388 research outputs found
Robust audiovisual speech recognition using noise-adaptive linear discriminant analysis
© 2016 IEEE.Automatic speech recognition (ASR) has become a widespread and convenient mode of human-machine interaction, but it is still not sufficiently reliable when used under highly noisy or reverberant conditions. One option for achieving far greater robustness is to include another modality that is unaffected by acoustic noise, such as video information. Currently the most successful approaches for such audiovisual ASR systems, coupled hidden Markov models (HMMs) and turbo decoding, both allow for slight asynchrony between audio and video features, and significantly improve recognition rates in this way. However, both typically still neglect residual errors in the estimation of audio features, so-called observation uncertainties. This paper compares two strategies for adding these observation uncertainties into the decoder, and shows that significant recognition rate improvements are achievable for both coupled HMMs and turbo decoding
A Review of Audio-Visual Speech Recognition
Speech is the most important tool of interaction among human beings. This has inspired researchers to study further on speech recognition and develop a computer system that is able to integrate and understand human speech. But acoustic noisy environment can highly contaminate audio speech and affect the overall recognition performance. Thus, Audio-Visual Speech Recognition (AVSR) is designed to overcome the problems by utilising visual images which are unaffected by noise. The aim of this paper is to discuss the AVSR structures, which includes the front end processes, audio-visual data corpus used, recent works and accuracy estimation methods
The listening talker: A review of human and algorithmic context-induced modifications of speech
International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output
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
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization
Automatic speech recognition (ASR) has recently become an important challenge
when using deep learning (DL). It requires large-scale training datasets and
high computational and storage resources. Moreover, DL techniques and machine
learning (ML) approaches in general, hypothesize that training and testing data
come from the same domain, with the same input feature space and data
distribution characteristics. This assumption, however, is not applicable in
some real-world artificial intelligence (AI) applications. Moreover, there are
situations where gathering real data is challenging, expensive, or rarely
occurring, which can not meet the data requirements of DL models. deep transfer
learning (DTL) has been introduced to overcome these issues, which helps
develop high-performing models using real datasets that are small or slightly
different but related to the training data. This paper presents a comprehensive
survey of DTL-based ASR frameworks to shed light on the latest developments and
helps academics and professionals understand current challenges. Specifically,
after presenting the DTL background, a well-designed taxonomy is adopted to
inform the state-of-the-art. A critical analysis is then conducted to identify
the limitations and advantages of each framework. Moving on, a comparative
study is introduced to highlight the current challenges before deriving
opportunities for future research
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