Linear Spectral Domain Research Articles

Vortex solitons in moiré optical lattices.

We show that optical moiré lattices enable the existence of vortex solitons of different types in self-focusing Kerr media. We address the properties of such states both in lattices having commensurate and incommensurate geometries (i.e., constructed with Pythagorean and non-Pythagorean twist angles, respectively), in the different regimes that occur below and above the localization-delocalization transition. We find that the threshold power required for the formation of vortex solitons strongly depends on the twist angle and, also, that the families of solitons exhibit intervals where their power is a nearly linear function of the propagation constant and they exhibit a strong stability. Also, in the incommensurate phase above the localization-delocalization transition, we found stable embedded vortex solitons whose propagation constants belong to the linear spectral domain of the system.

Cepstrum-Domain Model Combination Based on Decomposition of Speech and Noise Using MMSE-LSA for ASR in Noisy Environments

This paper presents an efficient method for combining models of speech and noise for robust speech recognition applications in noisy environments. This method decomposes the cepstrum domain representation of noise-corrupted speech into clean speech cepstrum and background noise cepstrum components using a minimum mean squared error-log spectral amplitude (MMSE-LSA) criterion. Speech recognition is then performed on noisy cepstrum domain observations using a model that is formed by parallel combination of cepstrum domain clean speech distributions and background noise distributions estimated using this MMSE-LSA based noise decomposition. This method is far more efficient than other parallel model combination (PMC) procedures because model combination is performed directly in the cepstrum domain rather than in the linear spectral domain. Whereas background noise model estimation is addressed as a separate issue in existing PMC procedures, this method explicitly incorporates a mechanism to continually update background noise models and signal-to-noise ratio (SNR) estimates over time. The performance of the proposed cepstrum-domain model combination method is compared with a well known implementation of PMC which uses a log-normal approximation when combining speech and background noise model means and variances on a connected digit string recognition task which is subjected to mismatched channel and environment conditions. As a result, it is shown that the proposed model combination technique gives a word error rate that is comparable to PMC when background noise information and SNR are known prior to estimation. The paper will also present the results of experiments where a combination of cepstrum-domain feature compensation and model combination are applied to this task.

Transforming Binary Uncertainties for Robust Speech Recognition

Recently, several algorithms have been proposed to enhance noisy speech by estimating a binary mask that can be used to select those time-frequency regions of a noisy speech signal that contain more speech energy than noise energy. This binary mask encodes the uncertainty associated with enhanced speech in the linear spectral domain. The use of the cepstral transformation smears the information from the noise dominant time-frequency regions across all the cepstral features. We propose a supervised approach using regression trees to learn the nonlinear transformation of the uncertainty from the linear spectral domain to the cepstral domain. This uncertainty is used by a decoder that exploits the variance associated with the enhanced cepstral features to improve robust speech recognition. Systematic evaluations on a subset of the Aurora4 task using the estimated uncertainty show substantial improvement over the baseline performance across various noise conditions.

Unsupervised incremental adaptation using maximum likelihood spectral transformation

A maximum likelihood spectral transformation (MLST) technique is proposed for rapid speech recognition under mismatched training and testing conditions. Speech feature vectors of real-time utterances are transformed in a linear spectral domain such that a likelihood of the utterances is increased after the transformation. Cepstral vectors are computed from the transformed spectra. The MLST function used for the spectral transformation is configured to handle both convolutional and additive noise. Since the function has small number of parameters to be estimated, only a few utterances are required for accurate adaptation, thus essentially eliminating the need for training speech data. Furthermore, the computation for parameter estimation and spectral transformation can be done efficiently in linear time. Therefore, the techniques of the present invention are well-suited for rapid online adaptation.

Feature transform in linear spectral domain for fast channel adaptation

A feature transform algorithm is proposed using the maximum likelihood spectral transform method for robust speech recognition. The feature transform compensates for the mismatch between clean training data and noisy testing data. A new technique is proposed to estimate a transform matrix for the feature transform in the linear spectral domain with a small number of parameters, which allows fast nonlinear adaptation to be realised.

Fast channel adaptation for continuous density HMMs using maximum likelihood spectral transform

A transform-based adaptation algorithm for robust speech recognition in unknown environments is proposed. In the cepstral domain, it is difficult to handle environmental noise. The proposed approach deals with such noise in the linear spectral domain, so that a small number of parameters can be used for fast adaptation.

Method of model adaptation for noisy speech recognition by transformation between cepstral and linear spectral domains

A method of model adaptation for noisy speech recognition determines the cepstral mean vector and covariance matrix of adapted noisy speech from the cepstral mean vectors and covariance matrices of speech and noise. The cepstral mean vectors of noise and speech are first transferred into the linear spectral domain, respectively. The linear spectral mean vectors of noise and speech are then combined to obtain a linear spectral mean vector of noisy speech. Next, the linear spectral mean vector of noisy speech is transferred from the linear spectral domain into the cepstral domain, so as to determine the cepstral mean vector of adapted noisy speech. Further, the cepstral covariance matrices of speech and noise are multiplied by a first and a second scaling factor, respectively, and the multiplied cepstral covariance matrices are combined together, so as to determine the cepstral covariance matrix of adapted noisy speech.

Iterative noise and channel estimation under the stochastic matching algorithm framework

In this letter, we introduce an unsupervised iterative algorithm to adapt HMMs trained using clean speech in order to recognize speech corrupted by an additive and a convolutional noise. Both types of noise are considered as stochastic processes that can be modeled using HMMs and can be estimated by applying Sankar's stochastic matching (SM) algorithm successively in the cepstral and in the linear spectral domain. These estimates are derived directly from the given test speech signal and the set of clean speech models, and lead to the estimation of a new set of HMMs that maximize the likelihood of the test signal.