多重描述量化碼書的索引指定設計

Abstract

本論文探討多重描述量化碼書之索引指定最佳化問題，進一步應用在無線隨意網路環境之分散式語音辨識系統。在系統的傳送端，多重描述量化器將語音參數量化，並根據事先定義的碼書索引指定產生兩個或多個描述，這些描述再通過多個互相獨立且無記憶性的通道傳送，在接收端則以一個符合最小期望擾動的解碼器將語音參數還原。傳統解決碼書索引指定最佳化的問題，是利用多重描述二進位置換演算法，它的做法是不斷地置換一對碼字的位置直到期望擾動收斂為止。為了要降低其運算複雜度，我們將索引指定設計轉換成符合線性規劃的雙方相配問題，並且提出多重描述亨格利演算法，藉此快速的建立最佳化的碼書索引指定。系統模擬是在不同的網路封包漏失環境中，利用國語數字串的分散式語音辨識進行碼書索引指定的性能評估。實驗結果顯示在隨機漏失和吉伯爾模型漏失的環境下，所提出的多重描述傳輸機制比其它單一描述傳輸方法具有更強健的性能表現，同時也利用無線隨意網路模擬平台評估分散式語音辨識的性能。

This study addresses the index assignment optimization for multiple description quantizers and its application to distributed speech recognition over Mobile Ad Hoc Networks (MANET). In the encoder, the speech parameters are quantized and mapped to two or more descriptions according to a predefined index assignment. After being transmitted over multiple independent channels, the decoder uses the received descriptions to reproduce the speech parameters. The optimization criterion and a practical approach based on the multiple description binary switching algorithm (MD-BSA) are first presented for the index assignment optimization. The basic idea of the MD-BSA is to switch a pair of codevectors recursively until the expected channel distortion can not be further reduced. In order to reduce its computational complexity, we formulated the index assignment problem on the basis of a linear programming framework and then proposed a fast local search algorithm based on the multiple description Hungarian algorithm (MD-HA). Experiments on the Mandarin digit string recognition task show that the proposed multiple description scheme outperforms single description methods in the presence of random and Gilbert-model packet losses. The ns-2 based MANET simulation was also conducted to examine the performance of the proposed multiple description transmission scheme.