# An Efficient VQ Codebook Search Algorithm Applied to AMR-WB Speech Coding
## 4 Experimental Results  
There are three experiments conducted in this work. The first is a search load comparison among various search approaches. The second is a quantization accuracy (QA) comparison among a full search and other search approaches. The third is a performance comparison among various approaches in terms of ITU-T P862 perceptual evaluation of speech quality (PESQ) [21] as an objective measure of speech quality. A speech database, completely different from all the training data, is employed for  
Figure 1: A plot of the average number of searches versus \(TQA\). outside testing purposes. With one male and one female speaker, the speech database in total takes up more than 221 MB of memory, occupies more than 120 min, and covers 363,281 speech frames. Firstly, Table 4 lists a comparison on the average number of searches among full search, multiple TIE (MTIE) [13], DI-TIE, and EEENNS, while Table 5 gives the search load corresponding to \(TQA\) values of the BSS-VQ algorithm. Moreover, with the search load required in the full search algorithm as a benchmark, Tables 6 and 7 present comparisons on the load reduction (LR) with respect to Tables 4 and 5. A high value of LR reflects a high search load reduction. Table 6 indicates that DI-TIE provides a higher value of LR than MTIE and EEENNS search approaches among all the codebooks. It is also found that most LR values of BSS-VQ are higher than the DI-TIE approach by an observation in Tables 6 and 7. For example, the LR values of BSS-VQ are indeed higher than DI-TIE in case the \(TQA\) is equal to or smaller than 0.99, 0.98, 0.96, and 0.99 in codebooks CB1, CB2, CB21, and CB22, respectively. Accordingly, a remarkable search load reduction is reached by the BSS-VQ search algorithm. In the QA aspect, a 100% QA is obtained by the MTIE, DI-TIE, and EEENNS algorithms as compared with a full search approach. Thus, only the QA experiment of BSS-VQ is conducted. The QA corresponding to \(TQA\) values of the BSS-VQ algorithm is given in Table 8. It reveals that QA is an approximation of \(TQA\) in either inside or outside testing cases. Moreover, this algorithm provides an LR between 77.78% and 93.98% at \(TQA\) = 0.90 as well as an LR between 67.23% and 88.39% at \(TQA\) = 0.99, depending on the codebooks. In other words, a trade-off can be made between the quantization accuracy and the search load. Furthermore, an overall LR is evaluated to observe the total search load of an entire VQ encoding procedure of an input vector. The overall LR refers to the total search load, defined as the sum of the average number of searches multiplied by the vector dimension in each codebook. Thus, an overall LR comparison with the full search as a benchmark is presented as a bar graph in Figure 2. As clearly indicated in Figure 2, the overall LR of BSS-VQ is higher than MTIE, DI-TIE, and EEENNS approaches, but at the same time the QA is as high as 0.98. Moreover, Table 9 gives a PESQ comparison, including the mean and the STD, among various approaches. Since MTIE, DI-TIE, and EEENNS provide a 100% QA, they both share the same PESQ with a full search, meaning that there is no deterioration in the speech quality. A close observation reveals little difference between PESQs obtained in a full search and in this search algorithm, that is, the speech quality is well maintained in BSS-VQ at \(TQA\) not less than 0.90. This BSS-VQ search algorithm is experimentally validated as a superior candidate relative to its counterparts.