The process of transfer a speech signal by high confidentially and as quickly as possible through the Internet needs to develop compression and encryption technology for a speech signal, so as, to reduce its size and make it understandable to persons
not authorized to listen to.
A system was designed to encrypt the voice over Internet Protocol (VoIP) and use compression technique for the purpose of reducing the size of data and send it over the network, (A_law PCM) algorithm was used the to compress audio data. Then algorithms of Triple Data Encryption Standard (TDES) and Advanced.
Encryption Standard (AES) were applied. A new encryption algorithm was proposed based in its work on the block cipher encryption system called the Direct and Reverse algorithm, which based on three basic steps, firstly expand the initial key, secondly direct the encryption of each round in one direction, and finally substitute (Bytes) as used in the Compensation Box in AES algorithm by making it moving. In general compression ratio was calculated and it was (50%) and the results of the correlation coefficient for the proposed algorithm was compared with the results of (AES, TDES) algorithms.
Intelligent personal assistants (IPAs) such as Amazon Alexa, Google Assistant and Apple Siri extend their built-in capabilities by supporting voice apps developed by third-party developers. Sometimes the smart assistant is not able to successfully re
spond to user voice commands (aka utterances). There are many reasons including automatic speech recognition (ASR) error, natural language understanding (NLU) error, routing utterances to an irrelevant voice app or simply that the user is asking for a capability that is not supported yet. The failure to handle a voice command leads to customer frustration. In this paper, we introduce a fallback skill recommendation system to suggest a voice app to a customer for an unhandled voice command. One of the prominent challenges of developing a skill recommender system for IPAs is partial observation. To solve the partial observation problem, we propose collaborative data relabeling (CDR) method. In addition, CDR also improves the diversity of the recommended skills. We evaluate the proposed method both offline and online. The offline evaluation results show that the proposed system outperforms the baselines. The online A/B testing results show significant gain of customer experience metrics.
Paralinguistics, the non-lexical components of speech, play a crucial role in human-human interaction. Models designed to recognize paralinguistic information, particularly speech emotion and style, are difficult to train because of the limited label
ed datasets available. In this work, we present a new framework that enables a neural network to learn to extract paralinguistic attributes from speech using data that are not annotated for emotion. We assess the utility of the learned embeddings on the downstream tasks of emotion recognition and speaking style detection, demonstrating significant improvements over surface acoustic features as well as over embeddings extracted from other unsupervised approaches. Our work enables future systems to leverage the learned embedding extractor as a separate component capable of highlighting the paralinguistic components of speech.
One of the first building blocks to create a voice assistant relates to the task of tagging entities or attributes in user queries. This can be particularly challenging when entities are in the tenth of millions, as is the case of e.g. music catalogs
. Training slot tagging models at an industrial scale requires large quantities of accurately labeled user queries, which are often hard and costly to gather. On the other hand, voice assistants typically collect plenty of unlabeled queries that often remain unexploited. This paper presents a weakly-supervised methodology to label large amounts of voice query logs, enhanced with a manual filtering step. Our experimental evaluations show that slot tagging models trained on weakly-supervised data outperform models trained on hand-annotated or synthetic data, at a lower cost. Further, manual filtering of weakly-supervised data leads to a very significant reduction in Sentence Error Rate, while allowing us to drastically reduce human curation efforts from weeks to hours, with respect to hand-annotation of queries. The method is applied to successfully bootstrap a slot tagging system for a major music streaming service that currently serves several tens of thousands of daily voice queries.
Reliable tagging of Temporal Expressions (TEs, e.g., Book a table at L'Osteria for Sunday evening) is a central requirement for Voice Assistants (VAs). However, there is a dearth of resources and systems for the VA domain, since publicly-available te
mporal taggers are trained only on substantially different domains, such as news and clinical text. Since the cost of annotating large datasets is prohibitive, we investigate the trade-off between in-domain data and performance in DA-Time, a hybrid temporal tagger for the English VA domain which combines a neural architecture for robust TE recognition, with a parser-based TE normalizer. We find that transfer learning goes a long way even with as little as 25 in-domain sentences: DA-Time performs at the state of the art on the news domain, and substantially outperforms it on the VA domain.
Internet of Things plays a key role in our lives today from managing airport passenger traffic, smart houses and cities to taking care of the elderly, it aims to improve life in all areas, and the technological development we are seeing has contribut
ed to a wide spread in many domains.
Platforms are the supporting software that connects everything within the Internet of things system. The platform facilitates communication, data flow, device management, and application functionality.
The Thinger.io platform is an easy-to-use platform that provides a variety of services to users. The platform enables communication of various types of devices and chipsets.
The idea was to create a personal assistant that works via voice commands to control devices connected to the Thinger.io platform remotely over the Internet in real time, The aim of adding this possibility to the platform is to make it simpler to allow anyone of any age or experience to use it to facilitate their life the way they choose, whereas The Vinus Assistant - as we called it - has the flexibility, reliability and functionality to deal with any application.
In this paper, we assess the Voice Over Internet Protocol
performance by comparing the performance of two protocols
used in VOIP such as SIP and H.323. Moreover, we evaluate
the quality indicators such as delay and packets loss. For this
purpose
OPNET simulator is used as suitable simulation
technology.
Deconstruction tries to disrupt the transcendental signification and its domination on
the other implications by introducing a new concept: the concept of free play of the
marque. This concept is based on the philosophy of the otherness and differe
nce. Derrida
never stooped to remind that the difference is not a concept or an idea or a term. We see
the difference as Law, a Law of reading and writing. But it's also the Law of the Other and
the different. It reflectsa philosophy of otherness that is rooted in the writings of Derrida.
Derrida expressed this Law by a formulation that reduces deconstruction as a philosophy
and as a method: tout autre est tout autre, a sentence that says the identity and the
otherness together. It says the identity, and says the other, each other, the other in his
irreducible plurality, the other "is" different and we cannot tell his identity. Peerless
formulation of the Law that says the impossible.
In this paper, the text-independent method of person voice
identification based on the features extraction from speech signal
that characterize the linear prediction of the behavior of the
autocorrelation function of the voice signal cepstrum are
considered
and developed.
The desire to improve the overall performance of IP network leads to designing new
QoS achitectures. The new Enhancement in providing quality of service (QoS) on the
Internet is based on the Different Services (DiffServ). DiffServ divides traffic i
nto small
classes and allocates network resources on a per-class basis. In this architecture, packets
are marked with different DiffServ code points (DSCP) at edge routers, and the priority for
packets is given via the value of this field. On the other hand, MPLS is a fast forwarding
mechanism that depends on Label's. The main advantage of MPLS is its support for traffic
engineering which results in best utilization of network's resources like link capacity. The
integration of using MPLS (as a forwarding mechanism) with DiffServ (as a QoS
mechanism) offer high Quality of service especially for real time applications (such as
VoIP, Video Conference).
We evaluate the performance of MPLS-DiffServ networks in our research. We study
QoS metrics as delay, variation of delay, upload response time, link utilization, packet loss
for several kinds of traffic (Voice, Video, FTP) by using OPNET Simulator and we
showed its superiority over MPLS network and pure IP ones. We compare our results with
MPLS networks and pure IP ones. Our results showed superiority of MPLS-DiffServ over
other kinds of networks. This is clear in decreasing delay ,delay variation, upload response
time, queuing delay, reduction of lost packets and best utilization of link capacity.
