BROWSE
Detail
Journal Article
Weighted Finite State Transducer-Based Endpoint Detection Using Probabilistic Decision Logic
- Authors
- Hoon Chung, Sung Joo Lee, Yun Keun Lee
- Issue Date
- 2014-10
- Citation
- ETRI Journal, v.36, no.5, pp.714-720
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- In this paper, we propose the use of data-driven probabilistic utterance-level decision logic to improve Weighted Finite State Transducer (WFST)-based endpoint detection. In general, endpoint detection is dealt with using two cascaded decision processes. The first process is frame-level speech/non-speech classification based on statistical hypothesis testing, and the second process is a heuristic-knowledge-based utterance-level speech boundary decision. To handle these two processes within a unified framework, we propose a WFST-based approach. However, a WFST-based approach has the same limitations as conventional approaches in that the utterance-level decision is based on heuristic knowledge and the decision parameters are tuned sequentially. Therefore, to obtain decision knowledge from a speech corpus and optimize the parameters at the same time, we propose the use of data-driven probabilistic utterance-level decision logic. The proposed method reduces the average detection failure rate by about 14% for various noisy-speech corpora collected for an endpoint detection evaluation.
- KSP Keywords
- Based Approach, Cascaded decision, Data-Driven, Decision Knowledge, Decision logic, Finite state transducer, Frame-level, Non-speech, Probabilistic Decision, Speech classification, Speech corpora