Introduction

Model 2 avoids linear scan of epoch file and the intonation file. Therefore this implementation is much faster and efficient compared to Model 1. A performance comparison will be given in next chapter where both the implementations are checked on some given input. For this application where pairs are to be retrieved efficiently, Extendible Hashing is the best data structure and GDBMCE uses extendible hashing.

There are two drawbacks in this model:

1. 
   The Tokens.txt file generated after hindianalyser phase is scanned linearly by the hindiengine dynamic link library to retrieve the token name and the token type. Refer to the implementation of fscanf using ReadFile in Model 1 section of this chapter. So in the next model to avoid this linear scan token name and token type are also saved in a GDBMCE database using an index value as key which starts from 0 and increases till there are more tokens.
   
2. 
   The second drawback of this model is that the sound library is still a directory which contains the wav files. In the next model the sound files are also kept in a GDBM database as the wav files also have the token name as the key and the value is the sound file. A directory with a number of wav files are replaced by this one file called voice.db. It facilitate the transfer of this single file to the Windows CE device. Also this single file can be provided on flash ram accompanying the application. Also making the voice database will also avoid linear scanning of voice files during the generation of the speech. Since now the voice file is saved on the database as a character array, efficient array operations like memcpy can be used to avoid the linear scan of the sound files that was done in the present model.
   

These drawbacks resulted into the development of a third model in which the voice database is added, along with the epoch database and the intonation database that were already there. The tokens are also saved in a database and no normal file operations are used in this version. That makes the third model as the most robust model out of the other two models. Let’s now take a look at the changes done in third model.