Leadership capability of team leaders in construction industry

This research was conducted to identify the important leadership capabilities for Malaysia construction industry team leaders. This research used exploratory sequential mix-method research design which is qualitative followed by quantitative research method. In the qualitative phase, semi-structured in-depth interview was selected and purposive sampling was employed in selecting 15 research participants involving team leaders and Human Resource Managers. Qualitative data was analysed using content and thematic analyses. Quantitative data was collected using survey questionnaire involving 171 randomly selected team leaders as respondents. The data was analyzed using descriptive and inferential statistics consisting of t-test, One-way Analysis of Variance (ANOVA), Pearson Correlation, Multiple Regression and Structured Equation Modeling (SEM). This study found that personal integrity, working within industry, customer focus and quality, communication and interpersonal skill, developing and empowering people and working as a team were needed leadership capabilities among construction industry team leaders. The research was also able to prove that leadership skill is a key element to develop leadership capability. A framework was developed based on the results of this study, which can be used as a guide by employers and relevant agencies in enhancing leadership capability of Malaysia construction industry team leade

Listening to features

This work explores nonparametric methods which aim at synthesizing audio from low-dimensionnal acoustic features typically used in MIR frameworks. Several issues prevent this task to be straightforwardly achieved. Such features are designed for analysis and not for synthesis, thus favoring high-level description over easily inverted acoustic representation. Whereas some previous studies already considered the problem of synthesizing audio from features such as Mel-Frequency Cepstral Coefficients, they mainly relied on the explicit formula used to compute those features in order to inverse them. Here, we instead adopt a simple blind approach, where arbitrary sets of features can be used during synthesis and where reconstruction is exemplar-based. After testing the approach on a speech synthesis from well known features problem, we apply it to the more complex task of inverting songs from the Million Song Dataset. What makes this task harder is twofold. First, that features are irregularly spaced in the temporal domain according to an onset-based segmentation. Second the exact method used to compute these features is unknown, although the features for new audio can be computed using their API as a black-box. In this paper, we detail these difficulties and present a framework to nonetheless attempting such synthesis by concatenating audio samples from a training dataset, whose features have been computed beforehand. Samples are selected at the segment level, in the feature space with a simple nearest neighbor search. Additionnal constraints can then be defined to enhance the synthesis pertinence. Preliminary experiments are presented using RWC and GTZAN audio datasets to synthesize tracks from the Million Song Dataset.Comment: Technical Repor

Improving the robustness of the usual fbe-based asr front-end

All speech recognition systems require some form of signal representation that parametrically models the temporal evolution of the spectral envelope. Current parameterizations involve, either explicitly or implicitly, a set of energies from frequency bands which are often distributed in a mel scale. The computation of those filterbank energies (FBE) always includes smoothing of basic spectral measurements and non-linear amplitude compression. A variety of linear transformations are typically applied to this time-frequency representation prior to the Hidden Markov Model (HMM) pattern-matching stage of recognition. In the paper, we will discuss some robustness issues involved in both the computation of the FBEs and the posterior linear transformations, presenting alternative techniques that can improve robustness in additive noise conditions. In particular, the root non-linearity, a voicing-dependent FBE computation technique and a time&frequency filtering (tiffing) technique will be considered. Recognition results for the Aurora database will be shown to illustrate the potential application of these alternatives techniques for enhancing the robustness of speech recognition systems.Peer ReviewedPostprint (published version

A REVIEW ON VOICE ACTIVITY DETECTION AND MEL-FREQUENCY CEPSTRAL COEFFICIENTS FOR SPEAKER RECOGNITION (TREND ANALYSIS)

ABSTRACTObjective: The objective of this review article is to give a complete review of various techniques that are used for speech recognition purposes overtwo decades.Methods: VAD-Voice Activity Detection, SAD-Speech Activity Detection techniques are discussed that are used to distinguish voiced from unvoicedsignals and MFCC- Mel Frequency Cepstral Coefficient technique is discussed which detects specific features.Results: The review results show that research in MFCC has been dominant in signal processing in comparison to VAD and other existing techniques.Conclusion: A comparison of different speaker recognition techniques that were used previously were discussed and those in current research werealso discussed and a clear idea of the better technique was identified through the review of multiple literature for over two decades.Keywords: Cepstral analysis, Mel-frequency cepstral coefficients, signal processing, speaker recognition, voice activity detection