Fast likelihood computation techniques in nearest-neighbor based search for continuous speech recognition

IEEE Signal Processing Letters

Volumn 8, Issue 8, 2001, Pages 221-224

  Pellom, Bryan L ^a   Sarikaya, Ruhi ^a   Hansen, John H L ^a  

^a UNIVERSITY OF COLORADO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL COMPLEXITY; FEATURE EXTRACTION; GAUSSIAN NOISE (ELECTRONIC); MARKOV PROCESSES; MAXIMUM LIKELIHOOD ESTIMATION; SIGNAL DISTORTION; VECTOR QUANTIZATION;

GAUSSIAN SPEECH RECOGNITION SYSTEMS;

CONTINUOUS SPEECH RECOGNITION;

EID: 0035424821     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/97.935736     Document Type: Article

References (8)

1. Fast likelihood computation for continuous-mixture densities using a tree-based nearest neighbor search
2. Analysis and improvement of the partial distance search algorithm
3. Vector quantization for efficient computation of continuous density likelihoods
4. State-based Gaussian selection in large vocabulary continuous speech recognition using HMMs
5. The bucket box intersection (BBI) algorithm for fast approximative evaluation of diagonal mixture Gaussians
6. An improvement of the minimum distortion encoding algorithm for vector quantization
7. Fast likelihood computation methods for continuous mixture densities in large vocabulary speech recognition
8. A comparison of several acoustic representations for speech recognition with degraded and undegraded speech