Predicting Word Error Rate For Reverberant Speech

Abstract

Reverberation negatively impacts the performance of automatic speech recognition (ASR). Prior work on quantifying the effect of reverberation has shown that clarity (C50), a parameter that can be estimated from the acoustic impulse response, is correlated with ASR performance. In this paper we propose predicting ASR performance in terms of the word error rate (WER) directly from acoustic parameters via a polynomial, sigmoidal, or neural network fit, as well as blindly from reverberant speech samples using a convolutional neural network (CNN). We carry out experiments on two state-of-the-art ASR models and a large set of acoustic impulse responses (AIRs). The results confirm C50 and C80 to be highly correlated with WER, allowing WER to be predicted with the proposed fitting approaches. The proposed non-intrusive CNN model outperforms C50-based WER prediction, indicating that WER can be estimated blindly, i.e., directly from the reverberant speech samples without knowledge of the acousti

Stats

• citations8
• S2 citations—
• github stars0
• HF likes0
• heat score7.16
• arxiv keygamper2019predicting

Related papers

• Automatic Speech Recognition System-independent Word Error Rate Estimation (2024)3.58
• Fast Word Error Rate Estimation Using Self-supervised Representations For Speech And Text (2023)5.24
• ASR Performance Prediction On Unseen Broadcast Programs Using Convolutional Neural Networks (2018)3.58
• On The Impact Of Word Error Rate On Acoustic-linguistic Speech Emotion Recognition: An Update For The Deep Learning Era (2021)0.00
• Speech Dereverberation With Context-aware Recurrent Neural Networks (2017)10.35
• On Combining Features For Single-channel Robust Speech Recognition In Reverberant Environments (2019)0.00
• Beyond Levenshtein: Leveraging Multiple Algorithms For Robust Word Error Rate Computations And Granular Error Classifications (2024)2.26
• Neural Network-augmented Kalman Filtering For Robust Online Speech Dereverberation In Noisy Reverberant Environments (2022)0.00