251 research outputs found
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
We describe a statistical approach for modeling dialogue acts in
conversational speech, i.e., speech-act-like units such as Statement, Question,
Backchannel, Agreement, Disagreement, and Apology. Our model detects and
predicts dialogue acts based on lexical, collocational, and prosodic cues, as
well as on the discourse coherence of the dialogue act sequence. The dialogue
model is based on treating the discourse structure of a conversation as a
hidden Markov model and the individual dialogue acts as observations emanating
from the model states. Constraints on the likely sequence of dialogue acts are
modeled via a dialogue act n-gram. The statistical dialogue grammar is combined
with word n-grams, decision trees, and neural networks modeling the
idiosyncratic lexical and prosodic manifestations of each dialogue act. We
develop a probabilistic integration of speech recognition with dialogue
modeling, to improve both speech recognition and dialogue act classification
accuracy. Models are trained and evaluated using a large hand-labeled database
of 1,155 conversations from the Switchboard corpus of spontaneous
human-to-human telephone speech. We achieved good dialogue act labeling
accuracy (65% based on errorful, automatically recognized words and prosody,
and 71% based on word transcripts, compared to a chance baseline accuracy of
35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling
changed
Framework for Human Computer Interaction for Learning Dialogue Strategies using Controlled Natural Language in Information Systems
Spoken Language systems are going to have a tremendous impact in all
the real world applications, be it healthcare enquiry, public transportation
system or airline booking system maintaining the language ethnicity for
interaction among users across the globe. These system have the capability
of interacting with the user in di erent languages that the system
supports. Normally when a person interacts with another person there are
many non-verbal clues which guide the dialogue and all the utterances have
a contextual relationship, which manage the dialogue as its mixed by the
two speakers. Human Computer Interaction has a wide impact on the design
of the applications and has become one of the emerging interest area of
the researchers. All of us are witness to an explosive electronic revolution
where lots of gadgets and gizmo's have surrounded us, advanced not only
in power, design, applications but the ease of access or what we call user
friendly interfaces are designed that we can easily use and control all the
functionality of the devices. Since speech is one of the most intuitive form
of interaction that humans use. It provides potential bene ts such as handfree
access to machines, ergonomics and greater e ciency of interaction.
Yet, speech-based interfaces design has been an expert job for a long time.
Lot of research has been done in building real spoken Dialogue Systems
which can interact with humans using voice interactions and help in performing
various tasks as are done by humans. Last two decades have seen
utmost advanced research in the automatic speech recognition, dialogue
management, text to speech synthesis and Natural Language Processing
for various applications which have shown positive results. This dissertation
proposes to apply machine learning (ML) techniques to the problem
of optimizing the dialogue management strategy selection in the Spoken
Dialogue system prototype design. Although automatic speech recognition
and system initiated dialogues where the system expects an answer in the
form of `yes' or `no' have already been applied to Spoken Dialogue Systems(
SDS), no real attempt to use those techniques in order to design a
new system from scratch has been made. In this dissertation, we propose
some novel ideas in order to achieve the goal of easing the design of Spoken
Dialogue Systems and allow novices to have access to voice technologies.
A framework for simulating and evaluating dialogues and learning optimal
dialogue strategies in a controlled Natural Language is proposed. The simulation
process is based on a probabilistic description of a dialogue and
on the stochastic modelling of both arti cial NLP modules composing a
SDS and the user. This probabilistic model is based on a set of parameters
that can be tuned from the prior knowledge from the discourse or learned
from data. The evaluation is part of the simulation process and is based
on objective measures provided by each module. Finally, the simulation
environment is connected to a learning agent using the supplied evaluation
metrics as an objective function in order to generate an optimal behaviour
for the SDS
Statistical language modelling of dialogue material in the British national corpus.
Statistical language modelling may not only be used to uncover the patterns which underlie the composition of utterances and texts, but also to build practical language processing technology. Contemporary language applications in automatic speech recognition, sentence interpretation and even machine translation exploit statistical models of language. Spoken dialogue systems, where a human user interacts with a machine via a speech interface in order to get information, make bookings, complaints, etc., are example of such systems which are now technologically feasible. The majority of statistical language modelling studies to date have concentrated on written text material (or read versions thereof). However, it is well-known that dialogue is significantly different from written text in its lexical content and sentence structure. Furthermore, there are expected to be significant logical, thematic and lexical connections between successive turns within a dialogue, but "turns" are not generally meaningful in written text. There is therefore a need for statistical language modeling studies to be performed on dialogue, particularly with a longer-term aim to using such models in human-machine dialogue interfaces. In this thesis, I describe the studies I have carried out on statistically modelling the dialogue material within the British National Corpus (BNC) - a very large corpus of modern British English compiled during the 1990s. This thesis presents a general introductory survey of the field of automatic speech recognition. This is followed by a general introduction to some standard techniques of statistical language modelling which will be employed later in the thesis. The structure of dialogue is discussed using some perspectives from linguistic theory, and reviews some previous approaches (not necessarily statistical) to modelling dialogue. Then a qualitative description is given of the BNC and the dialogue data within it, together with some descriptive statistics relating to it and results from constructing simple trigram language models for both dialogue and text data. The main part of the thesis describes experiments on the application of statistical language models based on word caches, word "trigger" pairs, and turn clustering to the dialogue data. Several different approaches are used for each type of model. An analysis of the strengths and weaknesses of these techniques is then presented. The results of the experiments lead to a better understanding of how statistical language modelling might be applied to dialogue for the benefit of future language technologies
Intelligent Techniques to Accelerate Everyday Text Communication
People with some form of speech- or motor-impairments usually use a high-tech augmentative and alternative communication (AAC) device to communicate with other people in writing or in face-to-face conversations. Their text entry rate on these devices is slow due to their motor abilities. Making good letter or word predictions can help accelerate the communication of such users. In this dissertation, we investigated several approaches to accelerate input for AAC users. First, considering that an AAC user is participating in a face-to-face conversation, we investigated whether performing speech recognition on the speaking-side can improve next word predictions. We compared the accuracy of three plausible microphone deployment options and the accuracy of two commercial speech recognition engines. We found that despite recognition word error rates of 7-16%, our ensemble of n-gram and recurrent neural network language models made predictions nearly as good as when they used the reference transcripts. In a user study with 160 participants, we also found that increasing number of prediction slots in a keyboard interface does not necessarily correlate to improved performance. Second, typing every character in a text message may require an AAC user more time or effort than strictly necessary. Skipping spaces or other characters may be able to speed input and reduce an AAC user\u27s physical input effort. We designed a recognizer optimized for expanding noisy abbreviated input where users often omitted spaces and mid-word vowels. We showed using neural language models for selecting conversational-style training text and for rescoring the recognizer\u27s n-best sentences improved accuracy. We found accurate abbreviated input was possible even if a third of characters was omitted. In a study where users had to dwell for a second on each key, we found sentence abbreviated input was competitive with a conventional keyboard with word predictions. Finally, AAC keyboards rely on language modeling to auto-correct noisy typing and to offer word predictions. While today language models can be trained on huge amounts of text, pre-trained models may fail to capture the unique writing style and vocabulary of individual users. We demonstrated improved performance compared to a unigram cache by adapting to a user\u27s text via language models based on prediction by partial match (PPM) and recurrent neural networks. Our best model ensemble increased keystroke savings by 9.6%
Accessing spoken interaction through dialogue processing [online]
Zusammenfassung
Unser Leben, unsere Leistungen und unsere Umgebung, alles wird
derzeit durch Schriftsprache dokumentiert. Die rasante
Fortentwicklung der technischen Möglichkeiten Audio, Bilder und
Video aufzunehmen, abzuspeichern und wiederzugeben kann genutzt
werden um die schriftliche Dokumentation von menschlicher
Kommunikation, zum Beispiel Meetings, zu unterstützen, zu
ergänzen oder gar zu ersetzen. Diese neuen Technologien können
uns in die Lage versetzen Information aufzunehmen, die
anderweitig verloren gehen, die Kosten der Dokumentation zu
senken und hochwertige Dokumente mit audiovisuellem Material
anzureichern. Die Indizierung solcher Aufnahmen stellt die
Kerntechnologie dar um dieses Potential auszuschöpfen. Diese
Arbeit stellt effektive Alternativen zu schlüsselwortbasierten
Indizes vor, die Suchraumeinschränkungen bewirken und teilweise
mit einfachen Mitteln zu berechnen sind.
Die Indizierung von Sprachdokumenten kann auf verschiedenen
Ebenen erfolgen: Ein Dokument gehört stilistisch einer
bestimmten Datenbasis an, welche durch sehr einfache Merkmale
bei hoher Genauigkeit automatisch bestimmt werden kann.
Durch diese Art von Klassifikation kann eine Reduktion des
Suchraumes um einen Faktor der Größenordnung 410 erfolgen. Die
Anwendung von thematischen Merkmalen zur Textklassifikation
bei einer Nachrichtendatenbank resultiert in einer Reduktion um
einen Faktor 18. Da Sprachdokumente sehr lang sein können müssen
sie in thematische Segmente unterteilt werden. Ein neuer
probabilistischer Ansatz sowie neue Merkmale (Sprecherinitia
tive und Stil) liefern vergleichbare oder bessere Resultate als
traditionelle schlüsselwortbasierte Ansätze. Diese thematische
Segmente können durch die vorherrschende Aktivität
charakterisiert werden (erzählen, diskutieren, planen, ...),
die durch ein neuronales Netz detektiert werden kann. Die
Detektionsraten sind allerdings begrenzt da auch Menschen
diese Aktivitäten nur ungenau bestimmen. Eine maximale
Reduktion des Suchraumes um den Faktor 6 ist bei den verwendeten
Daten theoretisch möglich. Eine thematische Klassifikation
dieser Segmente wurde ebenfalls auf einer Datenbasis
durchgeführt, die Detektionsraten für diesen Index sind jedoch
gering.
Auf der Ebene der einzelnen Äußerungen können Dialogakte wie
Aussagen, Fragen, Rückmeldungen (aha, ach ja, echt?, ...) usw.
mit einem diskriminativ trainierten Hidden Markov Model erkannt
werden. Dieses Verfahren kann um die Erkennung von kurzen Folgen
wie Frage/AntwortSpielen erweitert werden (Dialogspiele).
Dialogakte und spiele können eingesetzt werden um
Klassifikatoren für globale Sprechstile zu bauen. Ebenso
könnte ein Benutzer sich an eine bestimmte Dialogaktsequenz
erinnern und versuchen, diese in einer grafischen
Repräsentation wiederzufinden.
In einer Studie mit sehr pessimistischen Annahmen konnten
Benutzer eines aus vier ähnlichen und gleichwahrscheinlichen
Gesprächen mit einer Genauigkeit von ~ 43% durch eine graphische
Repräsentation von Aktivität bestimmt.
Dialogakte könnte in diesem Szenario ebenso nützlich sein, die
Benutzerstudie konnte aufgrund der geringen Datenmenge darüber
keinen endgültigen Aufschluß geben. Die Studie konnte allerdings
für detailierte Basismerkmale wie Formalität und
Sprecheridentität keinen Effekt zeigen.
Abstract
Written language is one of our primary means for documenting our
lives, achievements, and environment. Our capabilities to
record, store and retrieve audio, still pictures, and video are
undergoing a revolution and may support, supplement or even
replace written documentation. This technology enables us to
record information that would otherwise be lost, lower the cost
of documentation and enhance highquality documents with
original audiovisual material.
The indexing of the audio material is the key technology to
realize those benefits. This work presents effective
alternatives to keyword based indices which restrict the search
space and may in part be calculated with very limited resources.
Indexing speech documents can be done at a various levels:
Stylistically a document belongs to a certain database which can
be determined automatically with high accuracy using very simple
features. The resulting factor in search space reduction is in
the order of 410 while topic classification yielded a factor
of 18 in a news domain.
Since documents can be very long they need to be segmented into
topical regions. A new probabilistic segmentation framework as
well as new features (speaker initiative and style) prove to be
very effective compared to traditional keyword based methods. At
the topical segment level activities (storytelling, discussing,
planning, ...) can be detected using a machine learning approach
with limited accuracy; however even human annotators do not
annotate them very reliably. A maximum search space reduction
factor of 6 is theoretically possible on the databases used. A
topical classification of these regions has been attempted
on one database, the detection accuracy for that index, however,
was very low.
At the utterance level dialogue acts such as statements,
questions, backchannels (aha, yeah, ...), etc. are being
recognized using a novel discriminatively trained HMM procedure.
The procedure can be extended to recognize short sequences such
as question/answer pairs, so called dialogue games.
Dialog acts and games are useful for building classifiers for
speaking style. Similarily a user may remember a certain dialog
act sequence and may search for it in a graphical
representation.
In a study with very pessimistic assumptions users are able to
pick one out of four similar and equiprobable meetings correctly
with an accuracy ~ 43% using graphical activity information.
Dialogue acts may be useful in this situation as well but the
sample size did not allow to draw final conclusions. However the
user study fails to show any effect for detailed basic features
such as formality or speaker identity
Advances in deep learning methods for speech recognition and understanding
Ce travail expose plusieurs études dans les domaines de
la reconnaissance de la parole et
compréhension du langage parlé.
La compréhension sémantique du langage parlé est un sous-domaine important
de l'intelligence artificielle.
Le traitement de la parole intéresse depuis longtemps les chercheurs,
puisque la parole est une des charactéristiques qui definit l'être humain.
Avec le développement du réseau neuronal artificiel,
le domaine a connu une évolution rapide
à la fois en terme de précision et de perception humaine.
Une autre étape importante a été franchie avec le développement
d'approches bout en bout.
De telles approches permettent une coadaptation de toutes
les parties du modèle, ce qui augmente ainsi les performances,
et ce qui simplifie la procédure d'entrainement.
Les modèles de bout en bout sont devenus réalisables avec la quantité croissante
de données disponibles, de ressources informatiques et,
surtout, avec de nombreux développements architecturaux innovateurs.
Néanmoins, les approches traditionnelles (qui ne sont pas bout en bout)
sont toujours pertinentes pour le traitement de la parole en raison
des données difficiles dans les environnements bruyants,
de la parole avec un accent et de la grande variété de dialectes.
Dans le premier travail, nous explorons la reconnaissance de la parole hybride
dans des environnements bruyants.
Nous proposons de traiter la reconnaissance de la parole,
qui fonctionne dans
un nouvel environnement composé de différents bruits inconnus,
comme une tâche d'adaptation de domaine.
Pour cela, nous utilisons la nouvelle technique à l'époque
de l'adaptation du domaine antagoniste.
En résumé, ces travaux antérieurs proposaient de former
des caractéristiques de manière à ce qu'elles soient distinctives
pour la tâche principale, mais non-distinctive pour la tâche secondaire.
Cette tâche secondaire est conçue pour être la tâche de reconnaissance de domaine.
Ainsi, les fonctionnalités entraînées sont invariantes vis-à-vis du domaine considéré.
Dans notre travail, nous adoptons cette technique et la modifions pour
la tâche de reconnaissance de la parole dans un environnement bruyant.
Dans le second travail, nous développons une méthode générale
pour la régularisation des réseaux génératif récurrents.
Il est connu que les réseaux récurrents ont souvent des difficultés à rester
sur le même chemin, lors de la production de sorties longues.
Bien qu'il soit possible d'utiliser des réseaux bidirectionnels pour
une meilleure traitement de séquences pour l'apprentissage des charactéristiques,
qui n'est pas applicable au cas génératif.
Nous avons développé un moyen d'améliorer la cohérence de
la production de longues séquences avec des réseaux récurrents.
Nous proposons un moyen de construire un modèle similaire à un réseau bidirectionnel.
L'idée centrale est d'utiliser une perte L2 entre
les réseaux récurrents génératifs vers l'avant et vers l'arrière.
Nous fournissons une évaluation expérimentale sur
une multitude de tâches et d'ensembles de données,
y compris la reconnaissance vocale,
le sous-titrage d'images et la modélisation du langage.
Dans le troisième article, nous étudions la possibilité de développer
un identificateur d'intention de bout en bout pour la compréhension du langage parlé.
La compréhension sémantique du langage parlé est une étape importante vers
le développement d'une intelligence artificielle de type humain.
Nous avons vu que les approches de bout en bout montrent
des performances élevées sur les tâches, y compris la traduction automatique et
la reconnaissance de la parole.
Nous nous inspirons des travaux antérieurs pour développer
un système de bout en bout pour la reconnaissance de l'intention.This work presents several studies in the areas of speech recognition and
understanding.
The semantic speech understanding is an important sub-domain of the
broader field of artificial intelligence.
Speech processing has had interest from the researchers for long time
because language is one of the defining characteristics of a human being.
With the development of neural networks, the domain has seen rapid progress
both in terms of accuracy and human perception.
Another important milestone was achieved with the development of
end-to-end approaches.
Such approaches allow co-adaptation of all the parts of the model
thus increasing the performance, as well as simplifying the training
procedure.
End-to-end models became feasible with the increasing amount of available
data, computational resources, and most importantly with many novel
architectural developments.
Nevertheless, traditional, non end-to-end, approaches are still relevant
for speech processing due to challenging data in noisy environments,
accented speech, and high variety of dialects.
In the first work, we explore the hybrid speech recognition in noisy
environments.
We propose to treat the recognition in the unseen noise condition
as the domain adaptation task.
For this, we use the novel at the time technique of the adversarial
domain adaptation.
In the nutshell, this prior work proposed to train features in such
a way that they are discriminative for the primary task,
but non-discriminative for the secondary task.
This secondary task is constructed to be the domain recognition task.
Thus, the features trained are invariant towards the domain at hand.
In our work, we adopt this technique and modify it for the task of
noisy speech recognition.
In the second work, we develop a general method for regularizing
the generative recurrent networks.
It is known that the recurrent networks frequently have difficulties
staying on same track when generating long outputs.
While it is possible to use bi-directional networks for better
sequence aggregation for feature learning, it is not applicable
for the generative case.
We developed a way improve the consistency of generating long sequences
with recurrent networks.
We propose a way to construct a model similar to bi-directional network.
The key insight is to use a soft L2 loss between the forward and
the backward generative recurrent networks.
We provide experimental evaluation on a multitude of tasks and datasets,
including speech recognition, image captioning, and language modeling.
In the third paper, we investigate the possibility of developing
an end-to-end intent recognizer for spoken language understanding.
The semantic spoken language understanding is an important
step towards developing a human-like artificial intelligence.
We have seen that the end-to-end approaches show high
performance on the tasks including machine translation and speech recognition.
We draw the inspiration from the prior works to develop
an end-to-end system for intent recognition
An Overview on Language Models: Recent Developments and Outlook
Language modeling studies the probability distributions over strings of
texts. It is one of the most fundamental tasks in natural language processing
(NLP). It has been widely used in text generation, speech recognition, machine
translation, etc. Conventional language models (CLMs) aim to predict the
probability of linguistic sequences in a causal manner. In contrast,
pre-trained language models (PLMs) cover broader concepts and can be used in
both causal sequential modeling and fine-tuning for downstream applications.
PLMs have their own training paradigms (usually self-supervised) and serve as
foundation models in modern NLP systems. This overview paper provides an
introduction to both CLMs and PLMs from five aspects, i.e., linguistic units,
structures, training methods, evaluation methods, and applications.
Furthermore, we discuss the relationship between CLMs and PLMs and shed light
on the future directions of language modeling in the pre-trained era
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