A Bayesian Approach to Estimation of Speaker Normalization Parameters
Table 3: Table of specifications for front end signal processing used in the experiments of speech recognition
['[BOLD] Parameter', '[BOLD] Default Value']
The databases are divided into two parts, training and testing, as discussed earlier in this section. For Commodity database, the training data consists of 5899 utterances [∼30.5 hrs] and testing data consists of 1532 utterances [∼7.5 hrs], whereas the training and testing databases for District database consists of 3798 [∼20 hrs] and 985 [∼5 hrs] utterances respectively. Experiments using context independent phonemes have been carried out. The phonemes in each database are represented using three state left to right HMM. Further, each state of the HMM is modelled using a mixture of 16 Gaussian densities. The experiments are performed using the Sphinx3 toolkit. Note that the conducted experiments are gender independent i.e. the normalization parameters are extracted without gender information of the speaker. This method was adopted to increase usability of the system in real life scenario where such information may not be available.