The phase vocoder is a well established tool for time scaling and pitch shifting speech and audio signals via modification of their short-time Fourier transforms (STFTs). In contrast to time-domain time-scaling and pitch-shifting techniques, the phase vocoder is generally considered to yield high quality results, especially for large modification factors and/or polyphonic signals. However, the phase vocoder is also known for introducing a characteristic perceptual artifact, often described as "phasiness", "reverberation", or "loss of presence". This paper examines the problem of phasiness in the context of time-scale modification and provides new insights into its causes. Two extensions to the standard phase vocoder algorithm are introduced, and the resulting sound quality is shown to be significantly improved. Moreover, the modified phase vocoder is shown to provide a factor-of-two decrease in computational cost.

Multi-Layer Perception (MLP) neural networks have been used extensively for classification tasks. Typically, the MLP network is trained explicitly to produce the correct classification as its output. For speech recognition, however, several investigators have recently experimented with an indirect approach: a unique MLP predictive network is trained for each class of data, and classification is accomplished by determining which predictive network serves as the best model for samples of unknown speech. Results from this approach have been mixed. In this report, we compare the direct and indirect approaches to classification from a more fundamental perspective. We show how recent advances in nonlinear dimensionality reduction can be incorporated into the indirect approach, and we show how the two approaches can be integrated in a novel MLP framework. We further show how these new MLP networks can be usefully viewed as generalizations of Learning Vector Quantization (LVQ) and of subspace methods of pattern recognition. Lastly, we show that applying these ideas to the classification of temporal trajectories can substantially improve performance on simple tasks.