Likelihood-maximising frameworks for enhanced in-car speech recognition

Speech recognition in car environments has been identified as a valuable means for reducing driver distraction when operating non-critical in-car systems. Likelihood-maximising (LIMA) frameworks optimise speech enhancement algorithms based on recognised state sequences rather than traditional signal-level criteria such as maximising signal-to-noise ratio. Previously presented LIMA frameworks require calibration utterances to generate optimised enhancement parameters which are used for all subsequent utterances. Sub-optimal recognition performance occurs in noise conditions which are significantly different from that present during the calibration session - a serious problem in rapidly changing noise environments. We propose a dialog-based design which allows regular optimisation iterations in order to track the changing noise conditions. Experiments using Mel-filterbank spectral subtraction are performed to determine the optimisation requirements for vehicular environments and show that minimal optimisation assists real-time operation with improved speech recognition accuracy. It is also shown that the proposed design is able to provide improved recognition performance over frameworks incorporating a calibration session.

Incorporating interdependence in risk likelihood analysis to enhance diagnostics in condition monitoring

This research is aimed at addressing problems in the field of asset management relating to risk analysis and decision making based on data from a Supervisory Control and Data Acquisition (SCADA) system. It is apparent that determining risk likelihood in risk analysis is difficult, especially when historical information is unreliable. This relates to a problem in SCADA data analysis because of nested data. A further problem is in providing beneficial information from a SCADA system to a managerial level information system (e.g. Enterprise Resource Planning/ERP). A Hierarchical Model is developed to address the problems. The model is composed of three different Analyses: Hierarchical Analysis, Failure Mode and Effect Analysis, and Interdependence Analysis. The significant contributions from the model include: (a) a new risk analysis model, namely an Interdependence Risk Analysis Model which does not rely on the existence of historical information because it utilises Interdependence Relationships to determine the risk likelihood, (b) improvement of the SCADA data analysis problem by addressing the nested data problem through the Hierarchical Analysis, and (c) presentation of a framework to provide beneficial information from SCADA systems to ERP systems. The case study of a Water Treatment Plant is utilised for model validation.

Extraction of road lanes from high-resolution stereo aerial imagery based on maximum likelihood segmentation and texture enhancement

Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.

The effect of dialect mismatch on likelihood-maximising speech enhancement for noise-robust speech recognition

Traditional speech enhancement methods optimise signal-level criteria such as signal-to-noise ratio, but these approaches are sub-optimal for noise-robust speech recognition. Likelihood-maximising (LIMA) frameworks are an alternative that optimise parameters of enhancement algorithms based on state sequences generated for utterances with known transcriptions. Previous reports of LIMA frameworks have shown significant promise for improving speech recognition accuracies under additive background noise for a range of speech enhancement techniques. In this paper we discuss the drawbacks of the LIMA approach when multiple layers of acoustic mismatch are present – namely background noise and speaker accent. Experimentation using LIMA-based Mel-filterbank noise subtraction on American and Australian English in-car speech databases supports this discussion, demonstrating that inferior speech recognition performance occurs when a second layer of mismatch is seen during evaluation.

The application of phonetic distribution normalisation to likelihood-maximising speech enhancement for robust ASR

Traditional speech enhancement methods optimise signal-level criteria such as signal-to-noise ratio, but such approaches are sub-optimal for noise-robust speech recognition. Likelihood-maximising (LIMA) frameworks on the other hand, optimise the parameters of speech enhancement algorithms based on state sequences generated by a speech recogniser for utterances of known transcriptions. Previous applications of LIMA frameworks have generated a set of global enhancement parameters for all model states without taking in account the distribution of model occurrence, making optimisation susceptible to favouring frequently occurring models, in particular silence. In this paper, we demonstrate the existence of highly disproportionate phonetic distributions on two corpora with distinct speech tasks, and propose to normalise the influence of each phone based on a priori occurrence probabilities. Likelihood analysis and speech recognition experiments verify this approach for improving ASR performance in noisy environments.

Critical gap estimation by numerical and statistical highest likelihood search

Many traffic situations require drivers to cross or merge into a stream having higher priority. Gap acceptance theory enables us to model such processes to analyse traffic operation. This discussion demonstrated that numerical search fine tuned by statistical analysis can be used to determine the most likely critical gap for a sample of drivers, based on their largest rejected gap and accepted gap. This method shares some common features with the Maximum Likelihood Estimation technique (Troutbeck 1992) but lends itself well to contemporary analysis tools such as spreadsheet and is particularly analytically transparent. This method is considered not to bias estimation of critical gap due to very small rejected gaps or very large rejected gaps. However, it requires a sufficiently large sample that there is reasonable representation of largest rejected gap/accepted gap pairs within a fairly narrow highest likelihood search band.

Cross likelihood ratio based speaker clustering using eigenvoice models

This paper proposes the use of eigenvoice modeling techniques with the Cross Likelihood Ratio (CLR) as a criterion for speaker clustering within a speaker diarization system. The CLR has previously been shown to be a robust decision criterion for speaker clustering using Gaussian Mixture Models. Recently, eigenvoice modeling techniques have become increasingly popular, due to its ability to adequately represent a speaker based on sparse training data, as well as an improved capture of differences in speaker characteristics. This paper hence proposes that it would be beneficial to capitalize on the advantages of eigenvoice modeling in a CLR framework. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 35.1% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system.

Letter to the editor : likelihood of tetraplegia after CT clearance of the cervical spine in obtunded blunt trauma patients

...the probabilistic computer simulation study by Dunham and colleagues evaluating the impact of different cervical spine management (CSM) strategies on tetraplegia and brain injury outcomes.1 Based on literature findings, expert opinion and with use of advances programming techniques the authors conclude that early collar removal without cervical spine magnetic resonance imaging (MRI) is a preferable CSM strategy for comatose, blunt trauma patients with extremity movement and a negative cervical spine computed tomography(CT) scan. Although we do not have the required expertise to comment on the applied statistical approach, we would like to comment on one of the medical assumptions raised by the authors, namely the likelihood of tetraplegia in this specific population....

The perceived importance of mall retail categories and its effect on likelihood of purchase during a recession

Recessions impact the retail sector and as such research into consumer decision making during such times is imperative. In response to this, our study takes an innovative approach to examining how the perceived importance of retail store categories in a shopping mall influences the relationship between consumers' shopping attitudes and likelihood of purchasing in those categories during a recession. The overall findings show the importance of a product category to a consumer, which is often overlooked, has a strong explanatory influence on consumer purchase intentions for that specific retail store categories in a shopping mall under recession conditions. Findings also show that for consumers’ who have altered their shopping behaviour the perceived importance of a retail store category fully mediates the relationship for: Majors, Leisure, Food Catered and Mini Majors categories, and partial mediation for Apparel. Importance has no mediating effect for: Food Retail, General Retail, Mobile Phone Services, Home wares, and Retail Services. Our study makes a key contribution to the retail management literature with the findings suggesting that redefining and articulating the importance of the value offering for specific retail store categories can help reduce the impact of changes in consumers' recessionary shopping intentions across the mall tenant mix. Such actions can then help preserve the image of the shopping mall in the minds of the consumers when the economic recovery begins.

Novel methods and the maximum likelihood estimation technique for estimating traffic critical gap

Most unsignalised intersection capacity calculation procedures are based on gap acceptance models. Accuracy of critical gap estimation affects accuracy of capacity and delay estimation. Several methods have been published to estimate drivers’ sample mean critical gap, the Maximum Likelihood Estimation (MLE) technique regarded as the most accurate. This study assesses three novel methods; Average Central Gap (ACG) method, Strength Weighted Central Gap method (SWCG), and Mode Central Gap method (MCG), against MLE for their fidelity in rendering true sample mean critical gaps. A Monte Carlo event based simulation model was used to draw the maximum rejected gap and accepted gap for each of a sample of 300 drivers across 32 simulation runs. Simulation mean critical gap is varied between 3s and 8s, while offered gap rate is varied between 0.05veh/s and 0.55veh/s. This study affirms that MLE provides a close to perfect fit to simulation mean critical gaps across a broad range of conditions. The MCG method also provides an almost perfect fit and has superior computational simplicity and efficiency to the MLE. The SWCG method performs robustly under high flows; however, poorly under low to moderate flows. Further research is recommended using field traffic data, under a variety of minor stream and major stream flow conditions for a variety of minor stream movement types, to compare critical gap estimates using MLE against MCG. Should the MCG method prove as robust as MLE, serious consideration should be given to its adoption to estimate critical gap parameters in guidelines.

Indigeneity and the likelihood of imprisonment in Queensland’s adult and children’s courts

Australian research on Indigenous sentencing disparities of the standard of international work is somewhat recent. Contrary to expectations based on international research, Australian studies generally have not found Indigenous offenders to be treated substantively more harshly than non-Indigenous offenders in similar circumstances. However, this research has primarily focused on adult higher courts, with little attention to lower courts and children’s courts. In this article, we examine whether Indigeneity has a direct impact on the judicial decision to incarcerate for three courts (adult higher, adult lower, children’s higher court) in Queensland. We found no significant differences in the likelihood of a sentence of incarceration in the higher courts (adult and children’s). In contrast, in the lower courts, Indigenous defendants were more likely to be imprisoned than non-Indigenous defendants when sentenced under statistically similar circumstances.