Sixth International Conference on Spoken Language Processing (ICSLP 2000)
October 16-20, 2000
Large Vocabulary Continuous Speech Recognition Under Real Environments Using Adaptive Sub-Band Spectral Subtraction
Masahiro Fujimoto, Jun Ogata, Yasuo Ariki
Department of Electronics and Informatics,
Ryukoku University, Seta, Otsu-shi, Shiga, Japan
In this study, we propose an Adaptive Sub-Band Spectral
Subtraction (ASBSS) method which can vary noise
subtraction rate according to SNR in frequency bands at
each frame. In the conventional Spectral Subtraction(SS),
speech spectral is estimated by adjusting noise subtraction
rate according to SNR. In general, SNR is defined and
computed as the average over all the input speech signal.
However, even if the noise is stationary, SNR varies according
to speech energy. Therefore the subtraction rate
of noise spectral should be adjusted according to the segmental
SNR. This method is called Adaptive SS(ASS).
Considering difference of spectral features such as vowel
and consonant, the subtraction rate of noise spectral should
be adjusted according to the sub-band SNR. This idea leads
to the ASBSS method we propose in this paper. In order
to evaluate the proposed method, we carried out Large Vocabulary
Continuous Speech Recognition experiments and
compared the results by our method with the conventional
method in word accuracy.
Fujimoto, Masahiro / Ogata, Jun / Ariki, Yasuo (2000):
"Large vocabulary continuous speech recognition under real environments using adaptive sub-band spectral subtraction",
In ICSLP-2000, vol.1, 305-308.