Improving automatic transcription of call center speech using data simulation

2021 ◽  
Author(s):  
Vataya Chunwijitra ◽  
Nattapong Kurpukdee
Keyword(s):  
Call Center ◽  
Data Simulation ◽  
Automatic Transcription ◽  
Using Data

scholarly journals Application of queueing models with abandonment for Call Center congestion analysis

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
Sidney Carlos Ferrari ◽  
Reinaldo Morabito
Keyword(s):  
Call Center ◽  
Call Centers ◽  
Queueing Systems ◽  
Minimum Cost ◽  
Service Organizations ◽  
Analytical Models ◽  
Queueing Models ◽  
Phone Calls ◽  
Using Data ◽  
Mixed Distributions

Abstract This paper studies and applies queueing systems to Call Centers regarding the possibility of customer abandonment from the system before being served due to their impatience in waiting for a service. Call Centers are service organizations that predominantly serve customers via phone calls. One of the main concerns in managing them is to provide quality service at a minimum cost. Noticing the quality of services offered is expressed by customers, for example by abandonment from the queue. This paper shows that the M/M/c+G analytical queueing models with abandonment, with patience time represented by generic distributions (particularly mixed distributions), are more effective than the M/M/c+M analytical queueing models with abandonment, with Exponential patience, commonly used to evaluate congestion problems in Call Centers and support sizing and operational decisions in these systems. We conducted a study using data extracted from a Bank Call Center located in Israel and the parameters and some performance measures are determined based on this data. These sampling measures are compared with the same measures achieved by the M/M/c+M and M/M/c+G analytical queueing models considered in this research, which use parameters obtained empirically and the mixed and non-mixed distributions based on Exponential and Lognormal to represent user patience. An experimental discrete simulation model was also used to explore an alternative scenario, showing the potential of using the approaches based on analytical models with abandonment for Call Center analysis.

Employee Responses to Compensation Changes: Evidence from a Sales Firm

2021 ◽  
Author(s):  
Jason Sandvik ◽  
Richard Saouma ◽  
Nathan Seegert ◽  
Christopher Stanton
Keyword(s):  
Job Performance ◽  
Call Center ◽  
Turnover Rate ◽  
Long Term Consequences ◽  
Using Data ◽  
The Cost ◽  
Employee Responses

What are the long-term consequences of compensation changes? Using data from an inbound sales call center, we study employee responses to a compensation change that ultimately reduced take-home pay by 7% for the average affected worker. The change caused a significant increase in the turnover rate of the firm’s most productive employees, but the response was relatively muted for less productive workers. On-the-job performance changes were minimal among workers who remained at the firm. We quantify the cost of losing highly productive employees and find that their heightened sensitivity to changes in compensation limits managers’ ability to adjust incentives. Our results speak to a driver of compensation rigidity and the difficulty managers face when setting compensation. This paper was accepted by Lamar Pierce, organizations.

scholarly journals Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 2930 ◽  
Author(s):  
Søren Skovsen ◽  
Mads Dyrmann ◽  
Anders Mortensen ◽  
Kim Steen ◽  
Ole Green ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Botanical Composition ◽  
Data Simulation ◽  
Fully Convolutional Neural Networks ◽  
Rgb Images ◽  
Using Data

Affectivity, emotional exhaustion, and service sabotage behavior: The mediation role of rumination

2013 ◽  
Vol 41 (4) ◽  
pp. 651-661 ◽  
Author(s):  
Ping Luo ◽  
Zhao Bao
Keyword(s):  
Theoretical Model ◽  
Emotional Exhaustion ◽  
Call Center ◽  
Negative Affectivity ◽  
Service Sabotage ◽  
Using Data ◽  
Positive And Negative Affectivity

In this study, we examined the mechanisms of rumination including its antecedents (positive and negative affectivity) and outcomes (emotional exhaustion and service sabotage behavior). Our theoretical model was tested using data collected from 751 employees in a call center in China. Results showed that positive and negative affectivity were significantly related to rumination, which is then positively related to emotional exhaustion and service sabotage behavior. Moreover, rumination significantly mediated the relationships between positive and negative affectivity, and the outcomes of rumination. Finally, emotional exhaustion was found to be positively related to service sabotage behavior.

scholarly journals Author Profiling in Informal and Formal Language Scenarios Via Transfer Learning

TecnoLógicas ◽  
2021 ◽  
Vol 24 (52) ◽  
pp. e2166
Author(s):  
Daniel Escobar-Grisales ◽  
Juan Camilo Vásquez-Correa ◽  
Juan Rafael Orozco-Arroyave
Keyword(s):  
Social Media ◽  
Transfer Learning ◽  
Call Center ◽  
Formal Language ◽  
Learning Strategy ◽  
Computer Applications ◽  
Formal Structure ◽  
Language Variety ◽  
Using Data ◽  
Author Profiling

The interest in author profiling tasks has increased in the research community because computer applications have shown success in different sectors such as security, marketing, healthcare, and others. Recognition and identification of traits such as gender, age or location based on text data can help to improve different marketing strategies. This type of technology has been widely discussed regarding documents taken from social media. However, its methods have been poorly studied using data with a more formal structure, where there is no access to emoticons, mentions, and other linguistic phenomena that are only present in social media. This paper proposes the use of recurrent and convolutional neural networks and a transfer learning strategy to recognize two demographic traits, i.e., gender and language variety, in documents written in informal and formal language. The models were tested in two different databases consisting of tweets (informal) and call-center conversations (formal). Accuracies of up to 75 % and 68 % were achieved in the recognition of gender in documents with informal and formal language, respectively. Moreover, regarding language variety recognition, accuracies of 92 % and 72 % were obtained in informal and formal text scenarios, respectively. The results indicate that, in relation to the traits considered in this paper, it is possible to transfer the knowledge from a system trained on a specific type of expressions to another one where the structure is completely different and data are scarcer.

A survival model based on met expectations

2015 ◽  
Vol 28 (2) ◽  
pp. 177-194 ◽  
Author(s):  
Mauricio A. Valle ◽  
Gonzalo A. Ruz ◽  
Samuel Varas
Keyword(s):  
Employee Turnover ◽  
Call Center ◽  
Turnover Rates ◽  
High Turnover ◽  
Content Type ◽  
Voluntary Employee Turnover ◽  
The Difference ◽  
Expectation Theory ◽  
Using Data ◽  
Met Expectations

Purpose The purpose of this paper is to propose a model of voluntary employee turnover based on the theory of met expectations and self-perceived efficacy of the employee, using data from a field survey conducted in a call center. Design/methodology/approach The paper formulates a model of employee turnover. First explaining the fulfillment of expectations from initial expectations of the employee (before starting work) and their experience after a period of time. Second, explaining the turnover of employees from the fulfillment of their expectations. Findings Some of the variability in the fulfillment of expectations can be explained by the difference between expectations and experiences in different job dimensions (e.g. income levels and job recognition). Results show that the level of fulfillment of expectations helps explain the process of employee turnover. Research limitations/implications This work provides evidence for the met expectation theory, where the gap between the individual’s expectations and subsequent experiences lead to abandonment behaviors in the organization. Practical implications The results suggest two paths of action to reduce the high turnover rates in the call center: the first, through realistic expectations setting of the employee, and the second, with a constant monitoring of the fulfillment of those expectations. Originality/value A statistical model of survival is used, which is appropriate for the study of the employee turnover processes, and its inherent temporal nature.

Automatic transcription of the Polish newsreel

2019 ◽  
Vol 55 (2) ◽  
pp. 183-209
Author(s):  
Danijel Koržinek ◽  
Krzysztof Wołk ◽  
Łukasz Brocki ◽  
Krzysztof Marasek
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Language Models ◽  
Late 20Th Century ◽  
Word Error Rate ◽  
Audio Quality ◽  
Transcription System ◽  
Automatic Transcription ◽  
Using Data ◽  
Optimal Adaptation

Abstract This paper describes an automatic transcription system for the Polish Newsreel, which is a collection of mid to late 20th century news segments presented in audio and video form. They are characterized by their use of archaic language and poor audio quality, which makes them a demanding problem for speech recognition systems. Acoustic and language models had to be retrained using data from in-domain corpora. During the adaptation of the models, experiments were carried out to select optimal adaptation parameters. The experiments showed that the adaptation of the speech recognition system to a narrow and clearly defined domain significantly increases its efficiency. The final word error rate obtained for this domain was 10.97%.

Using Business Intelligence for Operational Decision-Making in Call Centers

2012 ◽  
Vol 4 (1) ◽  
pp. 43-54
Author(s):  
Eric Kyper ◽  
Michael Douglas ◽  
Roger Blake
Keyword(s):  
Business Intelligence ◽  
Call Center ◽  
Call Centers ◽  
Insurance Company ◽  
Service Levels ◽  
Factors Affecting ◽  
Intelligence System ◽  
Operational Decision Making ◽  
Using Data ◽  
Window Approach

This paper proposes an operational business intelligence system for call centers. Using data collected from a large U.S. insurance company, the authors demonstrate a decision tree based solution to help the company achieve excellence through improved service levels. The initial results from this study provide insight into the factors affecting this firm’s call center service levels, and the solution developed in this paper provides two distinct advantages to managers. First, it enables them to identify key factors and the role they play in determining service levels. Second, a sliding window approach is proposed which allows managers to see the effects of resource reallocation on service levels on an on-going basis.

scholarly journals Should multiple imputation be the method of choice for handling missing data in randomized trials?

2016 ◽  
Vol 27 (9) ◽  
pp. 2610-2626 ◽  
Author(s):  
Thomas R Sullivan ◽  
Ian R White ◽  
Amy B Salter ◽  
Philip Ryan ◽  
Katherine J Lee
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Missing At Random ◽  
Outcome Data ◽  
Average Treatment Effect ◽  
Analysis Model ◽  
Data Simulation ◽  
Using Data

The use of multiple imputation has increased markedly in recent years, and journal reviewers may expect to see multiple imputation used to handle missing data. However in randomized trials, where treatment group is always observed and independent of baseline covariates, other approaches may be preferable. Using data simulation we evaluated multiple imputation, performed both overall and separately by randomized group, across a range of commonly encountered scenarios. We considered both missing outcome and missing baseline data, with missing outcome data induced under missing at random mechanisms. Provided the analysis model was correctly specified, multiple imputation produced unbiased treatment effect estimates, but alternative unbiased approaches were often more efficient. When the analysis model overlooked an interaction effect involving randomized group, multiple imputation produced biased estimates of the average treatment effect when applied to missing outcome data, unless imputation was performed separately by randomized group. Based on these results, we conclude that multiple imputation should not be seen as the only acceptable way to handle missing data in randomized trials. In settings where multiple imputation is adopted, we recommend that imputation is carried out separately by randomized group.

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