Title :
Keyword verification considering the correlation of succeeding feature vectors
Author :
Junkawitsch, Jochen ; Höge, Harald
Author_Institution :
Corp. Res. & Technol., Siemens AG, Munich, Germany
Abstract :
The assumption of statistically independent feature vectors within the HMM approach is a well known problem. The aim of this study is to explore a simple and feasible method, that takes the correlation of adjacent feature vectors into account. A so called correlated HMM, that estimates the emission probability of a state with respect to correlated feature vectors, is built by combining two separate knowledge sources. On the one side, a traditional HMM provides an emission probability under the condition of a certain state, whereas on the other side a linear predictor delivers an emission probability considering the previous feature vectors. The efficiency of this method is shown with the help of the German SpeechDat(M) database. The application of the correlated HMM within the verification procedure of a keyword spotter provided an improvement of the figure-of-merit from 87.1% to 88.6%
Keywords :
correlation methods; feature extraction; hidden Markov models; prediction theory; probability; speech recognition; German SpeechDat(M) database; correlated HMM; correlated feature vectors; efficiency; emission probability; figure-of-merit; keyword spotter; keyword verification; knowledge sources; linear predictor; probability density functions; speech recognition; statistically independent feature vectors; succeeding feature vectors correlation; Acoustic emission; Design methodology; Feature extraction; Hidden Markov models; Probability distribution; Spatial databases; State estimation; Vectors; Yield estimation;
Conference_Titel :
Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-4428-6
DOI :
10.1109/ICASSP.1998.674407
