This paper presents methods for speaker adaptive modeling using vocal tract normalization (VTN) along with experimental tests on three databases. We propose a new training method for VTN: By using single-density acoustic models per HMM state for selecting the scale factor of the frequency axis, we avoid the problem that a mixture-density tends to learn the scale factors of the training speakers and thus cannot be used for selecting the scale factor. We show that using single Gaussian densities for selecting the scale factor in training results in lower error rates than using mixture densities. For the recognition phase, we propose an improvement of the well-known two-pass strategy: By using a nonnormalized acoustic model for the first recognition pass instead of a normalized model, lower error rates are obtained. In recognition tests, this method is compared with a fast variant of VTN. The two-pass strategy is an efficient method, but it is suboptimal because the scale factor and the word sequence are determined sequentially. We found that for telephone digit string recognition this suboptimality reduces the VTN gain in recognition performance by 30% relative. In summary, on the German spontaneous speech task Verbmobil, the WSJ task and the German telephone digit string corpus SieTill, the proposed methods for VTN reduce the error rates significantly.Index Terms-Speaker adaptive modeling and training, speaker adaptive recognition, speech recognition, vocal tract (length) normalization.

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Abstract-This paper presents a new method for estimating formant frequencies. The formant model is based on a digital resonator. Each resonator represents a segment of the short-time power spectrum. The complete spectrum is modeled by a set of digital resonators connected in parallel. An algorithm based on dynamic programming produces both the model parameters and the segment boundaries that optimally match the spectrum.We used this method in experimental tests that were carried out on the TI digit string data base. The main results of the experimental tests are: 1) the presented approach produces reliable estimates of formant frequencies across a wide range of sounds and speakers; and 2) the estimated formant frequencies were used in a number of variants for recognition. The best set-up resulted in a string error rate of 4.2% on the adult corpus of the TI digit string data base.

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This paper presents a new method for estimating formant frequencies. The formant model is based on a digital resonator. Each resonator represents a segment of the short-time power spectrum. The complete spectrum is modeled by a set of digital resonators connected in parallel. An algorithm based on dynamic programming produces both the model parameters and segment boundaries that optimally match the spectrum.The main results of this paper are: 1) Modeling formants by digital resonators allows a reliable estimation of formant frequencies. 2) Digital resonators can be used efficiently in connection with dynamic programming. 3) A recognition test with formant frequencies results in a string error rate of 4.8% on the adult corpus of the TI digit string database.