Topic
Cepstrum
About: Cepstrum is a research topic. Over the lifetime, 3346 publications have been published within this topic receiving 55742 citations.
Papers published on a yearly basis
Papers
More filters
14 May 2006
TL;DR: This work explores how two spectral noise estimation approaches can be applied in the context of model-based feature enhancement and shows that the resulting system achieves an accuracy on the Aurora2 task that is comparable to MBFE with prior knowledge on noise.
Abstract: Many compensation techniques, both in the model and feature domain, require an estimate of the noise statistics to compensate for the clean speech degradation in adverse environments. We explore how two spectral noise estimation approaches can be applied in the context of model-based feature enhancement. The minimum statistics method and the improved minima controlled recursive averaging method are used to estimate the noise power spectrum based only on the noisy speech. The noise mean and variance estimates are nonlinearly transformed to the cepstral domain and used in the Gaussian noise model of MBFE. We show that the resulting system achieves an accuracy on the Aurora2 task that is comparable to MBFE with prior knowledge on noise. Finally, this performance can be significantly cantly improved when the MS or IMCRA noise mean is reestimated based on a clean speech model.
15 citations
TL;DR: In this paper, the authors proposed a low-variance and adaptive-bandwidth spectral estimator for spectral subtraction, which is based on the two-stage spectral estimation (TSSE) and the modified cepstrum thresholding (MCT).
15 citations
07 May 1996
TL;DR: It was shown that the RCEP do contain useful information, in particular, they are complementary to the LPCC and Mel-scaled FFT based cepstrum (MFCC) was found to be superior to LPCC.
Abstract: In speech recognition based on LPC analysis the prediction residues are usually ignored, only the LPC-derived cepstral coefficients (LPCC) are used to compose feature vectors. In this study, a number of parameters (called the residual cepstrum or RCEP) were calculated from these residues and their effectiveness for speech recognition was evaluated. It was shown that the RCEP do contain useful information, in particular, they are complementary to the LPCC. In an evaluation experiment, if the LPCC were used jointly with a few RCEP coefficients, the recognition rate of the English E-set letters was improved from 54% to 67% and from 69% to 71% by the use of HMMs based recognizer and the DTW based recognizer, respectively. In addition, Mel-scaled FFT based cepstrum (MFCC) was found to be superior to LPCC.
15 citations
TL;DR: This study investigated the effect of additive noise and reverberation on speech on the basis of the concept of temporal modulation transfer and proposed a two-stage processing algorithm that adaptively normalizes the temporal modulation of speech to extract robust speech features for automatic speech recognition.
15 citations
24 Sep 1997
TL;DR: An algorithm for unsupervised speaker classification using Kohonen SOM is presented and correct classification of more than 90% was demonstrated.
Abstract: An algorithm for unsupervised speaker classification using Kohonen SOM is presented. The system employs 6/spl times/10 SOM networks for each speaker and for non-speech segments. The algorithm was evaluated using high quality as well as telephone quality conversations between two speakers. Correct classification of more than 90% was demonstrated. High quality conversation between three speakers yielded 80% correct classification. The high quality speech required the use of 12/sup th/ order cepstral coefficients vector. In telephone quality speech, an additional 12 features of the difference of the cepstrum were required.
15 citations