Titlebook: Automatic Speech Recognition on Mobile Devices and over Communication Networks; Zheng-Hua Tan,Børge Lindberg Book 2008 Springer-Verlag Lon

执拗

共同时代

evanescent

Speech Recognition Over IP Networks and several speech coders used for voice over IP, where the performance of a network speech recognition (NSR) system can significantly degrade. Second, several techniques for maintaining the performance of NSR against packet loss are addressed. The techniques are classified into client-based techni

抒情短诗

Distributed Speech Recognition Standardscreated to ensure interoperability between the feature extraction running on a client device and a compatible recogniser running on a remote server. They are intended for use in the implementation of commercial services for speech and multimodal services over mobile networks. In the process of devel

自传

没花的是打扰

代替

Error Recovery: Channel Coding and Packetizationreless or network communication channels within DSR systems are typically noisy and bursty. Thus, DSR systems must utilize efficient Error Recovery (ER) schemes during transmission of speech information. Some ER strategies, referred to as forward error control (FEC), aim to create redundancy in the

谎言

Error Concealmenthe network. While there are good reasons for doing so, a disadvantage of this client-server architecture is clearly that the communication medium may introduce errors, which then impairs speech recognition accuracy. Even sophisticated channel coding cannot completely prevent the occurrence of residu

commodity

Algorithm Optimizations: Low Computational Complexity us to use ever more complex algorithms in server-based large vocabulary ASR systems (e.g. in telephony applications), the capability of embedded platforms will always lag behind. Nevertheless as the popularity of ASR application grows, we can expect an increasing demand for functionality on embedde

Cosmopolitan

Algorithm Optimizations: Low Memory Footprinttatic (long-term) and dynamic (run-time) in this chapter we focus mainly on the long-term memory requirements and, more specifically, on the techniques for acoustic model compression. As all compression methods, acoustic model compression is exploiting redundancies within the data as well as the lim