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Variational inference and learning for segmental switching state space models of hidden speech dynamics
- Source :
- ICASSP (1)
- Publication Year :
- 2003
- Publisher :
- IEEE, 2003.
-
Abstract
- This paper describes novel and powerful variational EM algorithms for the segmental switching state space models used in speech applications, which are capable of capturing key internal (or hidden) dynamics of natural speech production. Hidden dynamic models (HDMs) have recently become a class of promising acoustic models to incorporate crucial speech-specific knowledge and overcome many inherent weaknesses of traditional HMMs. However, the lack of powerful and efficient statistical learning algorithms is one of the main obstacles preventing them from being well studied and widely used. Since exact inference and learning are intractable, a variational approach is taken to develop effective approximate algorithms. We have implemented the segmental constraint crucial for modeling speech dynamics and present algorithms for recovering hidden speech dynamics and discrete speech units from acoustic data only. The effectiveness of the algorithms developed are verified by experiments on simulation and Switchboard speech data.
- Subjects :
- Speech production
Computer science
business.industry
Bayesian network
Inference
Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)
Machine learning
computer.software_genre
Speech processing
Speech enhancement
Computer Science::Sound
State space
Artificial intelligence
business
Hidden Markov model
computer
Natural language
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).
- Accession number :
- edsair.doi...........e49f340d5efa897928ece0685a6b535d