New waves of consumer-centric applications, such as voice search and voice interaction
with mobile devices and home entertainment systems, increasingly require automatic …

There is a great need for robust techniques in data mining and machine learning contexts
where many standard techniques such as principal component analysis and linear …

It is well known that the performance of automatic speech recognition degrades in noisy
conditions. To address this, typically the noise is removed from the features or the models …

Adaptive training is a widely used technique for building speech recognition systems on
nonhomogeneous training data. Recently, there has been interest in applying these …

Model compensation is a standard way of improving the robustness of speech recognition
systems to noise. A number of popular schemes are based on vector Taylor series (VTS) …

Publisher Summary This chapter provides an overview of an automatic speech recognition
system and describes sources of speech variability that cause mismatch between training …

A powerful approach for handling uncertainty in observations is to modify the statistical
model of the data to appropriately reflect this uncertainty. For the task of noise-robust speech …

(57) ABSTRACT A speech processing method, comprising: receiving a speech input which
comprises a sequence of feature vectors; determining the likelihood of a sequence of words …

Rapidly adapting a speech recognition system to new speakers using a small amount of
adaptation data is important to improve initial user experience. In this paper, a count …

Noise compensation techniques for robust automatic speech recognition (ASR) attempt to
improve system performance in the presence of acoustic interference. In feature-based …