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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 10, NO. 8, NOVEMBER 2002

Fig. 5.

Noise-normalized SPLICE denoising using the iterative stochastic algorithm for tracking nonstationary noise in an utterance of the Aurora2 data with an

average SNR

  • 0

    dB. From top to bottom panels are noisy speech, clean speech, and denoised speech, all in the same spectrogram format.

Fig. 6.

Full set of noise-robust speech recognition results in the September-2001 Aurora2 evaluation, using the dynamic and noise-normalized SPLICE with the

noise estimation obtained from iterative stochastic approximation; Sets A, B, and C are separate test sets with different noise and channel distortion conditions. In (a) are the recognition rates using multicondition training mode where the denoising algorithm is applied to the training data set and the resulting denoised Mel-cepstral features are used to train the HMMs. In (b) are the recognition rates using the “clean” training model where the HMMs are trained using clean speech Mel-cepstra and the denoising algorithm is applied only to the test set. Reference curves in both (a) and (b) refer to the recognition rates obtained with no denoising

processing.

we can evaluate every possible combination of bits to subvec- tors and select the best according to a certain criterion. To better match the training procedure to the speech recognition task we use the criterion of minimal word error rate (WER). That is, bit

assignment is the result of the following constrained optimiza- tion:

(11)

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