Disfluency Detection with a Semi-Markov Model and Prosodic Features

Abstract

We present a discriminative model for detecting disfluencies in spoken language transcripts. Structurally, our model is a semi-Markov conditional random field with features targeting characteristics unique to speech repairs. This gives a significant performance improvement over standard chain-structured CRFs that have been employed in past work. We then incorporate prosodic features over silences and relative word duration into our semi-CRF model, resulting in further performance gains; moreover, these features are not easily replaced by discrete prosodic indicators such as ToBI breaks. Our final system, the semi-CRF with prosodic information, achieves an F-score of 85.4, which is 1.3 F 1 better than the best prior reported F-score on this dataset.

Transition-Based Disfluency Detection using LSTMs

“…We compare our transition-based neural model to five top performing systems. Our model outperforms the state-of-the-art, achieving a 87.5% F- 91.6 82.3 86.7 Bi-LSTM (Zayats et al, 2016) 91.8 80.6 85.9 semi-CRF (Ferguson et al, 2015) 90.0 81.2 85.4 UBT (Wu et al, 2015) 90.3 80.5 85.1 M 3 N (Qian and Liu, 2013) --84.1 Table 5: Comparison with previous state-of-theart methods on the test set of English Switchboard.…”

“…It achieves 2.4 point improvements over UBT (Wu et al, 2015), which is the best syntax-based method for disfluency detection. The best performance by linear statistical sequence labeling methods is the semi-CRF method of Ferguson et al (2015), achieving a 85.4% Fscore leveraging prosodic features. Our model obtains a 2.1 point improvement compared to this.…”

“…The hand-crafted discrete features of CRF refer to those in Ferguson et al (2015). For the Bi-LSTM model, the token embedding is the same with our transition-based method.…”

“…Another drawback of these approaches is that they are unable to exploit chunk-level features. Semi-CRF (Ferguson et al, 2015) is used to alleviate this issue to some extent. Semi-CRF models still have their inefficiencies because they can only use the local chunk information limited by the markov assumption when decoding.…”

Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model

“…4 Qian and Liu (2013) 82.1 Honnibal and Johnson (2014) 84.1 Ferguson et al (2015) * 85.4 Zwarts and Johnson (2011) 85.7 Zayats et al (2016) * 85.9 LSTM-NCM 86.8 Table 6: Comparison of the LSTM-NCM to stateof-the-art methods on the dev set. *Models have used richer input.…”