Sequence-level Confidence Classifier For ASR Utterance Accuracy And Application To Acoustic Models

Abstract

Scores from traditional confidence classifiers (CCs) in automatic speech recognition (ASR) systems lack universal interpretation and vary with updates to the underlying confidence or acoustic models (AMs). In this work, we build interpretable confidence scores with an objective to closely align with ASR accuracy. We propose a new sequence-level CC with a richer context providing CC scores highly correlated with ASR accuracy and scores stable across CC updates. Hence, expanding CC applications. Recently, AM customization has gained traction with the widespread use of unified models. Conventional adaptation strategies that customize AM expect well-matched data for the target domain with gold-standard transcriptions. We propose a cost-effective method of using CC scores to select an optimal adaptation data set, where we maximize ASR gains from minimal data. We study data in various confidence ranges and optimally choose data for AM adaptation with KL-Divergence regularization. On the Micr

Stats

• citations4
• S2 citations—
• github stars0
• HF likes0
• heat score5.24
• arxiv keyafshan2021sequence

Related papers

• Confidence Score Based Conformer Speaker Adaptation For Speech Recognition (2022)8.09
• Confidence Score Based Speaker Adaptation Of Conformer Speech Recognition Systems (2023)8.35
• Confidence Estimation For Attention-based Sequence-to-sequence Models For Speech Recognition (2020)11.49
• Accurate And Reliable Confidence Estimation Based On Non-autoregressive End-to-end Speech Recognition System (2023)4.52
• An Evaluation Of Word-level Confidence Estimation For End-to-end Automatic Speech Recognition (2021)0.00
• A Highly Adaptive Acoustic Model For Accurate Multi-dialect Speech Recognition (2022)10.85
• Semantic-aware Confidence Calibration For Automated Audio Captioning (2025)0.00
• Advancing Test-time Adaptation In Wild Acoustic Test Settings (2023)2.26