Learn More
The performance of Automatic Speech Recognition (ASR) systems in the presence of noise is an area that has attracted a lot of research interest. Additive noise from interfering noise sources, and convolutional noise arising from transmission channel characteristics both contribute to a degradation of performance in ASR systems. This paper addresses the(More)
a r t i c l e i n f o a b s t r a c t This paper examines the performance of a Distributed Speech Recognition (DSR) system in the presence of both background noise and packet loss. Recognition performance is examined for feature vectors extracted from speech using a physiologically-based auditory model, as an alternative to the more commonly-used Mel(More)
Traditionally, Received Signal Strength (RSS) has been the primary indicator informing network selection strategies. However, approaches based on RSS are limited as they do not consider how (a) dynamic network conditions and (b) potential predictability of movement affects network performance. The wider research focus analyses the potential effect of(More)
This paper addresses the problem of speech recognition in noisy conditions. In particular, the paper examines the use of an auditory model as a front-end for a HMM-based speech recognition system. To further improve the performance of the auditory-based recognition system in background noise, the input speech is pre-processed using an algorithm for speech(More)
Many existing algorithms manage network handover using static thresholds or weights applied to performance metrics. Such approaches are performance limited as they require knowledge of prior network performance. Performance metric thresholds and weights are often preconfigured based on the experience of network personnel. Static weightings are often(More)
Handover algorithms typically operate by assigning preconfigured threshold or weight values onto network performance metrics such as delay, data loss and signal strength. Such approaches are performance limited as they do not consider external factors that affect the network such as the physical environment and current weather conditions. Previous research(More)