Title :
A linked-HMM model for robust voicing and speech detection
Abstract :
We present a novel method for simultaneous voicing and speech detection based on a linked-HMM architecture, with robust features that are independent of the signal energy. Because this approach models the change in dynamics between speech and nonspeech regions, it is robust to low sampling rates, significant levels of additive noise, and large distances from the microphone. We demonstrate the performance of our method in a variety of testing conditions and also compare it to other methods reported in the literature.
Keywords :
feature extraction; hidden Markov models; signal detection; signal sampling; speech processing; additive noise robustness; linked-HMM model; microphone distance; nonspeech regions; performance; robust features; robust voicing; sampling rate robustness; speech detection; speech regions; Acoustic noise; Additive noise; Energy measurement; Entropy; Hidden Markov models; Microphones; Noise robustness; Sampling methods; Speech enhancement; Testing;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
Print_ISBN :
0-7803-7663-3
DOI :
10.1109/ICASSP.2003.1198906