Switchboard корпусы

In this paper we exhibit a novel approach to the problems of topic and speaker identification that makes use of a large vocabulary continuous speech recognizer. We present a theo- …...

We present a framework for the integrated analysis of the textual and prosodic char- acteristics of information structure in the Switchboard corpus of conversational En- glish. Information structure describes the …...

We identify a set of prosodic cues for parsing con- versational speech and show how such features can be effectively incorporated into a statistical parsing model. On the Switchboard corpus …...

Recent developments in statistical modeling of various linguistic phenomena have shown that additional features give consistent per- formance improvements. Quite often, im- provements are limited by the number of fea- …...

A grammatical method of combining two kinds of speech repair cues is presented. One cue, prosodic disjuncture, is detected by a decision tree-based ensemble clas- sifier that uses acoustic cues …...

This paper describes an incremental ap- proach to parsing transcribed spontaneous speech containing disfluencies with a Hier- archical Hidden Markov Model (HHMM). This model makes use of the right-corner transform, …...

AbstractCorpus-based dialogue systems rely on statistical models, whose parameters are inferred from annotated dialogues. The dialogues are usually annotated in terms of Dialogue Acts (DA), and the manual annotation is …...

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 …...

AbstractIn this study, we applied a deep LSTM structure to classify dialogue acts (DAs) in open-domain conversations. We found that the word embeddings parameters, dropout regularization, decay rate and number …...

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 …...

AbstractPrevious work on dialog act (DA) classification has investigated different methods, such as hidden Markov models, maximum entropy, conditional random fields, graphical models, and support vector machines. A few recent …...

Motivated by a project to create a system for people who are deaf or hard-of-hearing that would use automatic speech recognition (ASR) to produce real-time text captions of spoken English …...

AbstractConversational context information, higher-level knowledge that spans across sentences, can help to recognize a long conversation. However, existing speech recognition models are typically built at a sentence level, and thus …...

AbstractWe present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings. Unlike conventional speech recognition models, our model learns longer conversational-context information …...

AbstractSuccess of deep learning techniques have renewed the interest in development of dialogue systems. However, current systems struggle to have consistent long term conversations with the users and fail to …...

AbstractRecent dialogue coherence models use the coherence features designed for monologue texts, e.g. nominal entities, to represent utterances and then explicitly augment them with dialogue-relevant features, e.g., dialogue act labels. …...

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 …...

AbstractIn response to (i) inconclusive results in the literature as to the properties of coreference chains in written versus spoken language, and (ii) a general lack of work on automatic …...

AbstractSpoken language is different from the written language in its style and structure. Disfluencies that appear in transcriptions from speech recognition systems generally hamper the performance of downstream NLP tasks. …...

AbstractParsing spoken dialogue poses unique difficulties, including disfluencies and unmarked boundaries between sentence-like units. Previous work has shown that prosody can help with parsing disfluent speech (Tran et al. 2018), …...

AbstractThis study proposes an utterance position-aware approach for a neural network-based dialogue act recognition (DAR) model, which incorporates positional encoding for utterance’s absolute or relative position. The proposed approach is …...

AbstractThere has been a lot of work on predicting the timing of feedback in conversational systems. However, there has been less focus on predicting the prosody and lexical form of …...

AbstractIn this paper, we explore the task of determining indirect answers to yes-no questions in real conversations. We work with transcripts of phone conversations in the Switchboard Dialog Act (SwDA) …...