A Novel Method for Indexing and Retrieval of Speech using Gaussian Mixture Model Techniques
Explore this paper's citation graph
Summary
A novel method for indexing and retrieval of the speech using Gaussian mixture models and the average Probability density function is computed for each of the feature vectors in the database and the retrieval is based on the highest probability.
- Type
- article
- Published
- 2016-08-16
- Cited by
- 0
- References
- 27
- Access
- Open access
- OpenAlex
- https://openalex.org/W2509326865
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:63693176
Keywords
Computer science, Search engine indexing, Information retrieval, Gaussian, Speech recognition
References
- Searching for dominant high-level features for Music Information Retrieval
- Optimizing digital speech coders by exploiting masking properties of the human ear
- Query word retrieval from continuous speech using GMM posteriorgrams
- Semantic inference based on neural probabilistic language modeling for speech indexing
- Interactive spoken content retrieval by extended query model and continuous state space Markov Decision Process
- PNCC features and FNN - MAP compensation techniques for continuous speech recognition
- An efficient multimodal person authentication system using Gabor and sub band coding
- Optimum nonnegative integer bit allocation for wavelet based signal compression and coding
- Fusing heterogeneous traffic data by Kalman filters and Gaussian mixture models
- Voice activation detection using Teager-Kaiser energy measure
- Speech/music indexing for audio life-logs from portable device record
- Speech and text query based Tamil - English Cross Language Information Retrieval system
- Gaussian mixture models with class-dependent features for speech emotion recognition
- Time-frequency Analysis of Musical Rhythm
- Power-Normalized Cepstral Coefficients (PNCC) for robust speech recognition
- Generic Audio Classification Using a Hybrid Model Based on GMMs and HMMs
- Partially Supervised Speaker Clustering
- Anti-noise Power Normalized Cepstral Coefficients for Robust Environmental Sounds Recognition in Real Noisy Conditions
- Partial mutual information based input variable selection for supervised learning approaches to voice activity detection
- Power-Normalized Cepstral Coefficients (PNCC) for Robust Speech Recognition
Cited by
No citing papers recorded for this paper.
Related papers
- A closer look on indexing and indexing parameters
- A Review on Indexing Techniques and its application in Multilingual Information Retrieval System
- Analysis in indexing: document and domain centered approaches
- Indexing: Another way for authors to communicate
- The problems of Chinese Library Classification(CLC) in indexing of scientific and technical journals and the indexing principles
- Discussion on the Accurate Modeling of Cylindrical Indexing Cam
- Multiple Entry Indexing and Double Indexing
- The NLM Indexing Initiative