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On-device mobile speech recognition
Despite many years of research, Speech Recognition remains an active area of research in Artificial Intelligence. Currently, the most common commercial application of this technology on mobile devices uses a wireless client – server approach to meet the computational and memory demands of the speech recognition process. Unfortunately, such an approach is unlikely to remain viable when fully applied over the approximately 7.22 Billion mobile phones currently in circulation. In this thesis we present an On – Device Speech recognition system. Such a system has the potential to completely eliminate the wireless client-server bottleneck. For the Voice Activity Detection part of this work, this thesis presents two novel algorithms used to detect speech activity within an audio signal. The first algorithm is based on the Log Linear Predictive Cepstral Coefficients Residual signal. These LLPCCRS feature vectors were then classified into voice signal and non-voice signal segments using a modified K-means clustering algorithm. This VAD algorithm is shown to provide a better performance as compared to a conventional energy frame analysis based approach. The second algorithm developed is based on the Linear Predictive Cepstral Coefficients. This algorithm uses the frames within the speech signal with the minimum and maximum standard deviation, as candidates for a linear cross correlation against the rest of the frames within the audio signal. The cross correlated frames are then classified using the same modified K-means clustering algorithm. The resulting output provides a cluster for Speech frames and another cluster for Non–speech frames. This novel application of the linear cross correlation technique to linear predictive cepstral coefficients feature vectors provides a fast computation method for use on the mobile platform; as shown by the results presented in this thesis. The Speech recognition part of this thesis presents two novel Neural Network approaches to mobile Speech recognition. Firstly, a recurrent neural networks architecture is developed to accommodate the output of the VAD stage. Specifically, an Echo State Network (ESN) is used for phoneme level recognition. The drawbacks and advantages of this method are explained further within the thesis. Secondly, a dynamic Multi-Layer Perceptron approach is developed. This builds on the drawbacks of the ESN and provides a dynamic way of handling speech signal length variabilities within its architecture. This novel Dynamic Multi-Layer Perceptron uses both the Linear Predictive Cepstral Coefficients (LPC) and the Mel Frequency Cepstral Coefficients (MFCC) as input features. A speaker dependent approach is presented using the Centre for spoken Language and Understanding (CSLU) database. The results show a very distinct behaviour from conventional speech recognition approaches because the LPC shows performance figures very close to the MFCC. A speaker independent system, using the standard TIMIT dataset, is then implemented on the dynamic MLP for further confirmation of this. In this mode of operation the MFCC outperforms the LPC. Finally, all the results, with emphasis on the computation time of both these novel neural network approaches are compared directly to a conventional hidden Markov model on the CSLU and TIMIT standard datasets
Integrating user-centred design in the development of a silent speech interface based on permanent magnetic articulography
Abstract: A new wearable silent speech interface (SSI) based on Permanent Magnetic Articulography (PMA) was developed with the involvement of end users in the design process. Hence, desirable features such as appearance, port-ability, ease of use and light weight were integrated into the prototype. The aim of this paper is to address the challenges faced and the design considerations addressed during the development. Evaluation on both hardware and speech recognition performances are presented here. The new prototype shows a com-parable performance with its predecessor in terms of speech recognition accuracy (i.e. ~95% of word accuracy and ~75% of sequence accuracy), but significantly improved appearance, portability and hardware features in terms of min-iaturization and cost
Human-centred artificial intelligence for mobile health sensing:challenges and opportunities
Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions
Efficient Service for Next Generation Network Slicing Architecture and Mobile Traffic Analysis Using Machine Learning Technique
The tremendous growth of mobile devices, IOT devices, applications and many other services have placed high demand on mobile and wireless network infrastructures. Much research and development of 5G mobile networks have found the way to support the huge volume of traffic, extracting of fine-gained analytics and agile management of mobile network elements, so that it can maximize the user experience. It is very challenging to accomplish the tasks as mobile networks increase the complexity, due to increases in the high volume of data penetration, devices, and applications. One of the solutions, advance machine learning techniques, can help to mitigate the large number of data and algorithm driven applications. This work mainly focus on extensive analysis of mobile traffic for improving the performance, key performance indicators and quality of service from the operations perspective. The work includes the collection of datasets and log files using different kind of tools in different network layers and implementing the machine learning techniques to analyze the datasets to predict mobile traffic activity. A wide range of algorithms were implemented to compare the analysis in order to identify the highest performance. Moreover, this thesis also discusses about network slicing architecture its use cases and how to efficiently use network slicing to meet distinct demands
SPEECH RECOGNITION FOR CONNECTED WORD USING CEPSTRAL AND DYNAMIC TIME WARPING ALGORITHMS
Speech Recognition or Speech Recognizer (SR) has become an important tool for people with physical disabilities when handling Home Automation (HA) appliances. This technology is expected to improve the daily life of the elderly and the disabled so that they are always in control over their lives, and continue to live independently, to learn and stay involved in social life. The goal of the research is to solve the constraints of current Malay SR that is still in its infancy stage where there is limited research in Malay words, especially for HA applications. Since, most of the previous works were confined to wired microphone; this limitation of using wireless microphone type makes it an important area of the research. Research was carried out to develop SR word model for five (5) Malay words and five (5) English words as commands to activate and deactivate home appliances
Speech Recognition
Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes
A survey on touch dynamics authentication in mobile devices
© 2016 Elsevier Ltd. All rights reserved. There have been research activities in the area of keystroke dynamics biometrics on physical keyboards (desktop computers or conventional mobile phones) undertaken in the past three decades. However, in terms of touch dynamics biometrics on virtual keyboards (modern touchscreen mobile devices), there has been little published work. Particularly, there is a lack of an extensive survey and evaluation of the methodologies adopted in the area. Owing to the widespread use of touchscreen mobile devices, it is necessary for us to examine the techniques and their effectiveness in the domain of touch dynamics biometrics. The aim of this paper is to provide some insights and comparative analysis of the current state of the art in the topic area, including data acquisition protocols, feature data representations, decision making techniques, as well as experimental settings and evaluations. With such a survey, we can gain a better understanding of the current state of the art, thus identifying challenging issues and knowledge gaps for further research
Deep Spoken Keyword Spotting:An Overview
Spoken keyword spotting (KWS) deals with the identification of keywords in
audio streams and has become a fast-growing technology thanks to the paradigm
shift introduced by deep learning a few years ago. This has allowed the rapid
embedding of deep KWS in a myriad of small electronic devices with different
purposes like the activation of voice assistants. Prospects suggest a sustained
growth in terms of social use of this technology. Thus, it is not surprising
that deep KWS has become a hot research topic among speech scientists, who
constantly look for KWS performance improvement and computational complexity
reduction. This context motivates this paper, in which we conduct a literature
review into deep spoken KWS to assist practitioners and researchers who are
interested in this technology. Specifically, this overview has a comprehensive
nature by covering a thorough analysis of deep KWS systems (which includes
speech features, acoustic modeling and posterior handling), robustness methods,
applications, datasets, evaluation metrics, performance of deep KWS systems and
audio-visual KWS. The analysis performed in this paper allows us to identify a
number of directions for future research, including directions adopted from
automatic speech recognition research and directions that are unique to the
problem of spoken KWS
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