84,475 research outputs found
A Robust Feature Extraction with Dual Fusion aided Extreme Learning for Audio–Visual Hindi Speech Recognition
383-386In Automatic Speech Recognition (ASR) based system implementation, robustness to several noisy background situation is a unique challenge. In this paper, for estimating both audio and visual aspect feature in light of different information representation perspectives directs to the robust feature extraction from audio-visual speech image. Further, the authors obtain the bottleneck features from the bottleneck layer of the bottleneck deep neural network (BN-DNN). Further, a familiar powerful texture descriptor of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) is applied to obtain the visual related features from the face region. Moreover, the categorization is executed utilizing the help of Extreme Learning Machine (ELM) and to reach the global optimum through Jaya optimization algorithm for audio-visual Hindi speech recognition. The proposed scheme is evaluated in MATLAB platform and the implementation is equated with the existing audio-visual speech recognition (AVSR) approaches
Deep spiking neural networks with applications to human gesture recognition
The spiking neural networks (SNNs), as the 3rd generation of Artificial Neural Networks (ANNs), are a class of event-driven neuromorphic algorithms that potentially have a wide range of application domains and are applicable to a variety of extremely low power neuromorphic hardware. The work presented in this thesis addresses the challenges of human gesture recognition using novel SNN algorithms. It discusses the design of these algorithms for both visual and auditory domain human gesture recognition as well as event-based pre-processing toolkits for audio signals.
From the visual gesture recognition aspect, a novel SNN-based event-driven hand gesture recognition system is proposed. This system is shown to be effective in an experiment on hand gesture recognition with its spiking recurrent convolutional neural network (SCRNN) design, which combines both designed convolution operation and recurrent connectivity to maintain spatial and temporal relations with address-event-representation (AER) data. The proposed SCRNN architecture can achieve arbitrary temporal resolution, which means it can exploit temporal correlations between event collections. This design utilises a backpropagation-based training algorithm and does not suffer from gradient vanishing/explosion problems.
From the audio perspective, a novel end-to-end spiking speech emotion recognition system (SER) is proposed. This system employs the MFCC as its main speech feature extractor as well as a self-designed latency coding algorithm to effciently convert the raw signal to AER input that can be used for SNN. A two-layer spiking recurrent architecture is proposed to address temporal correlations between spike trains. The robustness of this system is supported by several open public datasets, which demonstrate state of the arts recognition accuracy and a significant reduction in network size, computational costs, and training speed.
In addition to directly contributing to neuromorphic SER, this thesis proposes a novel speech-coding algorithm based on the working mechanism of humans auditory organ system. The algorithm mimics the functionality of the cochlea and successfully provides an alternative method of event-data acquisition for audio-based data. The algorithm is then further simplified and extended into an application of speech enhancement which is jointly used in the proposed SER system. This speech-enhancement method uses the lateral inhibition mechanism as a frequency coincidence detector to remove uncorrelated noise in the time-frequency spectrum. The method is shown to be effective by experiments for up to six types of noise.The spiking neural networks (SNNs), as the 3rd generation of Artificial Neural Networks (ANNs), are a class of event-driven neuromorphic algorithms that potentially have a wide range of application domains and are applicable to a variety of extremely low power neuromorphic hardware. The work presented in this thesis addresses the challenges of human gesture recognition using novel SNN algorithms. It discusses the design of these algorithms for both visual and auditory domain human gesture recognition as well as event-based pre-processing toolkits for audio signals.
From the visual gesture recognition aspect, a novel SNN-based event-driven hand gesture recognition system is proposed. This system is shown to be effective in an experiment on hand gesture recognition with its spiking recurrent convolutional neural network (SCRNN) design, which combines both designed convolution operation and recurrent connectivity to maintain spatial and temporal relations with address-event-representation (AER) data. The proposed SCRNN architecture can achieve arbitrary temporal resolution, which means it can exploit temporal correlations between event collections. This design utilises a backpropagation-based training algorithm and does not suffer from gradient vanishing/explosion problems.
From the audio perspective, a novel end-to-end spiking speech emotion recognition system (SER) is proposed. This system employs the MFCC as its main speech feature extractor as well as a self-designed latency coding algorithm to effciently convert the raw signal to AER input that can be used for SNN. A two-layer spiking recurrent architecture is proposed to address temporal correlations between spike trains. The robustness of this system is supported by several open public datasets, which demonstrate state of the arts recognition accuracy and a significant reduction in network size, computational costs, and training speed.
In addition to directly contributing to neuromorphic SER, this thesis proposes a novel speech-coding algorithm based on the working mechanism of humans auditory organ system. The algorithm mimics the functionality of the cochlea and successfully provides an alternative method of event-data acquisition for audio-based data. The algorithm is then further simplified and extended into an application of speech enhancement which is jointly used in the proposed SER system. This speech-enhancement method uses the lateral inhibition mechanism as a frequency coincidence detector to remove uncorrelated noise in the time-frequency spectrum. The method is shown to be effective by experiments for up to six types of noise
Speech data analysis for semantic indexing of video of simulated medical crises.
The Simulation for Pediatric Assessment, Resuscitation, and Communication (SPARC) group within the Department of Pediatrics at the University of Louisville, was established to enhance the care of children by using simulation based educational methodologies to improve patient safety and strengthen clinician-patient interactions. After each simulation session, the physician must manually review and annotate the recordings and then debrief the trainees. The physician responsible for the simulation has recorded 100s of videos, and is seeking solutions that can automate the process. This dissertation introduces our developed system for efficient segmentation and semantic indexing of videos of medical simulations using machine learning methods. It provides the physician with automated tools to review important sections of the simulation by identifying who spoke, when and what was his/her emotion. Only audio information is extracted and analyzed because the quality of the image recording is low and the visual environment is static for most parts. Our proposed system includes four main components: preprocessing, speaker segmentation, speaker identification, and emotion recognition. The preprocessing consists of first extracting the audio component from the video recording. Then, extracting various low-level audio features to detect and remove silence segments. We investigate and compare two different approaches for this task. The first one is threshold-based and the second one is classification-based. The second main component of the proposed system consists of detecting speaker changing points for the purpose of segmenting the audio stream. We propose two fusion methods for this task. The speaker identification and emotion recognition components of our system are designed to provide users the capability to browse the video and retrieve shots that identify ”who spoke, when, and the speaker’s emotion” for further analysis. For this component, we propose two feature representation methods that map audio segments of arbitary length to a feature vector with fixed dimensions. The first one is based on soft bag-of-word (BoW) feature representations. In particular, we define three types of BoW that are based on crisp, fuzzy, and possibilistic voting. The second feature representation is a generalization of the BoW and is based on Fisher Vector (FV). FV uses the Fisher Kernel principle and combines the benefits of generative and discriminative approaches. The proposed feature representations are used within two learning frameworks. The first one is supervised learning and assumes that a large collection of labeled training data is available. Within this framework, we use standard classifiers including K-nearest neighbor (K-NN), support vector machine (SVM), and Naive Bayes. The second framework is based on semi-supervised learning where only a limited amount of labeled training samples are available. We use an approach that is based on label propagation. Our proposed algorithms were evaluated using 15 medical simulation sessions. The results were analyzed and compared to those obtained using state-of-the-art algorithms. We show that our proposed speech segmentation fusion algorithms and feature mappings outperform existing methods. We also integrated all proposed algorithms and developed a GUI prototype system for subjective evaluation. This prototype processes medical simulation video and provides the user with a visual summary of the different speech segments. It also allows the user to browse videos and retrieve scenes that provide answers to semantic queries such as: who spoke and when; who interrupted who? and what was the emotion of the speaker? The GUI prototype can also provide summary statistics of each simulation video. Examples include: for how long did each person spoke? What is the longest uninterrupted speech segment? Is there an unusual large number of pauses within the speech segment of a given speaker
End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models
Speech activity detection (SAD) plays an important role in current speech
processing systems, including automatic speech recognition (ASR). SAD is
particularly difficult in environments with acoustic noise. A practical
solution is to incorporate visual information, increasing the robustness of the
SAD approach. An audiovisual system has the advantage of being robust to
different speech modes (e.g., whisper speech) or background noise. Recent
advances in audiovisual speech processing using deep learning have opened
opportunities to capture in a principled way the temporal relationships between
acoustic and visual features. This study explores this idea proposing a
\emph{bimodal recurrent neural network} (BRNN) framework for SAD. The approach
models the temporal dynamic of the sequential audiovisual data, improving the
accuracy and robustness of the proposed SAD system. Instead of estimating
hand-crafted features, the study investigates an end-to-end training approach,
where acoustic and visual features are directly learned from the raw data
during training. The experimental evaluation considers a large audiovisual
corpus with over 60.8 hours of recordings, collected from 105 speakers. The
results demonstrate that the proposed framework leads to absolute improvements
up to 1.2% under practical scenarios over a VAD baseline using only audio
implemented with deep neural network (DNN). The proposed approach achieves
92.7% F1-score when it is evaluated using the sensors from a portable tablet
under noisy acoustic environment, which is only 1.0% lower than the performance
obtained under ideal conditions (e.g., clean speech obtained with a high
definition camera and a close-talking microphone).Comment: Submitted to Speech Communicatio
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Talking face generation aims to synthesize a sequence of face images that
correspond to a clip of speech. This is a challenging task because face
appearance variation and semantics of speech are coupled together in the subtle
movements of the talking face regions. Existing works either construct specific
face appearance model on specific subjects or model the transformation between
lip motion and speech. In this work, we integrate both aspects and enable
arbitrary-subject talking face generation by learning disentangled audio-visual
representation. We find that the talking face sequence is actually a
composition of both subject-related information and speech-related information.
These two spaces are then explicitly disentangled through a novel
associative-and-adversarial training process. This disentangled representation
has an advantage where both audio and video can serve as inputs for generation.
Extensive experiments show that the proposed approach generates realistic
talking face sequences on arbitrary subjects with much clearer lip motion
patterns than previous work. We also demonstrate the learned audio-visual
representation is extremely useful for the tasks of automatic lip reading and
audio-video retrieval.Comment: AAAI Conference on Artificial Intelligence (AAAI 2019) Oral
Presentation. Code, models, and video results are available on our webpage:
https://liuziwei7.github.io/projects/TalkingFace.htm
Learning weakly supervised multimodal phoneme embeddings
Recent works have explored deep architectures for learning multimodal speech
representation (e.g. audio and images, articulation and audio) in a supervised
way. Here we investigate the role of combining different speech modalities,
i.e. audio and visual information representing the lips movements, in a weakly
supervised way using Siamese networks and lexical same-different side
information. In particular, we ask whether one modality can benefit from the
other to provide a richer representation for phone recognition in a weakly
supervised setting. We introduce mono-task and multi-task methods for merging
speech and visual modalities for phone recognition. The mono-task learning
consists in applying a Siamese network on the concatenation of the two
modalities, while the multi-task learning receives several different
combinations of modalities at train time. We show that multi-task learning
enhances discriminability for visual and multimodal inputs while minimally
impacting auditory inputs. Furthermore, we present a qualitative analysis of
the obtained phone embeddings, and show that cross-modal visual input can
improve the discriminability of phonological features which are visually
discernable (rounding, open/close, labial place of articulation), resulting in
representations that are closer to abstract linguistic features than those
based on audio only
Deep Multimodal Learning for Audio-Visual Speech Recognition
In this paper, we present methods in deep multimodal learning for fusing
speech and visual modalities for Audio-Visual Automatic Speech Recognition
(AV-ASR). First, we study an approach where uni-modal deep networks are trained
separately and their final hidden layers fused to obtain a joint feature space
in which another deep network is built. While the audio network alone achieves
a phone error rate (PER) of under clean condition on the IBM large
vocabulary audio-visual studio dataset, this fusion model achieves a PER of
demonstrating the tremendous value of the visual channel in phone
classification even in audio with high signal to noise ratio. Second, we
present a new deep network architecture that uses a bilinear softmax layer to
account for class specific correlations between modalities. We show that
combining the posteriors from the bilinear networks with those from the fused
model mentioned above results in a further significant phone error rate
reduction, yielding a final PER of .Comment: ICASSP 201
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