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Found 6 papers, 3 papers with code
End-to-End Speech Recognition and Disfluency Removal
Disfluency detection is usually an intermediate step between an automatic speech recognition (ASR) system and a downstream task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Improving Disfluency Detection by Self-Training a Self-Attentive Model
However, we show that self-training - a semi-supervised technique for incorporating unlabeled data - sets a new state-of-the-art for the self-attentive parser on disfluency detection, demonstrating that self-training provides benefits orthogonal to the pre-trained contextualized word representations.
ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detection
This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO).
Neural Constituency Parsing of Speech Transcripts
This paper studies the performance of a neural self-attentive parser on transcribed speech.
Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model
This paper presents a model for disfluency detection in spontaneous speech transcripts called LSTM Noisy Channel Model.
Disfluency Detection using Auto-Correlational Neural Networks
In recent years, the natural language processing community has moved away from task-specific feature engineering, i. e., researchers discovering ad-hoc feature representations for various tasks, in favor of general-purpose methods that learn the input representation by themselves.
