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Adaptive short-time analysis-synthesis for speech enhancement

Published in:
2008 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 31 March - 4 April 2008.

Summary

In this paper we propose a multiresolution short-time analysis method for speech enhancement. It is well known that fixed resolution methods such as the traditional short-time Fourier transform do not generally match the time-frequency structure of the signal being analyzed resulting in poor estimates of the speech and noise spectra required for enhancement. This can lead to the reduction of quality in the enhanced signal through the introduction of artifacts such as musical noise. To counter these limitations, we propose an adaptive short-time analysis-synthesis scheme for speech enhancement in which the adaptation is based on a measure of local time-frequency concentration. Synthesis is made possible through a modified overlap-add procedure. Empirical results using voiced speech indicate a clear improvement over a fixed time-frequency resolution enhancement scheme both in terms of mean-square error and as indicated by informal listening tests.
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Summary

In this paper we propose a multiresolution short-time analysis method for speech enhancement. It is well known that fixed resolution methods such as the traditional short-time Fourier transform do not generally match the time-frequency structure of the signal being analyzed resulting in poor estimates of the speech and noise spectra...

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A covariance kernel for SVM language recognition

Published in:
ICASSP 2008, Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 31 March - 4 April 2008, pp. 4141-4144.

Summary

Discriminative training for language recognition has been a key tool for improving system performance. In addition, recognition directly from shifted-delta cepstral features has proven effective. A recent successful example of this paradigm is SVM-based discrimination of languages based on GMM mean supervectors (GSVs). GSVs are created through MAP adaptation of a universal background model (UBM) GMM. This work proposes a novel extension to this idea by extending the supervector framework to the covariances of the UBM. We demonstrate a new SVM kernel including this covariance structure. In addition, we propose a method for pushing SVM model parameters back to GMM models. These GMM models can be used as an alternate form of scoring. The new approach is demonstrated on a fourteen language task with substantial performance improvements over prior techniques.
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Summary

Discriminative training for language recognition has been a key tool for improving system performance. In addition, recognition directly from shifted-delta cepstral features has proven effective. A recent successful example of this paradigm is SVM-based discrimination of languages based on GMM mean supervectors (GSVs). GSVs are created through MAP adaptation of...

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A multi-class MLLR kernel for SVM speaker recognition

Published in:
Proc. IEEE Int. Connf. on Acoustics, Speech and Signal Processing, ICASSP, 31 March - 4 April 2008, pp. 4117-4120.

Summary

Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM) is adapted to each utterance yielding a set of features that are used in an SVM. We consider the case where the UBM is a Gaussian mixture model (GMM), and maximum likelihood linear regression (MLLR) adaptation is used to adapt the means of the UBM. Recent work has examined this setup for the case where a global MLLR transform is applied to all the mixture components of the GMM UBM. This work produced positive results that warrant examining this setup with multi-class MLLR adaptation, which groups the UBM mixture components into classes and applies a different transform to each class. This paper extends the MLLR/GMM framework to the multiclass case. Experiments on the NIST SRE 2006 corpus show that multi-class MLLR improves on global MLLR and that the proposed system?s performance is comparable with state of the art systems.
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Summary

Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM) is adapted to each utterance yielding a set of features that are used in an SVM. We consider the case where the UBM is a...

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Exploiting temporal change in pitch in formant estimation

Published in:
Proc. IEEE Int. Conf. on Acoustic, Speech, and Signal Processes, ICASSP, 31 March - 4 April 2008, pp. 3929-3932.

Summary

This paper considers the problem of obtaining an accurate spectral representation of speech formant structure when the voicing source exhibits a high fundamental frequency. Our work is inspired by auditory perception and physiological modeling studies implicating the use of temporal changes in speech by humans. Specifically, we develop and assess signal processing schemes aimed at exploiting temporal change of pitch as a basis for formant estimation. Our methods are cast in a generalized framework of two-dimensional processing of speech and show quantitative improvements under certain conditions over representations derived from traditional and homomorphic linear prediction. We conclude by highlighting potential benefits of our framework in the particular application of speaker recognition with preliminary results indicating a performance gender-gap closure on subsets of the TIMIT corpus.
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Summary

This paper considers the problem of obtaining an accurate spectral representation of speech formant structure when the voicing source exhibits a high fundamental frequency. Our work is inspired by auditory perception and physiological modeling studies implicating the use of temporal changes in speech by humans. Specifically, we develop and assess...

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Language recognition with discriminative keyword selection

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 31 March - 4 April 2008, pp. 4145-4148.

Summary

One commonly used approach for language recognition is to convert the input speech into a sequence of tokens such as words or phones and then to use these token sequences to determine the target language. The language classification is typically performed by extracting N-gram statistics from the token sequences and then using an N-gram language model or support vector machine (SVM) to perform the classification. One problem with these approaches is that the number of N-grams grows exponentially as the order N is increased. This is especially problematic for an SVM classifier as each utterance is represented as a distinct N-gram vector. In this paper we propose a novel approach for modeling higher order Ngrams using an SVM via an alternating filter-wrapper feature selection method. We demonstrate the effectiveness of this technique on the NIST 2007 language recognition task.
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Summary

One commonly used approach for language recognition is to convert the input speech into a sequence of tokens such as words or phones and then to use these token sequences to determine the target language. The language classification is typically performed by extracting N-gram statistics from the token sequences and...

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Multisensor very low bit rate speech coding using segment quantization

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 31 March - 4 April 2008, pp. 3997-4000.

Summary

We present two approaches to noise robust very low bit rate speech coding using wideband MELP analysis/synthesis. Both methods exploit multiple acoustic and non-acoustic input sensors, using our previously-presented dynamic waveform fusion algorithm to simultaneously perform waveform fusion, noise suppression, and crosschannel noise cancellation. One coder uses a 600 bps scalable phonetic vocoder, with a phonetic speech recognizer followed by joint predictive vector quantization of the error in wideband MELP parameters. The second coder operates at 300 bps with fixed 80 ms segments, using novel variable-rate multistage matrix quantization techniques. Formal test results show that both coders achieve equivalent intelligibility to the 2.4 kbps NATO standard MELPe coder in harsh acoustic noise environments, at much lower bit rates, with only modest quality loss.
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Summary

We present two approaches to noise robust very low bit rate speech coding using wideband MELP analysis/synthesis. Both methods exploit multiple acoustic and non-acoustic input sensors, using our previously-presented dynamic waveform fusion algorithm to simultaneously perform waveform fusion, noise suppression, and crosschannel noise cancellation. One coder uses a 600 bps...

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Improved GMM-based language recognition using constrained MLLR transforms

Author:
Published in:
Proc. 33rd IEEE Int. Conf. on Acoustics, Speech, and SIgnal Processing, ICASSP, 30 March - 4 April 2008, pp. 4149-4152.

Summary

In this paper we describe the application of a feature-space transform based on constrained maximum likelihood linear regression for unsupervised compensation of channel and speaker variability to the language recognition problem. We show that use of such transforms can improve baseline GMM-based language recognition performance on the 2005 NIST Language Recognition Evaluation (LRE05) task by 38%. Furthermore, gains from CMLLR are additive with other modeling enhancements such as vocal tract length normalization (VTLN). Further improvement is obtained using discriminative training, and it is shown that a system using only CMLLR adaption produces state-of-the-art accuracy with decreased test-time computational cost than systems using VTLN.
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Summary

In this paper we describe the application of a feature-space transform based on constrained maximum likelihood linear regression for unsupervised compensation of channel and speaker variability to the language recognition problem. We show that use of such transforms can improve baseline GMM-based language recognition performance on the 2005 NIST Language...

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Experimental demonstration of remote optical detection of trace explosives.

Published in:
SPIE Vol. 6954, Chemical, Biologica, Radiological, Nuclear and Explosives (CBRNE) Sensing IX, 18-20 March 2008, 695407.

Summary

MIT Lincoln Laboratory has developed a concept that could enable remote (10s of meters) detection of trace explosives' residues via a field-portable laser system. The technique relies upon laser-induced photodissociation of nitro-bearing explosives into vibrationally excited nitric oxide (NO) fragments. Subsequent optical probing of the first vibrationally excited state at 236 nm yields narrowband fluorescence at the shorter wavelength of 226 nm. With proper optical filtering, these photons provide a highly sensitive explosives signature that is not susceptible to interference from traditional optical clutter sources (e.g., red-shifted fluorescence). Quantitative measurements of trace residues of TNT have been performed demonstrating this technique using a breadboard system, which relies upon a pulsed optical parametric oscillator (OPO) based laser. Based on these results, performance projections for a fieldable system are made.
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Summary

MIT Lincoln Laboratory has developed a concept that could enable remote (10s of meters) detection of trace explosives' residues via a field-portable laser system. The technique relies upon laser-induced photodissociation of nitro-bearing explosives into vibrationally excited nitric oxide (NO) fragments. Subsequent optical probing of the first vibrationally excited state at...

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Analytic theory of power law graphs

Author:
Published in:
SIAM Conference on Parallel Processing for Scientific Computing

Summary

An analytical theory of power law graphs is presented basedon the Kronecker graph generation technique. The analysisuses Kronecker exponentials of complete bipartite graphsto formulate the sub-structure of such graphs. This allows various high level quantities (e.g. degree distribution,betweenness centrality, diameter, eigenvalues, and isoparametric ratio) to be computed directly from the model pa-rameters. The implications of this work on “clustering”and “dendragram” heuristics are also discussed.
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Summary

An analytical theory of power law graphs is presented basedon the Kronecker graph generation technique. The analysisuses Kronecker exponentials of complete bipartite graphsto formulate the sub-structure of such graphs. This allows various high level quantities (e.g. degree distribution,betweenness centrality, diameter, eigenvalues, and isoparametric ratio) to be computed directly from the...

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Integration of high-speed surface-channel charge coupled devices into an SOI CMOS process using strong phase shift lithography

Published in:
SPIE Vol. 6924, Optical Microlithography XXI, 26-27 February 2008, pp. 69244R.

Summary

To enable development of novel signal processing circuits, a high-speed surface-channel charge coupled device (CCD) process has been co-integrated with the Lincoln Laboratory 180-nm RF fully depleted silicon-on-insulator (FDSOI) CMOS technology. The CCDs support charge transfer clock speeds in excess of 1 GHz while maintaining high charge transfer efficiency (CTE). Both the CCD and CMOS gates are formed using a single-poly process, with CCD gates isolated by a narrow phase-shift-defined gap. CTE is strongly dependent on tight control of the gap critical dimension (CD). In this paper we review the tradeoffs encountered in the co-integration of the CCD and CMOS technologies. The effect of partial coherence on gap resolution and pattern fidelity is discussed. The impact of asymmetric bias due to phase error and phase shift mask (PSM) sidewall effects is presented, along with adopted mitigation strategies. Issues relating to CMOS pattern fidelity and CD control in the double patterning process are also discussed. Since some signal processing CCD structures involve two-dimensional transfer paths, many required geometries present phase compliance and trim engineering challenges. Approaches for implementing noncompliant geometries, such as T shapes, are described, and the impact of various techniques on electrical performance is discussed.
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Summary

To enable development of novel signal processing circuits, a high-speed surface-channel charge coupled device (CCD) process has been co-integrated with the Lincoln Laboratory 180-nm RF fully depleted silicon-on-insulator (FDSOI) CMOS technology. The CCDs support charge transfer clock speeds in excess of 1 GHz while maintaining high charge transfer efficiency (CTE)...

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