4,649 research outputs found
Attention-Inspired Artificial Neural Networks for Speech Processing: A Systematic Review
Artificial Neural Networks (ANNs) were created inspired by the neural networks in the human brain and have been widely applied in speech processing. The application areas of ANN include: Speech recognition, speech emotion recognition, language identification, speech enhancement, and speech separation, amongst others. Likewise, given that speech processing performed by humans involves complex cognitive processes known as auditory attention, there has been a growing amount of papers proposing ANNs supported by deep learning algorithms in conjunction with some mechanism to achieve symmetry with the human attention process. However, while these ANN approaches include attention, there is no categorization of attention integrated into the deep learning algorithms and their relation with human auditory attention. Therefore, we consider it necessary to have a review of the different ANN approaches inspired in attention to show both academic and industry experts the available models for a wide variety of applications. Based on the PRISMA methodology, we present a systematic review of the literature published since 2000, in which deep learning algorithms are applied to diverse problems related to speech processing. In this paper 133 research works are selected and the following aspects are described: (i) Most relevant features, (ii) ways in which attention has been implemented, (iii) their hypothetical relationship with human attention, and (iv) the evaluation metrics used. Additionally, the four publications most related with human attention were analyzed and their strengths and weaknesses were determined
Single-Microphone Speech Enhancement and Separation Using Deep Learning
The cocktail party problem comprises the challenging task of understanding a
speech signal in a complex acoustic environment, where multiple speakers and
background noise signals simultaneously interfere with the speech signal of
interest. A signal processing algorithm that can effectively increase the
speech intelligibility and quality of speech signals in such complicated
acoustic situations is highly desirable. Especially for applications involving
mobile communication devices and hearing assistive devices. Due to the
re-emergence of machine learning techniques, today, known as deep learning, the
challenges involved with such algorithms might be overcome. In this PhD thesis,
we study and develop deep learning-based techniques for two sub-disciplines of
the cocktail party problem: single-microphone speech enhancement and
single-microphone multi-talker speech separation. Specifically, we conduct
in-depth empirical analysis of the generalizability capability of modern deep
learning-based single-microphone speech enhancement algorithms. We show that
performance of such algorithms is closely linked to the training data, and good
generalizability can be achieved with carefully designed training data.
Furthermore, we propose uPIT, a deep learning-based algorithm for
single-microphone speech separation and we report state-of-the-art results on a
speaker-independent multi-talker speech separation task. Additionally, we show
that uPIT works well for joint speech separation and enhancement without
explicit prior knowledge about the noise type or number of speakers. Finally,
we show that deep learning-based speech enhancement algorithms designed to
minimize the classical short-time spectral amplitude mean squared error leads
to enhanced speech signals which are essentially optimal in terms of STOI, a
state-of-the-art speech intelligibility estimator.Comment: PhD Thesis. 233 page
Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems
Speaker adaptation techniques provide a powerful solution to customise
automatic speech recognition (ASR) systems for individual users. Practical
application of unsupervised model-based speaker adaptation techniques to data
intensive end-to-end ASR systems is hindered by the scarcity of speaker-level
data and performance sensitivity to transcription errors. To address these
issues, a set of compact and data efficient speaker-dependent (SD) parameter
representations are used to facilitate both speaker adaptive training and
test-time unsupervised speaker adaptation of state-of-the-art Conformer ASR
systems. The sensitivity to supervision quality is reduced using a confidence
score-based selection of the less erroneous subset of speaker-level adaptation
data. Two lightweight confidence score estimation modules are proposed to
produce more reliable confidence scores. The data sparsity issue, which is
exacerbated by data selection, is addressed by modelling the SD parameter
uncertainty using Bayesian learning. Experiments on the benchmark 300-hour
Switchboard and the 233-hour AMI datasets suggest that the proposed confidence
score-based adaptation schemes consistently outperformed the baseline
speaker-independent (SI) Conformer model and conventional non-Bayesian, point
estimate-based adaptation using no speaker data selection. Similar consistent
performance improvements were retained after external Transformer and LSTM
language model rescoring. In particular, on the 300-hour Switchboard corpus,
statistically significant WER reductions of 1.0%, 1.3%, and 1.4% absolute
(9.5%, 10.9%, and 11.3% relative) were obtained over the baseline SI Conformer
on the NIST Hub5'00, RT02, and RT03 evaluation sets respectively. Similar WER
reductions of 2.7% and 3.3% absolute (8.9% and 10.2% relative) were also
obtained on the AMI development and evaluation sets.Comment: IEEE/ACM Transactions on Audio, Speech, and Language Processin
Bio-motivated features and deep learning for robust speech recognition
Mención Internacional en el tÃtulo de doctorIn spite of the enormous leap forward that the Automatic Speech
Recognition (ASR) technologies has experienced over the last five years
their performance under hard environmental condition is still far from
that of humans preventing their adoption in several real applications.
In this thesis the challenge of robustness of modern automatic speech
recognition systems is addressed following two main research lines.
The first one focuses on modeling the human auditory system to
improve the robustness of the feature extraction stage yielding to novel
auditory motivated features. Two main contributions are produced.
On the one hand, a model of the masking behaviour of the Human
Auditory System (HAS) is introduced, based on the non-linear filtering
of a speech spectro-temporal representation applied simultaneously
to both frequency and time domains. This filtering is accomplished
by using image processing techniques, in particular mathematical
morphology operations with an specifically designed Structuring Element
(SE) that closely resembles the masking phenomena that take
place in the cochlea. On the other hand, the temporal patterns of
auditory-nerve firings are modeled. Most conventional acoustic features
are based on short-time energy per frequency band discarding
the information contained in the temporal patterns. Our contribution
is the design of several types of feature extraction schemes based on
the synchrony effect of auditory-nerve activity, showing that the modeling
of this effect can indeed improve speech recognition accuracy in
the presence of additive noise. Both models are further integrated into
the well known Power Normalized Cepstral Coefficients (PNCC).
The second research line addresses the problem of robustness in
noisy environments by means of the use of Deep Neural Networks
(DNNs)-based acoustic modeling and, in particular, of Convolutional
Neural Networks (CNNs) architectures. A deep residual network
scheme is proposed and adapted for our purposes, allowing Residual
Networks (ResNets), originally intended for image processing tasks,
to be used in speech recognition where the network input is small
in comparison with usual image dimensions. We have observed that
ResNets on their own already enhance the robustness of the whole system
against noisy conditions. Moreover, our experiments demonstrate
that their combination with the auditory motivated features devised
in this thesis provide significant improvements in recognition accuracy
in comparison to other state-of-the-art CNN-based ASR systems
under mismatched conditions, while maintaining the performance in
matched scenarios.
The proposed methods have been thoroughly tested and compared
with other state-of-the-art proposals for a variety of datasets and
conditions. The obtained results prove that our methods outperform
other state-of-the-art approaches and reveal that they are suitable for
practical applications, specially where the operating conditions are
unknown.El objetivo de esta tesis se centra en proponer soluciones al problema
del reconocimiento de habla robusto; por ello, se han llevado a cabo
dos lÃneas de investigación.
En la primera lÃınea se han propuesto esquemas de extracción de caracterÃsticas novedosos, basados en el modelado del comportamiento
del sistema auditivo humano, modelando especialmente los fenómenos
de enmascaramiento y sincronÃa. En la segunda, se propone mejorar
las tasas de reconocimiento mediante el uso de técnicas de
aprendizaje profundo, en conjunto con las caracterÃsticas propuestas.
Los métodos propuestos tienen como principal objetivo, mejorar la
precisión del sistema de reconocimiento cuando las condiciones de
operación no son conocidas, aunque el caso contrario también ha sido
abordado.
En concreto, nuestras principales propuestas son los siguientes:
Simular el sistema auditivo humano con el objetivo de mejorar
la tasa de reconocimiento en condiciones difÃciles, principalmente
en situaciones de alto ruido, proponiendo esquemas de
extracción de caracterÃsticas novedosos.
Siguiendo esta dirección, nuestras principales propuestas se detallan a continuación:
• Modelar el comportamiento de enmascaramiento del sistema
auditivo humano, usando técnicas del procesado de
imagen sobre el espectro, en concreto, llevando a cabo el
diseño de un filtro morfológico que captura este efecto.
• Modelar el efecto de la sincronà que tiene lugar en el nervio
auditivo.
• La integración de ambos modelos en los conocidos Power
Normalized Cepstral Coefficients (PNCC).
La aplicación de técnicas de aprendizaje profundo con el objetivo
de hacer el sistema más robusto frente al ruido, en particular
con el uso de redes neuronales convolucionales profundas, como
pueden ser las redes residuales.
Por último, la aplicación de las caracterÃsticas propuestas en
combinación con las redes neuronales profundas, con el objetivo
principal de obtener mejoras significativas, cuando las condiciones
de entrenamiento y test no coinciden.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Javier Ferreiros López.- Secretario: Fernando DÃaz de MarÃa.- Vocal: Rubén Solera Ureñ
Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference
Multimodal Emotion Recognition among Couples from Lab Settings to Daily Life using Smartwatches
Couples generally manage chronic diseases together and the management takes
an emotional toll on both patients and their romantic partners. Consequently,
recognizing the emotions of each partner in daily life could provide an insight
into their emotional well-being in chronic disease management. The emotions of
partners are currently inferred in the lab and daily life using self-reports
which are not practical for continuous emotion assessment or observer reports
which are manual, time-intensive, and costly. Currently, there exists no
comprehensive overview of works on emotion recognition among couples.
Furthermore, approaches for emotion recognition among couples have (1) focused
on English-speaking couples in the U.S., (2) used data collected from the lab,
and (3) performed recognition using observer ratings rather than partner's
self-reported / subjective emotions. In this body of work contained in this
thesis (8 papers - 5 published and 3 currently under review in various
journals), we fill the current literature gap on couples' emotion recognition,
develop emotion recognition systems using 161 hours of data from a total of
1,051 individuals, and make contributions towards taking couples' emotion
recognition from the lab which is the status quo, to daily life. This thesis
contributes toward building automated emotion recognition systems that would
eventually enable partners to monitor their emotions in daily life and enable
the delivery of interventions to improve their emotional well-being.Comment: PhD Thesis, 2022 - ETH Zuric
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