Anthony Phillip Stark

Exploiting Conjugate Symmetry of the Short-Time Fourier Spectrum for Speech Enhancement

Typical speech enhancement algorithms operate on the short-time magnitude spectrum, while keeping the short-time phase spectrum unchanged for synthesis. We propose a novel approach where the noisy magnitude spectrum is recombined with a changed phase spectrum to produce a modified complex spectrum. During synthesis, the low energy components of the modified complex spectrum cancel out more than the high energy components, thus reducing background noise. Using objective speech quality measures, informal subjective listening tests and spectrogram analysis, we show that the proposed method results in improved speech quality.

Use of speech presence uncertainty with MMSE spectral energy estimation for robust automatic speech recognition

In this paper, we investigate the use of the minimum mean square error (MMSE) spectral energy estimator for use in environment-robust automatic speech recognition (ASR). In the past, it has been common to use the MMSE log-spectral amplitude estimator for this task. However, this estimator was originally derived under subjective human listening criteria. Therefore its complex suppression rule may not be optimal for use in ASR. On the other hand, it can be shown that the MMSE spectral energy estimator is closely related to the MMSE Mel-frequency cepstral coefficient (MFCC) estimator. Despite this, the spectral energy estimator has tended to suffer from the problem of excessive residual noise. We examine the cause of this residual noise and show that the introduction of a heuristic based speech presence uncertainty (SPU) can significantly improve its performance as a front-end ASR enhancement regime. The proposed spectral energy SPU estimator is evaluated on the Aurora2, RM and OLLO2 speech recognition tasks and can be shown to significantly improve additive noise robustness over the more common spectral amplitude and log-spectral amplitude estimators.

MMSE estimation of log-filterbank energies for robust speech recognition

In this paper, we derive a minimum mean square error log-filterbank energy estimator for environment-robust automatic speech recognition. While several such estimators exist within the literature, most involve trade-offs between simplifications of the log-filterbank noise distortion model and analytical tractability. To avoid this limitation, we extend a well known spectral domain noise distortion model for use in the log-filterbank energy domain. To do this, several mathematical transformations are developed to transform spectral domain models into filterbank and log-filterbank energy models. As a result, a new estimator is developed that allows for robust estimation of both log-filterbank energies and subsequent Mel-frequency cepstral coefficients. The proposed estimator is evaluated over the Aurora2, and RM speech recognition tasks, with results showing a significant reduction in word recognition error over both baseline results and several competing estimators.

Comparative evaluation of speech enhancement methods for robust automatic speech recognition

A comparative evaluation of speech enhancement algorithms for robust automatic speech recognition is presented. The evaluation is performed on a core test set of the TIMIT speech corpus. Mean objective speech quality scores as well as ASR correctness scores under two noise conditions are given.

Kalman fitler with phase spectrum compensation algorithm for speech enhancement

In this paper, we propose to combine the Kalman filter with a recent speech enhancement technique, called the phase spectrum compensation procedure, or PSC. More specifically, we apply the PSC technique to initialise the Kalman filter, whereby PSC is used to clean the noisy speech prior to LPC estimation for the Kalman recursion. We refer to the combined technique as the Kalman-PSC filter. Using an objective speech quality measure, formal subjective listening tests and spectrogram analysis, we show that the proposed method results in improved speech quality.

Switched parasitic patch antenna array using thirteen hexagonal shaped elements

A novel circularly polarized smart patch antenna array with hexagonal elements was designed as a hub for indoor/outdoor mobile wireless computing networks. A frequency domain finite element method (FEM) was employed to design the patch antenna array using an optimization technique based on the gradient algorithm to optimize the physical structure on a finite ground plane for optimal directivity in elevation plane and antenna gain at 2.4 GHz. The switched parasitic element technique was used to enable steering through six locations in azimuth with an elevation angle of between 60deg and 70deg. The single-feed circularly polarized patch antenna array with thirteen-element configuration achieved the highest frequency bandwidth. A comparison between simulated and measured reflection coefficients and radiation patterns was made with consistent results.