Parameter-embedded watermarking is effected through slight perturbations of parametric models of some deeply-integrated dynamics of the speech signal. In set-membership filtering (SMF) based parametric watermarking, linear predictor (LP) coefficients of the original speech are modified subject to an objective fidelity constraint. SMF is used to obtain sets of allowable parameter perturbations (i.e., watermarks) subject to a constraint on the error between the watermarked and original material. This paper discusses the robustness of SMF based watermarking to filtering, quantization and combination attacks. An important consideration in watermark robustness is the energy of the watermark signal (difference between watermarked and original signals). The most robust watermark is obtained from perturbed LP coefficients at the boundary of the membership set. A constrained optimization problem is solved to obtain the best watermarks for filtering and quantization attacks

Parametric watermarking is effected by modifying the linear predictor coefficients of speech. In this work, the parameter noise is analyzed when watermarked speech is subjected to additive white and colored noise in the time domain. The paper presents two detection techniques for parametric watermarking. The first approach uses the Neyman-Pearson criterion to solve a binary decision problem. In the second approach, discriminant functions based on the minimum-error-rate criterion are used to determine which one of the many watermarks was embedded or if no watermark is present. Experiments with speech data are used to determine the false-alarm and missed detection rates

Advances in formulating spoken document retrieval for a new National Gallery of the Spoken Word (NGSW) are addressed. NGSW is the first large-scale repository of its kind, consisting of speeches, news broadcasts, and recordings from the 20th century. After presenting an overview of the audio stream content of the NGSW, with sample audio files from U.S. Presidents from 1893 to the present, an overall system diagram is proposed with a discussion of critical tasks associated with effective audio information retrieval. These include advanced audio segmentation, speech recognition model adaptation for acoustic background noise and speaker variability, and information retrieval using natural language processing for text query requests that include document and query expansion. For segmentation, a new evaluation criterion entitled fused error score (FES) is proposed, followed by application of the CompSeg segmentation scheme on DARPA Hub4 Broadcast News (30.5% relative improvement in FES) and NGSW data. Transcript generation is demonstrated for a six-decade portion of the NGSW corpus. Novel model adaptation using structure maximum likelihood eigenspace mapping shows a relative 21.7% improvement. Issues regarding copyright assessment and metadata construction are also addressed for the purposes of a sustainable audio collection of this magnitude. Advanced parameter-embedded watermarking is proposed with evaluations showing robustness to correlated noise attacks. Our experimental online system entitled "SpeechFind" is presented, which allows for audio retrieval from a portion of the NGSW corpus. Finally, a number of research challenges such as language modeling and lexicon for changing time periods, speaker trait and identification tracking, as well as new directions, are discussed in order to address the overall task of robust phrase searching in unrestricted audio corpora.

A general formulation for speech watermarking through parametric modeling is suggested, then the paper focuses on a watermarking technique based on linear-predictive (LP) modeling of speech. In the particular strategy employed here, information is embedded by modifying the autocorrelation values of the original speech. The amount of information that can be embedded is subject to fidelity constraints. The modified LP coe#cients derived from the new set of autocorrelation values are used for reconstructing the watermarked speech. The perceptual quality of the watermarked speech depends on the relative energy of the embedded watermark, the watermark sequence used, and the LP model order. Robustness of the technique to various signal processing operations and attacks like compression, cropping, and additive noise are studied via experiments on a small speech application. Factors a#ecting watermark robustness and related security issues are also discussed.

Most digital watermarking techniques are susceptible to damage by data cropping. Although the effects of cropping might not be perceptible, watermark recovery may be rendered difficult or impossible due to the desynchronization of the recovery process. The transform encryption coding (TEC) based watermarking algorithm was presented at ICASSP 2000 (see Ruiz, F.J. and Deller, J.R., Jr, IEEE International Conference on Acoustics, Speech and Signal Processing 2000). This paper investigates the performance of TEC watermarking in the presence of cropping, and presents an algorithm that identifies cropped samples and recovers watermarks from the damaged record. Implementation details and experimental results under different environmental conditions are presented.