2,531 research outputs found
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
A Systematic Study and Empirical Analysis of Lip Reading Models using Traditional and Deep Learning Algorithms
Despite the fact that there are many applications for analyzing and recreating the audio through existinglip movement recognition, the researchers have shown the interest in developing the automatic lip-readingsystems to achieve the increased performance. Modelling of the framework has been playing a major role inadvance yield of sequential framework. In recent years there have been lot of interest in Deep Neural Networks(DNN) and break through results in various domains including Image Classification, Speech Recognition andNatural Language Processing. To represents complex functions DNNs are used and also they play a vital rolein Automatic Lip Reading (ALR) systems. This paper mainly focuses on the traditional pixel, shape and mixedfeature extractions and their improved technologies for lip reading recognitions. It highlights the mostimportant techniques and progression from end-to-end deep learning architectures that were evolved duringthe past decade. The investigation points out the voice-visual databases that are used for analyzing and trainthe system with the most common words and the count of speakers and the size, length of the language andtime duration. On the flip side, ALR systems developed were compared with their old-style systems. Thestatistical analysis is performed to recognize the characters or numerals and words or sentences in English andcompared their performances
Adaptive threshold optimisation for colour-based lip segmentation in automatic lip-reading systems
A thesis submitted to the Faculty of Engineering and the Built Environment,
University of the Witwatersrand, Johannesburg, in ful lment of the requirements for
the degree of Doctor of Philosophy.
Johannesburg, September 2016Having survived the ordeal of a laryngectomy, the patient must come to terms with
the resulting loss of speech. With recent advances in portable computing power,
automatic lip-reading (ALR) may become a viable approach to voice restoration. This
thesis addresses the image processing aspect of ALR, and focuses three contributions
to colour-based lip segmentation.
The rst contribution concerns the colour transform to enhance the contrast
between the lips and skin. This thesis presents the most comprehensive study to
date by measuring the overlap between lip and skin histograms for 33 di erent
colour transforms. The hue component of HSV obtains the lowest overlap of 6:15%,
and results show that selecting the correct transform can increase the segmentation
accuracy by up to three times.
The second contribution is the development of a new lip segmentation algorithm
that utilises the best colour transforms from the comparative study. The algorithm
is tested on 895 images and achieves percentage overlap (OL) of 92:23% and segmentation
error (SE) of 7:39 %.
The third contribution focuses on the impact of the histogram threshold on the
segmentation accuracy, and introduces a novel technique called Adaptive Threshold
Optimisation (ATO) to select a better threshold value. The rst stage of ATO
incorporates -SVR to train the lip shape model. ATO then uses feedback of shape
information to validate and optimise the threshold. After applying ATO, the SE
decreases from 7:65% to 6:50%, corresponding to an absolute improvement of 1:15 pp
or relative improvement of 15:1%. While this thesis concerns lip segmentation in
particular, ATO is a threshold selection technique that can be used in various
segmentation applications.MT201
A motion-based approach for audio-visual automatic speech recognition
The research work presented in this thesis introduces novel approaches for both visual
region of interest extraction and visual feature extraction for use in audio-visual
automatic speech recognition. In particular, the speaker‘s movement that occurs
during speech is used to isolate the mouth region in video sequences and motionbased
features obtained from this region are used to provide new visual features for
audio-visual automatic speech recognition. The mouth region extraction approach
proposed in this work is shown to give superior performance compared with existing
colour-based lip segmentation methods. The new features are obtained from three
separate representations of motion in the region of interest, namely the difference in
luminance between successive images, block matching based motion vectors and
optical flow. The new visual features are found to improve visual-only and audiovisual
speech recognition performance when compared with the commonly-used
appearance feature-based methods.
In addition, a novel approach is proposed for visual feature extraction from either the
discrete cosine transform or discrete wavelet transform representations of the mouth
region of the speaker. In this work, the image transform is explored from a new
viewpoint of data discrimination; in contrast to the more conventional data
preservation viewpoint. The main findings of this work are that audio-visual
automatic speech recognition systems using the new features extracted from the
frequency bands selected according to their discriminatory abilities generally
outperform those using features designed for data preservation.
To establish the noise robustness of the new features proposed in this work, their
performance has been studied in presence of a range of different types of noise and at
various signal-to-noise ratios. In these experiments, the audio-visual automatic speech
recognition systems based on the new approaches were found to give superior
performance both to audio-visual systems using appearance based features and to
audio-only speech recognition systems
Multimodal person recognition for human-vehicle interaction
Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies
Capture, Learning, and Synthesis of 3D Speaking Styles
Audio-driven 3D facial animation has been widely explored, but achieving
realistic, human-like performance is still unsolved. This is due to the lack of
available 3D datasets, models, and standard evaluation metrics. To address
this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans
captured at 60 fps and synchronized audio from 12 speakers. We then train a
neural network on our dataset that factors identity from facial motion. The
learned model, VOCA (Voice Operated Character Animation) takes any speech
signal as input - even speech in languages other than English - and
realistically animates a wide range of adult faces. Conditioning on subject
labels during training allows the model to learn a variety of realistic
speaking styles. VOCA also provides animator controls to alter speaking style,
identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball
rotations) during animation. To our knowledge, VOCA is the only realistic 3D
facial animation model that is readily applicable to unseen subjects without
retargeting. This makes VOCA suitable for tasks like in-game video, virtual
reality avatars, or any scenario in which the speaker, speech, or language is
not known in advance. We make the dataset and model available for research
purposes at http://voca.is.tue.mpg.de.Comment: To appear in CVPR 201
Use of Coherent Point Drift in computer vision applications
This thesis presents the novel use of Coherent Point Drift in improving the robustness of a number of computer vision applications. CPD approach includes two methods for registering two images - rigid and non-rigid point set approaches which are based on the transformation model used. The key characteristic of a rigid transformation is that the distance between points is preserved, which means it can be used in the presence of translation, rotation, and scaling. Non-rigid transformations - or affine transforms - provide the opportunity of registering under non-uniform scaling and skew. The idea is to move one point set coherently to align with the second point set. The CPD method finds both the non-rigid transformation and the correspondence distance between two point sets at the same time without having to use a-priori declaration of the transformation model used.
The first part of this thesis is focused on speaker identification in video conferencing. A real-time, audio-coupled video based approach is presented, which focuses more on the video analysis side, rather than the audio analysis that is known to be prone to errors. CPD is effectively utilised for lip movement detection and a temporal face detection approach is used to minimise false positives if face detection algorithm fails to perform.
The second part of the thesis is focused on multi-exposure and multi-focus image fusion with compensation for camera shake. Scale Invariant Feature Transforms (SIFT) are first used to detect keypoints in images being fused. Subsequently this point set is reduced to remove outliers, using RANSAC (RANdom Sample Consensus) and finally the point sets are registered using CPD with non-rigid transformations. The registered images are then fused with a Contourlet based image fusion algorithm that makes use of a novel alpha blending and filtering technique to minimise artefacts. The thesis evaluates the performance of the algorithm in comparison to a number of state-of-the-art approaches, including the key commercial products available in the market at present, showing significantly improved subjective quality in the fused images.
The final part of the thesis presents a novel approach to Vehicle Make & Model Recognition in CCTV video footage. CPD is used to effectively remove skew of vehicles detected as CCTV cameras are not specifically configured for the VMMR task and may capture vehicles at different approaching angles. A LESH (Local Energy Shape Histogram) feature based approach is used for vehicle make and model recognition with the novelty that temporal processing is used to improve reliability. A number of further algorithms are used to maximise the reliability of the final outcome. Experimental results are provided to prove that the proposed system demonstrates an accuracy in excess of 95% when tested on real CCTV footage with no prior camera calibration
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