A study of computation speed-UPS of the GMM-UBM speaker recognition system
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Summary
Using data from the Switchboard telephone speech corpus, it is shown that speed-ups can be obtained while sacrificing surprisingly little accuracy, and it is expected that these techniques, involving lowering model order as well as processing fewer speech frames, will apply equally well to other recognition systems.
- Type
- article
- Published
- 1999-09-05
- Cited by
- 98
- References
- 2
- OpenAlex
- https://openalex.org/W42176576
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2883334
Keywords
Computer science, Computation, Speaker recognition, Speech recognition, Artificial intelligence
References
Cited by
- Modeling intra-speaker variability for speaker recognition
- Real-time speaker identification
- Structural Gaussian mixture models for efficient text-independent speaker verification
- Efficient online cohort selection method for speaker verification
- The use of subvector quantization and discrete densities for fast GMM computation for speaker verification
- An adaptive speaker verification system with speaker dependent a priori decision thresholds
- Efficient speaker identification and retrieval
- Gaussian selection applied to text-independent speaker verification
- Text independent speaker recognition using speaker dependent word spotting
- Automatic spoken language identification utilizing acoustic and phonetic speech information
- System kontroli dostępu oparty na biometrycznej weryfikacji głosu
- Distributed systems: Principles and Paradigms
- Fast GMM computation for speaker verification using scalar quantization and discrete densities
- Model compression for GMM based speaker recognition systems
- Speaker indexing in audio archives using test utterance Gaussian mixture modeling
- Real-time speaker identification using speaker model distance
- Fast approach to speaker identification for large population using MLLR and sufficient statistics
- Efficient Speaker Recognition Using Approximated Cross Entropy (ACE)
- Voiceprint identification based on model clustering
- A fast two-level Speaker Identification method employing sparse representation and GMM-based methods
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