10445058
Time series classification using Gaussian mixture models of reconstructed phase spaces
A new signal classification approach is presented that is based upon modeling the dynamics of a system as they are captured in a reconstructed phase space. The modeling is done using full covariance Gaussian mixture models of time domain signatures, in contrast with current and previous work in signal classification that is typically focused on either linear systems analysis using frequency content or simple nonlinear machine learning models such as artificial neural networks. The proposed approach has strong theoretical foundations based on dynamical systems and topological theorems, resulting in a signal reconstruction, which is asymptotically guaranteed to be a complete representation of the underlying system, given properly chosen parameters. The algorithm automatically calculates these parameters to form appropriate reconstructed phase spaces, requiring only the number of mixtures, the signals, and their class labels as input. Three separate data sets are used for validation, including motor current simulations, electrocardiogram recordings, and speech waveforms. The results show that the proposed method is robust across these diverse domains, significantly outperforming the time delay neural network used as a baseline.
26304890
Speech signal enhancement through adaptive wavelet thresholding
This paper demonstrates the application of the Bionic Wavelet Transform (BWT), an adaptive wavelet transform derived from a non-linear auditory model of the cochlea, to the task of speech signal enhancement. Results, measured objectively by Signal-to-Noise ratio (SNR) and Segmental SNR (SSNR) and subjectively by Mean Opinion Score (MOS), are given for additive white Gaussian noise as well as four different types of realistic noise environments. Enhancement is accomplished through the use of thresholding on the adapted BWT coefficients, and the results are compared to a variety of speech enhancement techniques, including Ephraim Malah filtering, iterative Wiener filtering, and spectral subtraction, as well as to wavelet denoising based on a perceptually scaled wavelet packet transform decomposition. Overall results indicate that SNR and SSNR improvements for the proposed approach are comparable to those of the Ephraim Malah filter, with BWT enhancement giving the best results of all methods for the noisiest (-10db and -5db input SNR) conditions. Subjective measurements using MOS surveys across a variety of 0db SNR noise conditions indicate enhancement quality competitive with but still lower than results for Ephraim Malah filtering and iterative Wiener filtering, but higher than the perceptually scaled wavelet method.
1340064
Sub-banded reconstructed phase spaces for speech recognition
A novel method combining filter banks and reconstructed phase spaces is proposed for the modeling and classification of speech. Reconstructed phase spaces, which are based on dynamical systems theory, have advantages over spectral-based analysis methods in that they can capture nonlinear or higher-order statistics. Recent work has shown that the natural measure of a reconstructed phase space can be used for modeling and classification of phonemes. In this work, sub-banding of speech, which has been examined for recognition of noise-corrupted speech, is studied in combination with phase space reconstruction. This sub-banding, which is motivated by empirical psychoacoustical studies, is shown to dramatically improve the phoneme classification accuracy of reconstructed phase space-based approaches. Experiments that examine the performance of fused sub-banded reconstructed phase spaces for phoneme classification are presented. Comparisons against a cepstral-based classifier show that the proposed approach is competitive with state-of-the-art methods for modeling and classification of phonemes. Combination of cepstral-based features and the sub-band RPS features shows improvement over a cepstral-only baseline.
8126535
An adaptive approach for texture modelling
Markov random fields (MRFs) continue to be an important and useful representation for modelling textured images. Standard methods for MRF image modeling make use of the equivalent Gibbs distribution (GD) to express the joint probabilities of groups of neighboring pixels. The authors investigate a new approach to the use of the GD in image modeling. Specifically, they develop an adaptive approach to the formation of clique potential functions for the distribution. Traditional tools, such as the multi-level logistic (MLL) model, have been based on the use of a predetermined and identical set of potential functions. In the present paper it is shown that by incorporating additional parameters into the model in order to control the shape of these functions, it is possible to arrive at a more complete parametrization of the image. A simple model based on this concept is described and implemented, and image simulations using the well-known Gibbs sampler algorithm are constructed to demonstrate the usefulness of an adaptive set of potential functions.<<ETX>>
958169
Third-Order Moments of Filtered Speech Signals for Robust Speech Recognition
Novel speech features calculated from third-order statistics of subband-filtered speech signals are introduced and studied for robust speech recognition. These features have the potential to capture nonlinear information not represented by cepstral coefficients. Also, because the features presented in this paper are based on the third-order moments, they may be more immune to Gaussian noise than cepstrals, as Gaussian distributions have zero third-order moments. Experiments on the AURORA2 database studying these features in combination with Mel-frequency cepstral coefficients (MFCC's) are presented, and some improvement over the MFCC-only baseline is shown when clean speech is used for training, though the same improvement is not seen when multi-condition training data is used.
5925726
A Resource Sensitive Interpretation of Lexical Functional Grammar
This paper investigates whether the fundamental linguistic insights and intuitions of Lexical Functional Grammar (LFG), which is usually presented as a “constraint-based” linguistic theory, can be reformulated in a “resource sensitive” framework using a substructural modal logic. In the approach investigated here, LFG's f-descriptions are replaced with expressions from a multi-modal propositional logic (with permutation and possibly limited contraction). In effect, the feature structure “unification” basis of LFG's f-structures is replaced with a very different resource based mechanism. It turns out that some linguistic analyses that required non-monotonic devices in LFG (such as the “constraint equations” in the Andrews (1982) analysis of Icelandic) can be straightforwardly expressed in the framework presented here. Moreover, a Curry–Howard correspondence between proofs in this logic and λ-terms provides a semantic interpretation as a by-product of the process of showing syntactic well-formedness.
5454540
Edit Detection and Parsing for Transcribed Speech
We present a simple architecture for parsing transcribed speech in which an edited-word detector first removes such words from the sentence string, and then a standard statistical parser trained on transcribed speech parses the remaining words. The edit detector achieves a misclassification rate on edited words of 2.2%. (The NULL-model, which marks everything as not edited, has an error rate of 5.9%.) To evaluate our parsing results we introduce a new evaluation metric, the purpose of which is to make evaluation of a parse tree relatively indifferent to the exact tree position of EDITED nodes. By this metric the parser achieves 85.3% precision and 86.5% recall.
907916
Contextual Dependencies in Unsupervised Word Segmentation
Developing better methods for segmenting continuous text into words is important for improving the processing of Asian languages, and may shed light on how humans learn to segment speech. We propose two new Bayesian word segmentation methods that assume unigram and bigram models of word dependencies respectively. The bigram model greatly outperforms the unigram model (and previous probabilistic models), demonstrating the importance of such dependencies for word segmentation. We also show that previous probabilistic models rely crucially on sub-optimal search procedures.
62761494
Evolving visual routines
Traditional machine vision assumes that the vision system recovers a complete, labeled description of the world [10]. Recently, several researchers have criticized this model and proposed an alternative model that considers perception as a distributed collection of task-specific, context-driven visual routines [1, 12]. Some of these researchers have argued that in natural living systems these visual routines are the product of natural selection [11]. So far, researchers have hand-coded task-specific visual routines for actual implementations (e.g., [3]). In this article we propose an alternative approach in which visual routines for simple tasks are created using an artificial evolution approach. We present results from a series of runs on actual camera images, in which simple routines were evolved using genetic programming techniques [7]. The results obtained are promising: The evolved routines are able to process correctly up to 93% of the test images, which is better than any algorithm we were able to write by hand.
2681981
Steganalysis of recorded speech
Digital audio provides a suitable cover for high-throughput steganography. At 16 bits per sample and sampled at a rate of 44,100 Hz, digital audio has the bit-rate to support large messages. In addition, audio is often transient and unpredictable, facilitating the hiding of messages. Using an approach similar to our universal image steganalysis, we show that hidden messages alter the underlying statistics of audio signals. Our statistical model begins by building a linear basis that captures certain statistical properties of audio signals. A low-dimensional statistical feature vector is extracted from this basis representation and used by a non-linear support vector machine for classification. We show the efficacy of this approach on LSB embedding and Hide4PGP. While no explicit assumptions about the content of the audio are made, our technique has been developed and tested on high-quality recorded speech.