Frame-based speech processing inherently assumes a stationary behavior of speech
signals in a short period of time. Over a long time, the characteristics of the signals can …

Distributed speech recognition (DSR) splits the processing of data between a mobile device
and a network server. In the front-end, features are extracted and compressed to transmit …

The frame rate of the observation sequence in distributed speech recognition applications
may be reduced to suit a resource-limited front-end device. In order to use models trained …

An effective way to increase noise robustness in automatic speech recognition is to label the
noisy speech features as either reliable or unreliable ('missing'), and replace ('impute') the …

The enthusiasm of deploying automatic speech recognition (ASR) on mobile devices is
driven both by remarkable advances in ASR technology and by the demand for efficient user …

As mobile devices become pervasive and small, the design of efficient user interfaces is
rapidly develo** into a major issue. The expectation for speech-centric interfaces has …

In distributed speech recognition applications, the frame rate of observation may be reduced
to match a resource limited front end device. A method to adapt the transition probabilities of …

An improved hidden Markov model (HMM) adaptation method is proposed for the
recognition of reduced frame rate speech. In previous studies of this kind of model …

In this paper, we analyze the performance of network speech recognition (NSR) over IP
networks, adapting and proposing new solutions to the packet loss problem for code excited …

In distributed speech recognition (DSR), data packets may be lost over error prone
channels. A commonly used approach to rectify this is to reconstruct a full frame rate data …