상세정보
학술대회
Data-Driven Subvector Clustering using the Cross-Entropy Method
- 저자
- 정규준, 조훈영, 오영환
- 발행일
- 200704
- 출처
- International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2007, pp.IV977-IV980
- 협약과제
- 07MW1700, 신성장동력산업용 대용량 대화형 분산 처리 음성인터페이스 기술개발, 이윤근
- 초록
- Automatic Speech Recognition(ASR) systems are limited in the computational power and memory resources, especially in low-memory /low-power environments such as personal digital assistants. The parameter quantization is the one of the ways to achieve these conditions. In this work, we compare various subvector clustering procedures for the parameter quantization in the ASR system and propose a data-driven subvector clustering technique based on the entropy minimization. The Cross-Entropy(CE) method is a good choice for the combinatorial optimization problems. We compare the ASR performance on Resource Management(RM) speech recognition task and show that the proposed technique produces better performance than previous heuristic techniques. © 2007 IEEE.
- KSP 제안 키워드
- Clustering Technique, Computational Power, Data-Driven, Entropy minimization, Heuristic techniques, Low-Power, Parameter quantization, Personal digital assistant(PDA), Resource management, automatic speech recognition(ASR), combinatorial optimization problems(COPs)