Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information

In conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses. For automatically parsing spoken utterances, we introduce a model that integrates transcribed text and acoustic-prosodic features using a convolutional neural network over energy and pitch trajectories coupled with an attention-based recurrent neural network that accepts text and prosodic features. We find that different types of acoustic-prosodic features are individually helpful, and together give statistically significant improvements in parse and disfluency detection F1 scores over a strong text-only baseline. For this study with known sentence boundaries, error analyses show that the main benefit of acoustic-prosodic features is in sentences with disfluencies, attachment decisions are most improved, and transcription errors obscure gains from prosody.Comment: Accepted in NAACL HLT 201

Computational Approaches to the Syntax–Prosody Interface: Using Prosody to Improve Parsing

Prosody has strong ties with syntax, since prosody can be used to resolve some syntactic ambiguities. Syntactic ambiguities have been shown to negatively impact automatic syntactic parsing, hence there is reason to believe that prosodic information can help improve parsing. This dissertation considers a number of approaches that aim to computationally examine the relationship between prosody and syntax of natural languages, while also addressing the role of syntactic phrase length, with the ultimate goal of using prosody to improve parsing. Chapter 2 examines the effect of syntactic phrase length on prosody in double center embedded sentences in French. Data collected in a previous study were reanalyzed using native speaker judgment and automatic methods (forced alignment). Results demonstrate similar prosodic splitting behavior as in English in contradiction to the original study’s findings. Chapter 3 presents a number of studies examining whether syntactic ambiguity can yield different prosodic patterns, allowing humans and/or computers to resolve the ambiguity. In an experimental study, humans disambiguated sentences with prepositional phrase- (PP)-attachment ambiguity with 49% accuracy presented as text, and 63% presented as audio. Machine learning on the same data yielded an accuracy of 63-73%. A corpus study on the Switchboard corpus used both prosodic breaks and phrase lengths to predict the attachment, with an accuracy of 63.5% for PP-attachment sentences, and 71.2% for relative clause attachment. Chapter 4 aims to identify aspects of syntax that relate to prosody and use these in combination with prosodic cues to improve parsing. The aspects identified (dependency configurations) are based on dependency structure, reflecting the relative head location of two consecutive words, and are used as syntactic features in an ensemble system based on Recurrent Neural Networks, to score parse hypotheses and select the most likely parse for a given sentence. Using syntactic features alone, the system achieved an improvement of 1.1% absolute in Unlabelled Attachment Score (UAS) on the test set, above the best parser in the ensemble, while using syntactic features combined with prosodic features (pauses and normalized duration) led to a further improvement of 0.4% absolute. The results achieved demonstrate the relationship between syntax, syntactic phrase length, and prosody, and indicate the ability and future potential of prosody to resolve ambiguity and improve parsing

Model Adaptation for Sentence Unit Segmentation from Speech

The sentence segmentation task is a classification task that aims at inserting sentence boundaries in a sequence of words. One of the applications of sentence segmentation is to detect the sentence boundaries in the sequence of words that is output by an automatic speech recognition system (ASR). The purpose of correctly finding the sentence boundaries in ASR transcriptions is to make it possible to use further processing tasks, such as automatic summarization, machine translation, and information extraction. Being a classification task, sentence segmentation requires training data. To reduce the labor-intensive labeling task, available labeled data can be used to train the classifier. The high variability of speech among the various speech styles makes it inefficient to use the classifier from one speech style (designated as out-of-domain) to detect sentence boundaries on another speech style (in-domain) and thus, makes it necessary for one classifier to be adapted before it is used on another speech style. In this work, we first justify the need for adapting data among the broadcast news, conversational telephone and meeting speech styles. We then propose methods to adapt sentence segmentation models trained on conversational telephone speech to meeting conversations style. Our results show that using the model adapted from the telephone conversations, instead of the model trained only on meetings conversation style, significantly improves the performance of the sentence segmentation. Moreover, this improvement holds independently from the amount of in-domain data used. In addition, we also study the differences between speech styles, with statistical measures and by examining the performances of various subsets of features. Focusing on broadcast news and meeting speech style, we show that on the meeting speech style, lexical features are more correlated with the sentence boundaries than the prosodic features, whereas it is the contrary on the broadcast news. Furthermore, we observe that prosodic features are more independent from the speech style than lexical features

Modelling Incremental Self-Repair Processing in Dialogue.

PhDSelf-repairs, where speakers repeat themselves, reformulate or restart what they are saying, are pervasive in human dialogue. These phenomena provide a window into real-time human language processing. For explanatory adequacy, a model of dialogue must include mechanisms that account for them. Artificial dialogue agents also need this capability for more natural interaction with human users. This thesis investigates the structure of self-repair and its function in the incremental construction of meaning in interaction. A corpus study shows how the range of self-repairs seen in dialogue cannot be accounted for by looking at surface form alone. More particularly it analyses a string-alignment approach and shows how it is insufficient, provides requirements for a suitable model of incremental context and an ontology of self-repair function. An information-theoretic model is developed which addresses these issues along with a system that automatically detects self-repairs and edit terms on transcripts incrementally with minimal latency, achieving state-of-the-art results. Additionally it is shown to have practical use in the psychiatric domain. The thesis goes on to present a dialogue model to interpret and generate repaired utterances incrementally. When processing repaired rather than fluent utterances, it achieves the same degree of incremental interpretation and incremental representation. Practical implementation methods are presented for an existing dialogue system. Finally, a more pragmatically oriented approach is presented to model self-repairs in a psycholinguistically plausible way. This is achieved through extending the dialogue model to include a probabilistic semantic framework to perform incremental inference in a reference resolution domain. The thesis concludes that at least as fine-grained a model of context as word-by-word is required for realistic models of self-repair, and context must include linguistic action sequences and information update effects. The way dialogue participants process self-repairs to make inferences in real time, rather than filter out their disfluency effects, has been modelled formally and in practical systems.Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Account (DTA) scholarship from the School of Electronic Engineering and Computer Science at Queen Mary University of London

Toward Widely-Available and Usable Multimodal Conversational Interfaces

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 159-166).Multimodal conversational interfaces, which allow humans to interact with a computer using a combination of spoken natural language and a graphical interface, offer the potential to transform the manner by which humans communicate with computers. While researchers have developed myriad such interfaces, none have made the transition out of the laboratory and into the hands of a significant number of users. This thesis makes progress toward overcoming two intertwined barriers preventing more widespread adoption: availability and usability. Toward addressing the problem of availability, this thesis introduces a new platform for building multimodal interfaces that makes it easy to deploy them to users via the World Wide Web. One consequence of this work is City Browser, the first multimodal conversational interface made publicly available to anyone with a web browser and a microphone. City Browser serves as a proof-of-concept that significant amounts of usage data can be collected in this way, allowing a glimpse of how users interact with such interfaces outside of a laboratory environment. City Browser, in turn, has served as the primary platform for deploying and evaluating three new strategies aimed at improving usability. The most pressing usability challenge for conversational interfaces is their limited ability to accurately transcribe and understand spoken natural language. The three strategies developed in this thesis - context-sensitive language modeling, response confidence scoring, and user behavior shaping - each attack the problem from a different angle, but they are linked in that each critically integrates information from the conversational context.by Alexander Gruenstein.Ph.D

On Repairing Sentences: An Experimental and Computational Analysis of Recovery from Unexpected Syntactic Disambiguation in Sentence Parsing

This thesis was originally embargoed but the embargo was removed on 29 October 2014 at the request of the author.This thesis contends that the human parser has a repair mechanism. It is further contended that the human parser uses this mechanism to alter previously built structure in the case of unexpected disambiguation of temporary syntactic ambiguity. This position stands in opposition to the claim that unexpected disambiguation of temporary syntactic ambiguity is accomplished by the usual first pass parsing routines, a claim that arises from the relatively extraordinary capabilities of computational parsers, capabilities which have recently been extended by hypothesis to be available to the human sentence processing mechanism. The thesis argues that, while these capabilities have been demonstrated in computational parsers, the human parser is best explained in the terms of a repair based framework, and that this argument is demonstrated by examining eye movement behaviour in reading. In support of the thesis, evidence is provided from a set of eye tracking studies of reading. It is argued that these studies show that eye movement behaviours at disambiguation include purposeful visual search for linguistically relevant material, and that the form and structure of these searches vary reliably according to the nature of the repairs that the sentences necessitate.ESR

PCFGs with syntactic and prosodic indicators of speech repairs

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 classifier that uses acoustic cues to identify where normal prosody seems to be interrupted (Lickley, 1996). The other cue, syntactic parallelism, codifies the expectation that repairs continue a syntactic category that was left unfinished in the reparandum (Levelt, 1983). The two cues are combined in a Treebank PCFG whose states are split using a few simple tree transformations. Parsing performance on the Switchboard and Fisher corpora suggests that these two cues help to locate speech repairs in a synergistic way.