Call Routing Research Articles

A comparison of cepstral and spectral features using recurrent neural network for spoken language identification

Spoken language identification is the process of confirming labels regarding the language of an audio slice regardless of various features such as length, ambiance, duration, topic or message, age, gender, region, emotions, etc. Language identification systems are of great significance in the domain of natural language processing, more specifically multi-lingual machine translation, language recognition, and automatic routing of voice calls to particular nodes speaking or knowing a particular language. In his paper, we are comparing results based on various cepstral and spectral feature techniques such as Mel-frequency Cepstral Coefficients (MFCC), Relative spectral-perceptual linear prediction coefficients (RASTA-PLP), and spectral features (roll-off, flatness, centroid, bandwidth, and contrast) in the process of spoken language identification using Recurrent Neural Network-Long Short Term Memory (RNN-LSTM) as a procedure of sequence learning. The system or model has been implemented in six different languages, which contain Ladakhi and the five official languages of Jammu and Kashmir (Union Territory). The dataset used in experimentation consists of TV audio recordings for Kashmiri, Urdu, Dogri, and Ladakhi languages. It also consists of standard corpora IIIT-H and VoxForge containing English and Hindi audio data. Pre-processing of the dataset is done by slicing different types of noise with the use of the Spectral Noise Gate (SNG) and then slicing into audio bursts of 5 seconds duration. The performance is evaluated using standard metrics like F1 score, recall, precision, and accuracy. The experimental results showed that using spectral features, MFCC and RASTA-PLP achieved an average accuracy of 76%, 83%, and 78%, respectively. Therefore, MFCC proved to be the most convenient feature to be exploited in language identification using a recurrent neural network long short-term memory classifier.

Call graph obfuscation and diversification: an approach

Monetary loss due to software piracy nowadays reaches millions. In 2017, the commercial value for this concept rose to $46.3 billion. A way to mitigate this problem from the technological point of view is the use of software protection techniques, especially the obfuscation and diversification of code, highlighting the control obfuscation. There are many proposals connected with obfuscating control flow graph. However, there are few reported works that perform obfuscation of the call graph. In this study, the authors propose a novel mechanism for the static obfuscation and diversification of the call graph of a software. The mechanism is based on the routing of functions calls in order to modify the software call graph. A prototype of the proposed mechanism was developed by extending the functionalities of a compiler. The generated software differed structurally by 25% on average, compared to the original software. There was an increase in the level of obfuscation from 2 to 30% in the tests performed, with only a 3% overhead of the execution time in all cases. The proposal allows to restructure the whole call graph efficiently, increasing the level of protection without affecting significantly the software performance.

Analysis of Call Routing Rules for Improving Call Centre Operations in Nigeria

Analysis of Call Routing Rules for Improving Call Centre Operations in Nigeria

Feature Selection for Natural Language Call Routing Based on Self-Adaptive Genetic Algorithm

The text classification problem for natural language call routing was considered in the paper. Seven different term weighting methods were applied. As dimensionality reduction methods, the feature selection based on self-adaptive GA is considered. k-NN, linear SVM and ANN were used as classification algorithms. The tasks of the research are the following: perform research of text classification for natural language call routing with different term weighting methods and classification algorithms and investigate the feature selection method based on self-adaptive GA. The numerical results showed that the most effective term weighting is TRR. The most effective classification algorithm is ANN. Feature selection with self-adaptive GA provides improvement of classification effectiveness and significant dimensionality reduction with all term weighting methods and with all classification algorithms.

Topic Categorization Based on Collectives of TermWeighting Methods for Natural Language Call Routing

Received 26.12.2015, received in revised form 11.01.2016, accepted 20.02.2016 Natural language call routing is an important data analysis problem which can be applied in different domains including airspace industry. This paper presents the investigation of collectives of term weighting methods for natural language call routing based on text classification. The main idea is that collectives of different term weighting methods can provide classification effectiveness improvement with the same classification algorithm. Seven different unsupervised and supervised term weighting methods were tested and compared with each other for classification with k-NN. After that different combinations of term weighting methods were formed as collectives. Two approaches for the handling of the collectives were considered: the meta-classifier based on the rule induction and the majority vote procedure. The numerical experiments have shown that the best result is provided with the vote of all seven different term weighting methods. This combination provides a significant increasing of classification effectiveness in comparison with the most effective term weighting methods.

Towards Building an Intelligent Call Routing System

This paper presents EduICR - an Intelligent Call Routing system. This system can route calls to the most appropriate agent using routing rules built by the text classifier. EduICR includes the following main components: telephone communication network; Vietnamese speech recognition; Text classifier/ Natural language processor and Vietnamese speech synthesis. To our best knowledge, this is one of the first systems in Vietnam to implement the integration mechanism of text processing and speech processing. This allows voice applications to be more intelligent, able to communicate with humans in natural language with high accuracy and reasonable speed. Having been built and tested in real environment, our system proves its accuracy attaining more than 95%.

Balanced routing of random calls

We consider an online network routing problem in continuous time, where calls have Poisson arrivals and exponential durations. The first-fit dynamic alternative routing algorithm sequentially selects up to $d$ random two-link routes between the two endpoints of a call, via an intermediate node, and assigns the call to the first route with spare capacity on each link, if there is such a route. The balanced dynamic alternative routing algorithm simultaneously selects $d$ random two-link routes, and the call is accepted on a route minimising the maximum of the loads on its two links, provided neither of these two links is saturated. We determine the capacities needed for these algorithms to route calls successfully and find that the balanced algorithm requires a much smaller capacity. In order to handle such interacting random processes on networks, we develop appropriate tools such as lemmas on biased random walks.

Saving Costs using Smart Call Routing: Aligning Business and IT through Finance

This case describes the effective information technology (IT) business alignment considerations that led to the successful implementation of a telecommunication platform designed to efficiently route cell phone calls in a financial institution in Chile. The company had several branches throughout the country, including its headquarters in Santiago. This initiative was part of a Technology Cost Savings strategy generated in response to the 2008 crisis. The idea behind it was to align internal business leaders with the IT team in order to optimize these costs, realizing a more efficient telecommunication platform, and to work together, as several cost-reducing initiatives were being executed at the same time. Achievements were thoroughly reviewed by the board of directors, and thus pressure to clean up the house was high. At that time, when users called a cellular phone from a landline, the call was charged using a fee much higher than a mobile company rate. The goal of the project was naturally to optimize these charges. An automatic call routing was designed, discussed and executed with the participation of the business leaders, based on each branch's demand and projected growth. The case relates the project lifecycle: the mistakes made at the beginning, the initial platform tests and bottlenecks, the rejection and frustration of the business, and a posterior crisis control and recovery phase. IT alignment and sponsorship from the directors were crucial, as there was no way back. All these factors, plus the additional IT activities competing for business attention, made this project both a challenging and learning experience.

Performance Analysis of a Telecommunication Network using Swarm Intelligence

Intelligence, a mutant of biological swarms, exhibits numerous powerful features which are described in communication networks. With the increase in size and complexity of network, performance analysis is becoming difficult. With scalability of network, it becomes difficult to manage the route and indicate which one is the best. The intent of this paper is to find optimal path for routing of calls in a telecommunication network and achieve Load Sharing. For finding optimal path, the pheromone laying and sensing property of natural swarms is used. In this research work we present a simulated network model that uses artificial ants and simulated pheromone for finding optimal path from source to destination. Next node selection is done on the basis of local pheromone distribution and pheromone updation takes place based on the congestion encountered on the node. Simulation results using ACO mode will be compared with non-ACO mode using graphs.

Call Routing in Contact Center

Now a day companies uses the contact center to provide service to customer. One of the important challenges of today contact center solution is to align the customer to the most appropriate agent within the time constrain. Call routing is used to for the alignment of customer with agent. Call routing in the contact center also play an important role in customer relationship management. In this paper we discuss the various routing concept and we propose an advancement to the skill based routing concept called as advanced skill based routing.

Control of Admission and Routing in Loss Networks: Hybrid Dynamic Programming Equations

Call admission and routing control decisions in stochastic loss (circuit-switched) networks with semi Markovian, multi-class, call arrival and general connection time processes are formulated as optimal stochastic control problems. The resulting so-called Hybrid Dynamic Programming equation systems take the form of vectors of partial differential equations with each component associated to a distinct distribution of routed calls over the network (i.e. distinct occupation states). This framework reduces to that of a Markov Decision Process when the traffic is Poisson and the associated computational limitations are approximately those of linear programs. Examples are provided of (i) network state space constructions and controlled state transition processes, (ii) a new closed form solution for a simple network, and (iii) the analysis and illustrative numerical results for a three link network.

Constrained Minimization and Discriminative Training for Natural Language Call Routing

This paper presents a combination strategy of multiple individual routing classifiers to improve classification accuracy in natural language call routing applications. Since errors of individual classifiers in the ensemble should somehow be uncorrelated, we propose a combination strategy where the combined classifier accuracy is a function of the accuracy of individual classifiers and also the correlation between their classification errors. We show theoretically and empirically that our combination strategy, named the constrained minimization technique, has a good potential in improving the classification accuracy of single classifiers. We also show how discriminative training, more specifically the generalized probabilistic descent (GPD) algorithm, can be of benefit to further boost the performance of routing classifiers. The GPD algorithm has the potential to consider both positive and negative examples during training to minimize the classification error and increase the score separation of the correct from competing hypotheses. Some parameters become negative when using the GPD algorithm, resulting from suppressive learning not traditionally possible; important antifeatures are thus obtained. Experimental evaluation is carried on a banking call routing task and on switchboard databases with a set of 23 and 67 destinations, respectively. Results show either the GPD or constrained minimization technique outperform the accuracy of baseline classifiers by 44% when applied separately. When the constrained minimization technique is added on top of GPD, we show an additional 15% reduction in the classification error rate.

On the viscosity solutions of hybrid HJB equations arising in optimal control of loss networks

E § Abstract. Call admission and routing controls for loss (circuit-switched) networks with semi- Markovian, multi-class call arrivals and general connection durations, were formulated as optimal stochastic control problems in (12, 13). Each of the resulting so-called (network) hybrid HJB equations corresponds to a collection of coupled first-order partial differential equations for which, when it exists, the continuously differentiable value function is a solution to the associated hybrid HJB equations. In general, the smoothness of the value functions and uniqueness of the solutions to the hybrid HJB equations may not hold. In this paper, viscosity solutions to a general class of hybrid HJB equations are developed and under mild conditions it is shown that the value function is continuous and, further, any continuous value function is the unique viscosity solution to the hybrid HJB equations.

Server-switching policy for call routing in a multiskill inbound call centre

In this paper, we present a server-switching policy for the routing of calls to Customer Service Representatives (CSRs) in a multiskill inbound call centre utilising Skill-Based Routing (SBR). We have considered two types of scenarios in which a multiskill SBR call centre handling inbound calls can function: (a) with specialist agents only, and (b) with a mix of specialist and flexible agents. We have developed both these models and compared the rewards obtained from each of them. Our models and experimentations help in identifying practices that result in efficient usage of existing personnel in a call centre.

Preemption-Aware Instantaneous Request Call Routing for Networks With Book-Ahead Reservation

This paper presents a new preemption-aware quality of service (QoS) routing algorithm for instantaneous request (IR) call connections in a QoS-enabled network where resources are shared between IR and book-ahead (BA) call connections. BA reservation, which confirms the availability of resources in advance, is a highly attractive technique for time sensitive applications that require high amount of bandwidth with guaranteed QoS. One of the major concerns for the implementation of BA reservation is the need for preemption of on-going IR calls to accommodate BA calls when resource scarcity arises. Preemption disrupts service continuity of on-going calls which is considered as severely detrimental from users' perceived QoS definition found in recent studies. Existing QoS routing algorithms focus on resource conservation or load balancing as the key objective to attain in addition to guaranteed QoS. No works have yet focused on the preemption problem of on-going IR calls at routing stage in the presence of BA calls. We present a mathematical formulation to compute the preemption probability of an incoming IR call at routing stage based on the current IR and future BA load information. We propose a routing strategy by formulating a link cost function comprising of the calculated preemption probability of the incoming IR call and hop count. Simulation results confirm that QoS routing based on the proposed link cost function significantly outperforms widely recommended shortest path and widest path routing algorithms in terms of IR call preemption and blocking rate. The proposed approach also yields higher network utilization and IR effective throughput.

The carvingwidth of hypercubes

The notion of the carvingwidth of a graph was introduced by Seymour and Thomas [Call routing and the ratcatcher, Combinatorica 14 (1994) 217–241]. In this note, we show that the carvingwidth of a d-dimensional hypercube equals 2 d - 1 .

Resource allocation in communication networks using market-based agents

This work describes a system that allocates end-to-end bandwidth, in a switched meshed communications network. The solution makes use of market-based software agents that compete in a number of decentralised marketplaces to buy and sell bandwidth resources. Agents perform a distributed depth first search with decentralised markets in order to allocate routes for calls. The approach relies on a resource reservation and commit mechanism in the network. Initial results show that under a light network load, the system sets up a high percentage of calls which is comparable to the optimum value and that, under all network loads, it performs significantly better than a random strategy.

On-line routing of random calls in networks

We consider a random sequence of calls between nodes in a complete network with link capacities. Each call first tries the direct link. If that link is saturated, then the `first-fit dynamic alternative routing algorithm' sequentially selects up to d random two-link alternative routes, and assigns the call to the first route with spare capacity on each link, if there is such a route. The `balanced dynamic alternative routing algorithm' simultaneously selects d random two-link alternative routes; and the call is accepted on a route minimising the maximum of the loads on its two links, provided neither of these two links is saturated.

Modeling and analysis of hierarchical cellular networks with general distributions of call and cell residence times

We present an analytic model for the performance evaluation of hierarchical cellular systems, which can provide multiple routes for calls through overflow from one cell layer to another. Our model allows the case where both the call time and the cell residence time are generally distributed. Based on the characterization of the call time by a hyper-Erlang distribution, the Laplace transform of channel occupancy time distribution for each call type (new call, handoff call, and overflow call) is derived as a function of the Laplace transform of cell residence time. In particular, overflow calls are modeled by using a renewal process. Performance measures are derived based on the product form solution of a loss system with capacity limitation. Numerical results show that the distribution type of call time and/or cell residence time has influence on the performance measure and that the exponential case may underestimate the system performance.

Please Tell Me the Reason for Your Call: Understanding Natural Language Call Routing

(2002). Please Tell Me the Reason for Your Call: Understanding Natural Language Call Routing. EDPACS: Vol. 29, No. 9, pp. 16-17.