A typical fluency scoring system generally relies on an automatic speech
recognition (ASR) system to obtain time stamps in input speech for either the
subsequent calculation of fluency-related features or directly modeling speech
fluency with an end-to-end approach. This paper describes a novel ASR-free
approach for automatic fluency assessment using self-supervised learning (SSL).
Specifically, wav2vec2.0 is used to extract frame-level speech features,
followed by K-means clustering to assign a pseudo label (cluster index) to each
frame. A BLSTM-based model is trained to predict an utterance-level fluency
score from frame-level SSL features and the corresponding cluster indexes.
Neither speech transcription nor time stamp information is required in the
proposed system. It is ASR-free and can potentially avoid the ASR errors effect
in practice. Experimental results carried out on non-native English databases
show that the proposed approach significantly improves the performance in the
"open response" scenario as compared to previous methods and matches the
recently reported performance in the "read aloud" scenario.Comment: Accepted by ICASSP 202