Telephone Channels Research Articles

Electronic-Banking Services: A Study on Selected Commercial Banks in Bangladesh

E-Banking as the wave of the future, provides enormous benefits to consumers in terms of cost of transactions, either through internet, telephone or other electronic delivery channels. E-Banking is now widely practiced in Bangladesh. There are various types of e-banking services like SMS banking, Tele Banking, Push and Pull services, ATM, Fast Track etc. that have been introduced by the commercial banks in Bangladesh. This study attempts to explore various forms and characteristics of e-banking and finds out the merits and demerits of e-banking in selected commercial banks of our country. The major findings of the study show that all the commercial banks selected for the study have been practicing e-banking successfully and Dutch-Bangla Bank Ltd. is providing electronic banking services more effectively than other commercial banks selected in this study. Besides, there is a positive impact of e-banking on the socio-economic development in Bangladesh. There should be diversification of such activities in rural areas of Bangladesh. JEL Classification Code: M15

An Information Stock Model of Customer Behavior in Multichannel Customer Support Services

We develop a model to understand and predict customers’ observed multichannel behavior in a customer support setting. Using individual-level data from a U.S.-based health insurance firm, we model a customer’s query frequency and choice of using the telephone or web channel for resolving queries as a stochastic function of her latent “information stock.” The information stock is a function of the customer’s “information needs” (which arise when customers file health insurance claims) and “information gains” (which customers obtain when they resolve their queries through the telephone and web support channels), and other factors such as seasonal effects (for instance, queries that arise at the time of annual contract renewal). We find that average information gain from a telephone call is twice as much as that from visiting the web portal; customers prefer the telephone channel for health event-related information but prefer the web portal for structured seasonal information; and customers are polarized in their propensities of using the web channel and can be broadly classified into “web avoiders” and “web seekers.” Our model provides superior in-sample and out-of-sample fit than multiple benchmark models for aggregate and individual-level customer activity and has several managerial uses, such as capacity planning.

Partitioning technique for transforming perfect binary trees into single-row networks

Many problems in science and engineering can be simplified into the form of a perfect binary tree. This paper discusses our study entitled Perfect Binary Tree Sequence (PBTS) which transforms a perfect binary tree into the single-row network. The transformation is necessary in applications such as in the assignment of telephone channels to caller–receiver pairs roaming in cells in a cellular network on real-time basis. In this application, each caller and receiver in a call forms a node, while their pair connection forms the edge. A specific case of the graph in the form of a binary tree is then transformed into its corresponding single-row network for assigning the channels to the caller–receiver pairs. PBTS starts with the formation of the spine from a perfect binary tree through the insertion mechanism, and this leads to the expansion of the spine into one or more zones in the single-row network. This is followed by the formation of terminals and intervals for optimal transformation into the nets of the single-row network using our earlier method called ESSR. The numerical experiment results support our hypothesis that PBTS transforms the tree into its single-row network efficiently.

MODELS DEVELOPMENT FOR SINGLE–ROW NETWORKS FROM CONNECTED GRAPHS

In this paper, we present a collection of models for connected graphs mapping into single–row networks. The collection involves three specific models for perfect binary trees, trees and partially dense graphs, and three general models for connected graphs. These models are compared in terms of their structures, energy values, congestion and number of doglegs in the single–row transformation. The numerical experiments are run by each respective developed program. The transformation is necessary in applications such as in the assignment of telephone channels to caller–receiver pairs roaming in cells in a cellular network on real–time basis.

Analysis of acoustic parameters for consonant voicing classification in clean and telephone speech

This paper describes acoustic cues for classification of consonant voicing in a distinctive feature-based speech recognition system. Initial acoustic cues are selected by studying consonant production mechanisms. Spectral representations, band-limited energies, and correlation values, along with Mel-frequency cepstral coefficients features (MFCCs) are also examined. Analysis of variance is performed to assess relative significance of features. Overall, 82.2%, 80.6%, and 78.4% classification rates are obtained on the TIMIT database for stops, fricatives, and affricates, respectively. Combining acoustic parameters with MFCCs shows performance improvement in all cases. Also, performance in the NTIMIT telephone channel speech shows that acoustic parameters are more robust than MFCCs.

TEXT FILE ENCRYPTION USING FFT TECHNIQUE IN LAB VIEW 8.6

Encryption has always been a very important part of military communications. Here we deal with digital transmission technique. Digital transmission is always much more efficient than analog transmission, and it is much easier for digital encryption techniques to achieve a very high degree of security. Of course, this type of technique is still not quite compatible with today’s technical environment, i.e. most of the telephone systems are still analog instead of digital; most practical digitizers still require a relatively high bit rate which cannot be transmitted via standard analog telephone channels; and low bit rate speech digitizers still imply relatively high complexity and poor quality. Digital transmission adopts “Scrambling” technique. Scrambling methods are considered as important methods that provide the communication systems a specified degree of security, depending on the used technique to implement the scrambling method. There are many traditional scrambling methods used in single dimension such as time or frequency domain scrambling.

An Information Stock Model of Customer Behavior in Multichannel Customer Support Services

We develop a model to understand and predict customers’ observed multichannel behavior in a customer support setting. Using individual-level data from a US-based health insurance firm, we model a customer's query frequency and choice of using the telephone or web channel for resolving queries as a stochastic function of her latent “information stock.” The information stock is a function of the customer’s “information needs” (which arise when customers file health insurance claims) and “information gains” (which customers obtain when they resolve their queries through the telephone and web support channels), and other factors such as seasonal effects (for instance, queries that arise at the time of annual contract renewal). We find that: average information gain from a telephone call is twice as much as that from visiting the web portal; customers prefer the telephone channel for health event-related information but prefer the web portal for structured seasonal information; and customers are polarized in their propensities of using the web channel and can be broadly classified into “web avoiders” and “web seekers.” Our model provides superior in-sample and out-of-sample fit than multiple benchmark models for aggregate and individual-level customer activity, and has several managerial uses, such as capacity planning.

Telephone Channel Compensation in Speaker Verification Using a Polynomial Approximation in the Log-Filter-Bank Energy Domain

This correspondence presents a novel feature-space channel compensation technique that models the convolutional distortion in the log-energy mel-filter domain by means of a polynomial approximation. The proposed parametric distortion model generates appropriate constraints in the spectral domain that help to improve the channel cancelling estimation with limited data. In a text-dependent speaker verification task, the polynomial-based channel estimation scheme can lead to reductions in equal error rate (EER) as great as 22% and 8% when compared with the baseline system and with the standard cepstral bias removal approach, respectively, with no significant increase in computational load.

The Phonetic Complex Data Based on FPGA

This paper describes the use of FPGA realization of communication stations, then road transport service data method, if duty rate 32Kb/S, usually on duty voice quality ask for much, as long as can understand. Therefore, if duty rates can be lowered into the appropriate data transmission rate by reducing some of the data, the formation of voice, data combined for the road transport, the rate of post-combiner 32Kb / S, telephone channels in the transmission of duty .this paper designed a total of 5 programs, if the rate of the service16Kb/S, the data rate of 9.6Kb/S, 4.8Kb/S program, done simulation, meet design requirements.

Temporal envelope compensation for robust phoneme recognition using modulation spectrum

A robust feature extraction technique for phoneme recognition is proposed which is based on deriving modulation frequency components from the speech signal. The modulation frequency components are computed from syllable-length segments of sub-band temporal envelopes estimated using frequency domain linear prediction. Although the baseline features provide good performance in clean conditions, the performance degrades significantly in noisy conditions. In this paper, a technique for noise compensation is proposed where an estimate of the noise envelope is subtracted from the noisy speech envelope. The noise compensation technique suppresses the effect of additive noise in speech. The robustness of the proposed features is further enhanced by the gain normalization technique. The normalized temporal envelopes are compressed with static (logarithmic) and dynamic (adaptive loops) compression and are converted into modulation frequency features. These features are used in an automatic phoneme recognition task. Experiments are performed in mismatched train/test conditions where the test data are corrupted with various environmental distortions like telephone channel noise, additive noise, and room reverberation. Experiments are also performed on large amounts of real conversational telephone speech. In these experiments, the proposed features show substantial improvements in phoneme recognition rates compared to other speech analysis techniques. Furthermore, the contribution of various processing stages for robust speech signal representation is analyzed.

Tailored Health Communication to Change Lifestyle Behaviors

Tailored health communication research represents a very promising line of inquiry that has the potential to produce major impacts on lifestyle behaviors. This study defines tailoring and discusses how tailored interventions operate, including comparing/ contrasting different tailoring channels. Next, the authors review the literature on tailored interventions to change lifestyle behaviors, with a focus on smoking cessation, dietary change, and physical activity, as well as interventions that address multiple lifestyle behaviors. Finally, future directions for tailoring research are discussed. To date, a large literature has amassed showing the promise of tailored programs delivered via print, Internet, local computer/kiosk, telephone, and interpersonal channels. Numerous studies demonstrate that these programs are capable of significant impacts on smoking cessation, dietary change, physical activity, and multiple behavior change. It is concluded that the potential of tailoring will be more fully realized as (a) the field builds a more cumulative science of tailoring and (b) greater dissemination of efficacious tailored programs takes place.

Studies on Implementation of Wavelet for denoising Speech Signal

ABSTRACT The wavelet transform has become a powerful tool of signal analysis and is widely used in many applications which include signal detection and denoising. In hands free speech communication environments situation occurs that speech is superposed by background noise. Over the past few decades there is tremendous increase in the level of ambient environmental noise. This has been due to growth of technology. Noise is added by various factors like noisy engines, heavy machines, pumps, vehicles, over noisy telephone channel or using radio communication device in an aircraft cockpit. The wavelet denoising technique is called thresholding; it is a non linear algorithm. It can be decomposed in three steps. This paper is based on wavelet as denoising algorithm. Haar and Daubechies wavelets are implemented on speech signals and performance is evaluated. Categories and Subject Descriptors Speech signal Processing, Wavelets, Denoising.

Robust speaker recognition in cross-channel condition based on Gaussian mixture model

One of the most difficult challenges for speaker recognition is dealing with channel variability. In this paper, several new cross-channel compensation techniques are introduced for a Gaussian mixture model--universal background model (GMM-UBM) speaker verification system. These new techniques include wideband noise reduction, echo cancellation, a simplified feature-domain latent factor analysis (LFA) and data-driven score normalization. A novel dynamic Gaussian selection algorithm is developed to reduce the feature compensation time by more than 60% without any performance loss. The performance of different techniques across varying channel train/test conditions are presented and discussed, finding that speech enhancement, which used to be neglected for telephone speech, is essential for cross-channel tasks, and the channel compensation techniques developed for telephone channel speech also perform effectively. The per microphone performance analysis further shows that speech enhancement can boost the effects of other techniques greatly, especially on channels with larger signal-to-noise ratio (SNR) variance. All results are presented on NIST SRE 2006 and 2008 data, showing a promising performance gain compared to the baseline. The developed system is also compared with other state-of-the-art speaker verification systems. The result shows that the developed system can obtain comparable or even better performance but consumes much less CPU time, making it more suitable for practical use.

Modeling Call Quality for Time-Varying Transmission Characteristics Using Simulated Conversational Structures

This study investigates the perception of speech quality over telephone channels with time-varying transmission characteristics for simulated conversational structures. The aim is to establish a relationship between subjective quality associated with short speech samples (5―6 seconds) and quality associated with overall conversations (1― 2 minutes). Two two-part experiments were conducted. In the first part of each experiment, dialog-final ratings within the temporal structure of a telephone conversation were assessed. Varying transmission characteristics were realized with ten different degradation profiles of preprocessed speech samples obtained mainly from real mobile channels to ensure authentic types of degradation. The second part was carried out to obtain separate short-term ratings of the speech samples used in the first part. Experiments 1 and 2 tested different conversation durations (1 and 2 minutes). The results demonstrate that dialog-final ratings vary with respect to the degradation profile, revealing a recency effect and a strong impact of individual bad samples. Two related models which implement these findings are presented. With these models, dialog-final quality ratings can be estimated significantly better than by plain averaging of short sample ratings (about 10% absolute improvement). They also perform better than two algorithms taken from literature. Both models can be applied to the instrumental method described in ITU-T Rec. P.862 [1], resulting in about 13% absolute improvement. They were evaluated with the results of two different experiments, which were performed independently but on the basis of our test procedure. In these experiments similar profiles but a different type of quality degradation, different sample durations, and different speech material were used. The models proved to be valid and reliable for the time span investigated (1―2 minutes) and for the profiles used. One of them is now being recommended by the ETSI STQ mobile group.

Differences between students and non‐students' willingness to donate to a charitable organisation

Abstract This paper reports on a comparative study of students and non‐students that investigates which psycho‐social factors influence intended donation behaviour within a single organisation that offers multiple forms of donation activity. Additionally, the study examines which media channels are more important to encourage donation. A self‐administered survey instrument was used and a sample of 776 respondents recruited. Logistic regressions and a Chow test were used to determine statistically significant differences between the groups. For donating money, importance of charity and attitude towards charity influence students, whereas only importance of need significantly influences non‐students. For donating time, no significant influences were found for non‐students, however, importance of charity and attitude towards charity were significant for students. Importance of need was significant for both students and non‐students for donating goods, with importance of charity also significant for students. Telephone and television channels were important for both groups. However, Internet, email and short messaging services were more important for students, providing opportunities to enhance this group's perceptions of the importance of the charity, and the importance of the need, which ultimately impacts on their attitudes towards the charity. These differences highlight the importance of charities focussing on those motivations and attitudes that are important to a particular target segment and communicating through appropriate media channels for these segments. Copyright © 2009 John Wiley & Sons, Ltd.

Feature Compensation Techniques for ASR on Band-Limited Speech

Band-limited speech (speech for which parts of the spectrum are completely lost) is a major cause for accuracy degradation of automatic speech recognition (ASR) systems particularly when acoustic models have been trained with data with a different spectral range. In this paper, we present an extensive study of the problem of ASR of band-limited speech with full-bandwidth acoustic models. Our focus is mainly on band-limited feature compensation, covering even the case of time-varying band-limiting distortions, but we also compare this approach to more common model-side techniques (adaptation and retraining) and explore the combination of feature-based and model-side approaches. The feature compensation algorithms proposed are organized in a unified framework supported by a novel mathematical model of the impact of such distortions on Mel-frequency cepstral coefficient (MFCC) features. A crucial and novel contribution is the analysis made of the relative correlation of different elements in the MFCC feature vector for the cases of full-bandwidth and limited-bandwidth speech, which justifies an important modification in the feature compensation scheme. Furthermore, an intensive experimental analysis is provided. Experiments are conducted on real telephone channels, as well as artificial low-pass and bandpass filters applied over TIMIT data, and results are given for different experimental constraints and variations of the feature compensation method. Results for other well-known robustness approaches, such as cepstral mean normalization (CMN), model retraining, and model adaptation are also given for comparison. ASR performance with our approach is similar or even better than model adaptation, and we argue that in particular cases such as rapidly varying distortions, or limited computational or memory resources, feature compensation is more convenient. Furthermore, we show that feature-side and model-side approaches may be combined, outperforming any of those approaches alone.

Carrier-wave telephony over power lines: Early history [History of Communications

In this column we discuss the early history of power line voice communication, beginning in 1918 and continuing until the early 1930s. We note that these developments were based on the 1910 demonstration by Major George Squier of the United States Army Signal Corps of his "wired wireless" technique for transmitting multiple telephone channels over one pair of wires. Power companies world-wide picked up on this carrier-wave telephony technique for use over power lines, with electrical manufacturing companies such as GE and Westinghouse in the United States and Telefunken in Germany, among others, then developing a variety of systems for this purpose. By 1930, the technique had reached a period of maturity, with 1000 systems installed throughout Europe and the United States.

Estimating the Quality of Synthesized and Natural Speech Transmitted Through Telephone Networks Using Single-ended Prediction Models

This paper reports on experiments to estimate the speech output quality of telephone services in an instrumental way, using single-ended quality prediction models. It addresses both naturally-produced as well as synthesized speech generated with a Text-To-Speech (TTS) system. Three auditory tests have been carried out where typical speech samples have been transmitted over various telephone channels, and then judged by listeners with respect to their overall quality. The mean auditory ratings obtained in these tests have been compared to estimates provided by three different single-ended models, one of which is currently recommended by the International Telecommunication Union for predicting the quality of naturally-produced speech. Correlations between auditory and estimated quality scores vary considerably between experiments. It is concluded that the single-ended models mainly predict the effects of the transmission channel, but not of the (naturally-produced or synthesized) source speech material.

Robust Fundamental Frequency Estimation Combining Contrast Enhancement and Feature Unbiasing

A high-performance fundamental frequency estimator inspired by the human auditory system is presented. It uses a cubic-root transform and contrast enhancement on the spectral domain to enhance weak harmonics and suppress noise. The proposed weighted unbiasing process reduces multiple or submultiple pitch errors efficiently. Evaluation is performed under the presence of white noise, babble noise, and telephone channel distortion. The results show that the proposed method performs equivalent to or better than previous state-of-the-art methods in the majority of the noise and channel conditions.

On the Use of Complementary Spectral Features for Speaker Recognition

The most popular features for speaker recognition are Mel frequency cepstral coefficients (MFCCs) and linear prediction cepstral coefficients (LPCCs). These features are used extensively because they characterize the vocal tract configuration which is known to be highly speaker-dependent. In this work, several features are introduced that can characterize the vocal system in order to complement the traditional features and produce better speaker recognition models. The spectral centroid (SC), spectral bandwidth (SBW), spectral band energy (SBE), spectral crest factor (SCF), spectral flatness measure (SFM), Shannon entropy (SE), and Renyi entropy (RE) were utilized for this purpose. This work demonstrates that these features are robust in noisy conditions by simulating some common distortions that are found in the speakers' environment and a typical telephone channel. Babble noise, additive white Gaussian noise (AWGN), and a bandpass channel with 1 dB of ripple were used to simulate these noisy conditions. The results show significant improvements in classification performance for all noise conditions when these features were used to complement the MFCC and MFCC features. In particular, the SC and SCF improved performance in almost all noise conditions within the examined SNR range (10–40 dB). For example, in cases where there was only one source of distortion, classification improvements of up to 8% and 10% were achieved under babble noise and AWGN, respectively, using the SCF feature.