70 research outputs found
Blind dereverberation of speech from moving and stationary speakers using sequential Monte Carlo methods
Speech signals radiated in confined spaces are subject to reverberation due to reflections
of surrounding walls and obstacles. Reverberation leads to severe degradation
of speech intelligibility and can be prohibitive for applications where speech is digitally
recorded, such as audio conferencing or hearing aids. Dereverberation of speech
is therefore an important field in speech enhancement.
Driven by consumer demand, blind speech dereverberation has become a popular
field in the research community and has led to many interesting approaches in the literature.
However, most existing methods are dictated by their underlying models and
hence suffer from assumptions that constrain the approaches to specific subproblems
of blind speech dereverberation. For example, many approaches limit the dereverberation
to voiced speech sounds, leading to poor results for unvoiced speech. Few
approaches tackle single-sensor blind speech dereverberation, and only a very limited
subset allows for dereverberation of speech from moving speakers.
Therefore, the aim of this dissertation is the development of a flexible and extendible
framework for blind speech dereverberation accommodating different speech
sound types, single- or multiple sensor as well as stationary and moving speakers.
Bayesian methods benefit from – rather than being dictated by – appropriate model
choices. Therefore, the problem of blind speech dereverberation is considered from
a Bayesian perspective in this thesis. A generic sequential Monte Carlo approach
accommodating a multitude of models for the speech production mechanism and
room transfer function is consequently derived. In this approach both the anechoic
source signal and reverberant channel are estimated using their optimal estimators by
means of Rao-Blackwellisation of the state-space of unknown variables. The remaining
model parameters are estimated using sequential importance resampling.
The proposed approach is implemented for two different speech production models
for stationary speakers, demonstrating substantial reduction in reverberation for
both unvoiced and voiced speech sounds. Furthermore, the channel model is extended
to facilitate blind dereverberation of speech from moving speakers. Due to the
structure of measurement model, single- as well as multi-microphone processing is facilitated,
accommodating physically constrained scenarios where only a single sensor
can be used as well as allowing for the exploitation of spatial diversity in scenarios
where the physical size of microphone arrays is of no concern.
This dissertation is concluded with a survey of possible directions for future research,
including the use of switching Markov source models, joint target tracking
and enhancement, as well as an extension to subband processing for improved computational
efficiency
Glottal-synchronous speech processing
Glottal-synchronous speech processing is a field of speech science where the pseudoperiodicity
of voiced speech is exploited. Traditionally, speech processing involves segmenting
and processing short speech frames of predefined length; this may fail to exploit the inherent
periodic structure of voiced speech which glottal-synchronous speech frames have
the potential to harness. Glottal-synchronous frames are often derived from the glottal
closure instants (GCIs) and glottal opening instants (GOIs).
The SIGMA algorithm was developed for the detection of GCIs and GOIs from
the Electroglottograph signal with a measured accuracy of up to 99.59%. For GCI and
GOI detection from speech signals, the YAGA algorithm provides a measured accuracy
of up to 99.84%. Multichannel speech-based approaches are shown to be more robust to
reverberation than single-channel algorithms.
The GCIs are applied to real-world applications including speech dereverberation,
where SNR is improved by up to 5 dB, and to prosodic manipulation where the importance
of voicing detection in glottal-synchronous algorithms is demonstrated by subjective
testing. The GCIs are further exploited in a new area of data-driven speech modelling,
providing new insights into speech production and a set of tools to aid deployment into
real-world applications. The technique is shown to be applicable in areas of speech coding,
identification and artificial bandwidth extension of telephone speec
Models and Analysis of Vocal Emissions for Biomedical Applications
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
Models and Analysis of Vocal Emissions for Biomedical Applications
The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the newborn to the adult and elderly. Over the years the initial issues have grown and spread also in other fields of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years in Firenze, Italy. This edition celebrates twenty-two years of uninterrupted and successful research in the field of voice analysis
ARTICULATORY INFORMATION FOR ROBUST SPEECH RECOGNITION
Current Automatic Speech Recognition (ASR) systems fail to perform nearly as good as human speech recognition performance due to their lack of robustness against speech variability and noise contamination. The goal of this dissertation is to investigate these critical robustness issues, put forth different ways to address them and finally present an ASR architecture based upon these robustness criteria.
Acoustic variations adversely affect the performance of current phone-based ASR systems, in which speech is modeled as `beads-on-a-string', where the beads are the individual phone units. While phone units are distinctive in cognitive domain, they are varying in the physical domain and their variation occurs due to a combination of factors including speech style, speaking rate etc.; a phenomenon commonly known as `coarticulation'. Traditional ASR systems address such coarticulatory variations by using contextualized phone-units such as triphones. Articulatory phonology accounts for coarticulatory variations by modeling speech as a constellation of constricting actions known as articulatory gestures. In such a framework, speech variations such as coarticulation and lenition are accounted for by gestural overlap in time and gestural reduction in space. To realize a gesture-based ASR system, articulatory gestures have to be inferred from the acoustic signal. At the initial stage of this research an initial study was performed using synthetically generated speech to obtain a proof-of-concept that articulatory gestures can indeed be recognized from the speech signal. It was observed that having vocal tract constriction trajectories (TVs) as intermediate representation facilitated the gesture recognition task from the speech signal.
Presently no natural speech database contains articulatory gesture annotation; hence an automated iterative time-warping architecture is proposed that can annotate any natural speech database with articulatory gestures and TVs. Two natural speech databases: X-ray microbeam and Aurora-2 were annotated, where the former was used to train a TV-estimator and the latter was used to train a Dynamic Bayesian Network (DBN) based ASR architecture. The DBN architecture used two sets of observation: (a) acoustic features in the form of mel-frequency cepstral coefficients (MFCCs) and (b) TVs (estimated from the acoustic speech signal). In this setup the articulatory gestures were modeled as hidden random variables, hence eliminating the necessity for explicit gesture recognition. Word recognition results using the DBN architecture indicate that articulatory representations not only can help to account for coarticulatory variations but can also significantly improve the noise robustness of ASR system
Models and Analysis of Vocal Emissions for Biomedical Applications
The Models and Analysis of Vocal Emissions with Biomedical Applications (MAVEBA) workshop came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy
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