# How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR

arXiv: https://arxiv.org/abs/2201.06685 · HTML: https://mlsanity.com/paper/2201.06685/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2022.

## Built on by (most cited first, 28 of 28)

- [Hallucinations in Neural Automatic Speech Recognition: Identifying Errors and Hallucinatory Models](https://mlsanity.com/paper/2401.01572/) (2024)
- [End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation](https://mlsanity.com/paper/2204.00540/) (2022)
- [Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR](https://mlsanity.com/paper/2304.04974/) (2023)
- [TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition](https://mlsanity.com/paper/2308.10415/) (2023)
- [Measuring the Effect of Transcription Noise on Downstream Language Understanding Tasks](https://mlsanity.com/paper/2502.13645/) (2025)
- [Thunder : Unified Regression-Diffusion Speech Enhancement with a Single Reverse Step using Brownian Bridge](https://mlsanity.com/paper/2406.06139/) (2024)
- [Dual-Branch Knowledge Distillation for Noise-Robust Synthetic Speech Detection](https://mlsanity.com/paper/2310.08869/) (2023)
- [A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and Recognition](https://mlsanity.com/paper/2405.16952/) (2024)
- [Vibravox: A Dataset of French Speech Captured with Body-conduction Audio Sensors](https://mlsanity.com/paper/2407.11828/) (2024)
- [An Investigation of Noise Robustness for Flow-Matching-Based Zero-Shot TTS](https://mlsanity.com/paper/2406.05699/) (2024)
- [Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts](https://mlsanity.com/paper/2211.06493/) (2022)
- [Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications](https://mlsanity.com/paper/2502.20311/) (2025)
- [Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance](https://mlsanity.com/paper/2408.06264/) (2024)
- [CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR](https://mlsanity.com/paper/2502.20040/) (2025)
- [A Semantic Information-based Hierarchical Speech Enhancement Method Using Factorized Codec and Diffusion Model](https://mlsanity.com/paper/2505.13843/) (2025)
- [Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription](https://mlsanity.com/paper/2309.08454/) (2023)
- [Frequency-Weighted Training Losses for Phoneme-Level DNN-based Speech Enhancement](https://mlsanity.com/paper/2506.18714/) (2025)
- [Error Analysis in a Modular Meeting Transcription System](https://mlsanity.com/paper/2509.10143/) (2025)
- [Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning](https://mlsanity.com/paper/2309.09270/) (2023)
- [VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR Identification](https://mlsanity.com/paper/2502.07205/) (2025)
- [SEED: Speaker Embedding Enhancement Diffusion Model](https://mlsanity.com/paper/2505.16798/) (2025)
- [Revealing the Role of Audio Channels in ASR Performance Degradation](https://mlsanity.com/paper/2508.08967/) (2025)
- [Alternating Approach-Putt Models for Multi-Stage Speech Enhancement](https://mlsanity.com/paper/2508.10436/) (2025)
- [Latent-Level Enhancement with Flow Matching for Robust Automatic Speech Recognition](https://mlsanity.com/paper/2601.04459/) (2026)
- [Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits](https://mlsanity.com/paper/2602.15405/) (2026)
- [Training-Free Intelligibility-Guided Observation Addition for Noisy ASR](https://mlsanity.com/paper/2602.20967/) (2026)
- [Parallel Time-Band Mixing with Learned Observation-Adding for Robust ASR Front-Ends](https://mlsanity.com/paper/2608.30326/) (2026)
- [Cleaner Speech, Weaker Generalization: Revisiting Pitt-Derived Benchmarks for Alzheimer's Disease Detection](https://mlsanity.com/paper/2609.00276/) (2026)

## Abstract (arXiv)

It is challenging to improve automatic speech recognition (ASR) performance in noisy conditions with single-channel speech enhancement (SE). In this paper, we investigate the causes of ASR performance degradation by decomposing the SE errors using orthogonal projection-based decomposition (OPD). OPD decomposes the SE errors into noise and artifact components. The artifact component is defined as the SE error signal that cannot be represented as a linear combination of speech and noise sources. We propose manually scaling the error components to analyze their impact on ASR. We experimentally identify the artifact component as the main cause of performance degradation, and we find that mitigating the artifact can greatly improve ASR performance. Furthermore, we demonstrate that the simple observation adding (OA) technique (i.e., adding a scaled version of the observed signal to the enhanced speech) can monotonically increase the signal-to-artifact ratio under a mild condition. Accordingly, we experimentally confirm that OA improves ASR performance for both simulated and real recordings. The findings of this paper provide a better understanding of the influence of SE errors on ASR and open the door to future research on novel approaches for designing effective single-channel SE front-ends for ASR.