# Exploiting Beam Search Confidence for Energy-Efficient Speech Recognition

arXiv: https://arxiv.org/abs/2101.09083 · HTML: https://mlsanity.com/paper/2101.09083/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 82 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 18 of 18)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2010.10504/) (2020)
- [End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures](https://mlsanity.com/paper/1911.08460/) (2019)
- [Transformer-based Acoustic Modeling for Hybrid Speech Recognition](https://mlsanity.com/paper/1910.09799/) (2019)
- [Jasper: An End-to-End Convolutional Neural Acoustic Model](https://mlsanity.com/paper/1904.03288/) (2019)
- [Achieving Human Parity in Conversational Speech Recognition](https://mlsanity.com/paper/1610.05256/) (2016)
- [Speaker Recognition Based on Deep Learning: An Overview](https://mlsanity.com/paper/2012.00931/) (2020)
- [ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA](https://mlsanity.com/paper/1612.00694/) (2016)
- [Best-First Beam Search](https://mlsanity.com/paper/2007.03909/) (2020)
- [UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition](https://mlsanity.com/paper/1804.06508/) (2018)
- [MASR: A Modular Accelerator for Sparse RNNs](https://mlsanity.com/paper/1908.08976/) (2019)
- [E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks](https://mlsanity.com/paper/1711.07480/) (2017)

## Abstract (arXiv)

With computers getting more and more powerful and integrated in our daily lives, the focus is increasingly shifting towards more human-friendly interfaces, making Automatic Speech Recognition (ASR) a central player as the ideal means of interaction with machines. Consequently, interest in speech technology has grown in the last few years, with more systems being proposed and higher accuracy levels being achieved, even surpassing textitHuman Accuracy. While ASR systems become increasingly powerful, the computational complexity also increases, and the hardware support have to keep pace. In this paper, we propose a technique to improve the energy-efficiency and performance of ASR systems, focusing on low-power hardware for edge devices. We focus on optimizing the DNN-based Acoustic Model evaluation, as we have observed it to be the main bottleneck in state-of-the-art ASR systems, by leveraging run-time information from the Beam Search. By doing so, we reduce energy and execution time of the acoustic model evaluation by 25.6% and 25.9%, respectively, with negligible accuracy loss.