Обнаружение Дисфлюенции

Unrehearsed spoken language often contains disfluencies. In order to correctly inter- pret a spoken utterance, any such disfluen- cies must be identified and removed or other- wise dealt with. Operating …...

We investigate two systems for automatic dis- fluency detection on English and Mandarin conversational speech data. The first system combines various lexical and prosodic fea- tures in a Conditional Random …...

In this paper, we propose a multi-step stacked learning model for disfluency detection. Our method incorporates refined n-gram features step by step from different word sequences. First, we detect filler …...

In this paper1, we present a novel beam-search decoder for disfluency detection. We first pro- pose node-weighted max-margin Markov networks (M3N) to boost the performance on words belonging to specific …...

AbstractWe present an incremental dependency parsing model that jointly performs disfluency detection. The model handles speech repairs using a novel non-monotonic transition system, and includes several novel classes of features. …...

Speech transcripts often only capture seman- tic content, omitting disfluencies that can be useful for analyzing social dynamics of a dis- cussion. This work describes steps in build- ing a …...

AbstractIn this paper, we study the problem of disfluency detection using the encoder-decoder framework. We treat disfluency detection as a sequence-to-sequence problem and propose a neural attention-based model which can …...

Joint dependency parsing with disfluency de- tection is an important task in speech lan- guage processing. Recent methods show high performance for this task, although most au- thors make the …...

AbstractIn this paper, we model the problem of disfluency detection using a transition-based framework, which incrementally constructs and labels the disfluency chunk of input sentences using a new transition system …...

AbstractWhile the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity. To tackle this problem, we propose a novel semi-supervised approach …...

AbstractThis paper studies the performance of a neural self-attentive parser on transcribed speech. Speech presents parsing challenges that do not appear in written text, such as the lack of punctuation …...

AbstractMost existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice. There have been several proposals to alleviate this issue with, for instance, …...

AbstractDisfluent speech has been previously addressed from two main perspectives: the clinical perspective focusing on diagnostic, and the Natural Language Processing (NLP) perspective aiming at modeling these events and detect …...

AbstractSelf-attentive neural syntactic parsers using contextualized word embeddings (e.g. ELMo or BERT) currently produce state-of-the-art results in joint parsing and disfluency detection in speech transcripts. Since the contextualized word embeddings …...

AbstractExisting approaches to disfluency detection heavily depend on human-annotated data. Numbers of data augmentation methods have been proposed to alleviate the dependence on labeled data. However, current augmentation approaches such …...

AbstractWe present a multi-task learning framework to enable the training of one universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation …...

AbstractSupervised methods have achieved remarkable results in disfluency detection. However, in real-world scenarios, human-annotated data is difficult to obtain. Recent works try to handle disfluency detection with unsupervised self-training, which …...

AbstractDisfluency detection is a critical task in real-time dialogue systems. However, despite its importance, it remains a relatively unexplored field, mainly due to the lack of appropriate datasets. At the …...

AbstractIn modern interactive speech-based systems, speech is consumed and transcribed incrementally prior to having disfluencies removed. While this post-processing step is crucial for producing clean transcripts and high performance on …...