Online learning of large margin hidden Markov models for automatic speech recognition
Explore this paper's citation graph
Summary
This dissertation explores the use of sequential, mistake-driven updates for online learning and acoustic feature adaptation in large margin HMMs, and finds that online updates for large margin training not only converge faster than analogous batch optimizations, but also yield lower phone error rates than approaches that do not attempt to enforce a large margin.
- Type
- article
- Published
- 2011-01-01
- Cited by
- 3
- References
- 127
- OpenAlex
- https://openalex.org/W68992276
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:27114424
Keywords
Margin (machine learning), Discriminative model, Hidden Markov model, Computer science, Speech recognition
References
- Support vector machines for multi-class pattern recognition
- Penalty function maximization for large margin HMM training
- Maximum conditional mutual information projection for speech recognition
- A fast online algorithm for large margin training of continuous density hidden Markov models
- Stochastic gradient adaptation of front-end parameters
- Adaptation of front end parameters in a speech recognizer
- Dimensionality reduction for speech recognition using neighborhood components analysis
- Automatic Speech Recognition - A Brief History of the Technology Development
- Online Learning of Approximate Dependency Parsing Algorithms
- Discriminative adaptation for log-linear acoustic models
- Optimization methods for discriminative training
- Estimation of Dependences Based on Empirical Data
- Extracting Support Data for a Given Task
- MLLR transforms as features in speaker recognition
- Neural networks for pattern recognition
- Discriminative kernel-based phoneme sequence recognition
- Fundamentals of speech recognition
- The Sound Pattern of English
- On the Learnability and Design of Output Codes for Multiclass Problems
- Discriminant Analysis by Gaussian Mixtures
Cited by
Related papers
- SVMs for Automatic Speech Recognition: A Survey
- Energy-Based Models in Document Recognition and Computer Vision
- Hope and Fear for Discriminative Training of Statistical Translation Models
- Automatic Speaker Recognition with Limited Data
- Scalable inference in max-margin topic models
- Advanced state clustering for very large vocabulary HMM-based on-line handwriting recognition
- Training Continuous Space Language Models: Some Practical Issues
- On updates that constrain the features' connections during learning
- Joint Feature Selection in Distributed Stochastic Learning for Large-Scale Discriminative Training in SMT