Robust speech recognition in burst-like packet loss
Robust speech recognition in burst-like packet loss
复制标题
突发丢包情况下的鲁棒语音识别
DOI：
10.1109/icassp.2001.940817
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
B. Milner
中科院分区：
文献类型：
--
作者：
B. Milner
本文研究了在移动和IP网络上执行语音识别相关的问题。主要问题是基于编解码器的失真和网络中丢包导致的语音矢量丢失。在三态马尔可夫模型的基础上，建立了一个实际的丢包模型，并证明该模型能够模拟丢包的突发性质。提出了一种两阶段的丢包检测和估计方案，并证明该方案可以提高特征向量丢失时的识别性能。Aurora数据库的结果表明，在丢包50%的情况下，突发型丢包将数字精度从99%降低到57%。丢包估计可使性能恢复到77%。
This paper examines problems associated with performing speech recognition over mobile and IP networks. The main problems are identified as codec-based distortion and from speech vectors being lost from packet loss in the network. A realistic model for packet loss is developed, based on a three state Markov model and is shown to be capable of simulating the burst-like nature of packet loss. A two stage packet loss detection and estimation scheme is proposed and is shown to improve the recognition performance in the event of feature vectors being lost. Results from the Aurora database show that burst-like packet loss reduces the digit accuracy from 99% to 57% at 50% packet loss. Estimation of the lost packets recovers the performance to 77%.