In this paper, the estimation of speech AR parameters under noisy conditions is revisited. The EM algorithm serving this purpose was first proposed by Gannot et al. We present an extensive experimental study along with a new approach to implement the E-step of the algorithm. The new realization of the E-step uses matrix computations instead of a Kalman filter. By appropriate rearrangement of the E-step, the complexity O(P(p +q)3)of the Kalman filter approach has been reduced to O(P log P), where P is the frame length, p is the speech order and q is the noise order. In practice, a speed up of the E-step of at least two orders of magnitude has been achieved. An extensive evaluation of the algorithm shows that EM algorithm in its base form is unable to improve over a recent speech enhancement method proposed by Heusdens et al. and over an established Spectral Subtraction with Minimum Statistics method, as measured by various quality measures. However, with some modification it was possible to improve over these methods in terms of spectral distortion.

We consider adaptive two-channel multiple-description coding. We provide an analytical method for designing a resolution-constrained symmetrical multiple-description coder that uses an index assignment matrix. We use existing index assignment algorithms that are known for their good properties within our adaptive multiple-description coding architecture. These existing index assignment algorithms are parameterized and the coefficient of quantization of the side coders is described by a rational function. This leads to an analytical solution for the design problem, facilitating real-time adaptation based on information provided by feedback channels or rate conditions imposed by network management. Our experimental results show that the practical performance closely approximates theoretically obtained optimal behavior.

Speech coding algorithms that have been developed for clean speech are often used in a noisy environment. We describe maximum a posteriori (MAP) and minimum mean square error (MMSE) techniques to estimate the clean-speech short-term predictor (STP) parameters from noisy speech. The MAP and MMSE estimates are obtained using a likelihood function computed by means of the DFT or Kalman filtering and empirical probability distributions based on multidimensional histograms. The method is assessed in terms of the resulting root mean spectral distortion between the "clean" speech STP parameters and the STP parameters computed with the proposed method from noisy speech. The estimated parameters are also applied to obtain clean speech estimates by means of a Kalman filter. The quality of the estimated speech as compared to the "clean" speech is assessed by means of subjective tests, signal-to-noise ratio improvement, and the perceptual speech quality measurement method

We propose a method to estimate the short term predictor (STP) and the long-term predictor (LTP) under noisy conditions. We assume the speech signal to be a single, dual or triple frame asymptotic mean stationary process. The a priori STP parameter distribution is represented as databases sampled from the speech training data. Stochastic integration is used to obtain the minimum mean square error estimates of the STP parameters. After computing the STP parameters, the LTP parameters from a database of pairs of taps and excitation variances are matched, together with the lag, using a likelihood criterion, to the noisy speech. The estimated STP and LTP parameters are also applied to obtain clean speech estimates by means of a Wiener or a Kalman filter. For car noise with an SNR of -5dB, the proposed enhancement method gives a mean opinion score of 3.3 as measured using the perceptual speech quality measure software.

Minimum mean square error (MMSE) estimation of the speech short-term predictor (STP) parameters in the line spectral frequency (LSF) representation is considered. We exploit that the square error between LSF parameter vectors is a subjectively meaningful distortion criterion. As speech coding algorithms are often used in a noisy environment, it is relevant to estimate the STP parameters used in these algorithms under the inclusion of noise statistics. In our experiments, car noise is used as an example of an autoregressive (AR) noise process. The MMSE estimates are obtained using a likelihood function computed by means of Kalman filtering and empirical probability distributions. The method is assessed in terms of the resulting root mean spectral distortion between the 'clean' speech STP parameters and the STP parameters computed using the proposed method from noisy speech.

In this paper, we consider the estimation of short-term predictor (STP) parameters under noisy conditions. The possible autoregressive spectral shapes of the speech and additive noise are stored in AR-coefficient codebooks. The product codebook is then searched to maximize the likelihood function of the observed noisy speech signal frame. The maximum likelihood (ML) estimates of the variances of the driving term are computed for each pair of the speech and noise AR spectra. For further processing (e.g., Kalman filtering or speech coding using enhanced STP parameters), the spectra and variances that yield the maximum of the likelihood function are selected. To evaluate the proposed method, the estimates of the spectral shapes and variances are compared with those computed from clean speech signal using a common spectral distortion measure. Globally maximizing the likelihood function over some restricted region of the parameter space, the presented approach provides robust estimates.

In the conventional hidden Markov model, the model parameters are reestimated by an iterative procedure known as the Baum-Welch method. This paper proposes an alternative procedure using fuzzy estimation, which is generalised from the fuzzy c-means and the BaumWelch methods. An extension of this approach, which uses a garbage state to deal with outlier data is also proposed. Experiments using the TI46 speech data corpus show this approach can be applicable to speech and speaker recognition.