“Bag of codes” based automatic speaker identification

The response time required in speaker identification systems mainly depend on the amount of enrolled speakers. Thus, how to reduce the computational cost, when evaluating large speaker databases, is the key problem. Thus, a ldquobag of codesrdquo algorithm is proposed, which can generate speaker models by estimating the probability distribution of each code in speech data. Experiments prove that the new configuration has substantially lower complexity than commonly used methods with comparable identification accuracy, overcoming one bottleneck in the development of speaker identification research.