results](https://mlsanity.com/paper/2005.13981/) (2020)
- [ICASSP 2023 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2303.11510/) (2023)
- [TorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudio](https://mlsanity.com/paper/2304.01448/) (2023)
- [Sudo rm -rf: Efficient Networks for Universal Audio Source Separation](https://mlsanity.com/paper/2007.06833/) (2020)
- [A scalable noisy speech dataset and online subjective test framework](https://mlsanity.com/paper/1909.08050/) (2019)
- [TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids](https://mlsanity.com/paper/2005.11138/) (2020)
- [Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM](https://mlsanity.com/paper/1808.05344/) (2018)
- [Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features](https://mlsanity.com/paper/2111.02363/) (2021)
- [STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model](https://mlsanity.com/paper/2011.04292/) (2020)
- [Don't shoot butterfly with rifles: Multi-channel Continuous Speech Separation with Early Exit Transformer](https://mlsanity.com/paper/2010.12180/) (2020)
- [NORESQA: A Framework for Speech Quality Assessment using Non-Matching References](https://mlsanity.com/paper/2109.08125/) (2021)
- [MetricNet: Towards Improved Modeling For Non-Intrusive Speech Quality Assessment](https://mlsanity.com/paper/2104.01227/) (2021)
- [The Intel Neuromorphic DNS Challenge](https://mlsanity.com/paper/2303.09503/) (2023)
- [Latent Iterative Refinement for Modular Source Separation](https://mlsanity.com/paper/2211.11917/) (2022)
- [Dynamic nsNet2: Efficient Deep Noise Suppression with Early Exiting](https://mlsanity.com/paper/2308.16678/) (2023)
- [Learning to Inference with Early Exit in the Progressive Speech Enhancement](https://mlsanity.com/paper/2106.11730/) (2021)
- [Wideband Audio Waveform Evaluation Networks: Efficient, Accurate Estimation of Speech Qualities](https://mlsanity.com/paper/2206.13272/) (2022)
- [Naaloss: Rethinking the objective of speech enhancement](https://mlsanity.com/paper/2308.12615/) (2023)

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

- [Scalable Speech Enhancement with Dynamic Channel Pruning](https://mlsanity.com/paper/2412.17121/) (2024)
- [From Diet to Free Lunch: Estimating Auxiliary Signal Properties using Dynamic Pruning Masks in Speech Enhancement Networks](https://mlsanity.com/paper/2602.10666/) (2026)

## Abstract (arXiv)

As speech processing systems in mobile and edge devices become more commonplace, the demand for unintrusive speech quality monitoring increases. Deep learning methods provide high-quality estimates of objective and subjective speech quality metrics. However, their significant computational requirements are often prohibitive on resource-constrained devices. To address this issue, we investigated binary activation maps (BAMs) for speech quality prediction on a convolutional architecture based on DNSMOS. We show that the binary activation model with quantization aware training matches the predictive performance of the baseline model. It further allows using other compression techniques. Combined with 8-bit weight quantization, our approach results in a 25-fold memory reduction during inference, while replacing almost all dot products with summations. Our findings show a path toward substantial resource savings by supporting mixed-precision binary multiplication in hard- and software.