Virtual acoustics displays

The real time acoustic display capabilities are described which were developed for the Virtual Environment Workstation (VIEW) Project at NASA-Ames. The acoustic display is capable of generating localized acoustic cues in real time over headphones. An auditory symbology, a related collection of representational auditory 'objects' or 'icons', can be designed using ACE (Auditory Cue Editor), which links both discrete and continuously varying acoustic parameters with information or events in the display. During a given display scenario, the symbology can be dynamically coordinated in real time with 3-D visual objects, speech, and gestural displays. The types of displays feasible with the system range from simple warnings and alarms to the acoustic representation of multidimensional data or events

Relating Objective and Subjective Performance Measures for AAM-based Visual Speech Synthesizers

We compare two approaches for synthesizing visual speech using Active Appearance Models (AAMs): one that utilizes acoustic features as input, and one that utilizes a phonetic transcription as input. Both synthesizers are trained using the same data and the performance is measured using both objective and subjective testing. We investigate the impact of likely sources of error in the synthesized visual speech by introducing typical errors into real visual speech sequences and subjectively measuring the perceived degradation. When only a small region (e.g. a single syllable) of ground-truth visual speech is incorrect we find that the subjective score for the entire sequence is subjectively lower than sequences generated by our synthesizers. This observation motivates further consideration of an often ignored issue, which is to what extent are subjective measures correlated with objective measures of performance? Significantly, we find that the most commonly used objective measures of performance are not necessarily the best indicator of viewer perception of quality. We empirically evaluate alternatives and show that the cost of a dynamic time warp of synthesized visual speech parameters to the respective ground-truth parameters is a better indicator of subjective quality

An integrated approach to rotorcraft human factors research

As the potential of civil and military helicopters has increased, more complex and demanding missions in increasingly hostile environments have been required. Users, designers, and manufacturers have an urgent need for information about human behavior and function to create systems that take advantage of human capabilities, without overloading them. Because there is a large gap between what is known about human behavior and the information needed to predict pilot workload and performance in the complex missions projected for pilots of advanced helicopters, Army and NASA scientists are actively engaged in Human Factors Research at Ames. The research ranges from laboratory experiments to computational modeling, simulation evaluation, and inflight testing. Information obtained in highly controlled but simpler environments generates predictions which can be tested in more realistic situations. These results are used, in turn, to refine theoretical models, provide the focus for subsequent research, and ensure operational relevance, while maintaining predictive advantages. The advantages and disadvantages of each type of research are described along with examples of experimental results

Spectral discontinuity in concatenative speech synthesis – perception, join costs and feature transformations

This thesis explores the problem of determining an objective measure to represent human perception of spectral discontinuity in concatenative speech synthesis. Such measures are used as join costs to quantify the compatibility of speech units for concatenation in unit selection synthesis. No previous study has reported a spectral measure that satisfactorily correlates with human perception of discontinuity. An analysis of the limitations of existing measures and our understanding of the human auditory system were used to guide the strategies adopted to advance a solution to this problem. A listening experiment was conducted using a database of concatenated speech with results indicating the perceived continuity of each concatenation. The results of this experiment were used to correlate proposed measures of spectral continuity with the perceptual results. A number of standard speech parametrisations and distance measures were tested as measures of spectral continuity and analysed to identify their limitations. Time-frequency resolution was found to limit the performance of standard speech parametrisations.As a solution to this problem, measures of continuity based on the wavelet transform were proposed and tested, as wavelets offer superior time-frequency resolution to standard spectral measures. A further limitation of standard speech parametrisations is that they are typically computed from the magnitude spectrum. However, the auditory system combines information relating to the magnitude spectrum, phase spectrum and spectral dynamics. The potential of phase and spectral dynamics as measures of spectral continuity were investigated. One widely adopted approach to detecting discontinuities is to compute the Euclidean distance between feature vectors about the join in concatenated speech. The detection of an auditory event, such as the detection of a discontinuity, involves processing high up the auditory pathway in the central auditory system. The basic Euclidean distance cannot model such behaviour. A study was conducted to investigate feature transformations with sufficient processing complexity to mimic high level auditory processing. Neural networks and principal component analysis were investigated as feature transformations. Wavelet based measures were found to outperform all measures of continuity based on standard speech parametrisations. Phase and spectral dynamics based measures were found to correlate with human perception of discontinuity in the test database, although neither measure was found to contribute a significant increase in performance when combined with standard measures of continuity. Neural network feature transformations were found to significantly outperform all other measures tested in this study, producing correlations with perceptual results in excess of 90%

Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression

This paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on monaural segregation. The method starts with a training stage that establishes a locally-linear Gaussian regression model between the directional coordinates of all the sources and the auditory features extracted from binaural measurements. While fixed-length wide-spectrum sounds (white noise) are used for training to reliably estimate the model parameters, we show that the testing (localization) can be extended to variable-length sparse-spectrum sounds (such as speech), thus enabling a wide range of realistic applications. Indeed, we demonstrate that the method can be used for audio-visual fusion, namely to map speech signals onto images and hence to spatially align the audio and visual modalities, thus enabling to discriminate between speaking and non-speaking faces. We release a novel corpus of real-room recordings that allow quantitative evaluation of the co-localization method in the presence of one or two sound sources. Experiments demonstrate increased accuracy and speed relative to several state-of-the-art methods.Comment: 15 pages, 8 figure