A method is described for estimating telephone handset nonlinearity by matching the spectral magnitude of the distorted signal to the output of a nonlinear channel model, driven by an undistorted reference. This "magnitude only" representation allows the model to directly match unwanted speech formants that arise over nonlinear channels and that are a potential source of degradation in speaker and speech recognition algorithms. As such, the method is particularly suited to algorithms that use only spectral magnitude information. The distortion model consists of a memoryless nonlinearity sandwiched between two finite-length linear filters. Nonlinearities considered include arbitrary finite-order polynomials and parametric sigmoidal functionals derived from a carbon-button handset model. Minimization of a mean-squared spectral magnitude distance with respect to model parameters relies on iterative estimation via a gradient descent technique. Initial work has demonstrated the importance of addressing handset nonlinearity, in addition to linear distortion, in speaker recognition over telephone channels. A nonlinear handset "mapping," applied to training or testing data to reduce mismatch between different types of handset microphone outputs, improves speaker verification performance relative to linear compensation only. Finally, a method is proposed to merge the mapper strategy with a method of likelihood score normalization (hnorm) for further mismatch reduction and speaker verification performance improvement.

Blind clustering of multi-person utterances by speaker is complicated by the fact that each utterance has at least two talkers. In the case of a two-person conversation, one can simply split each conversation into its respective speaker halves, but this introduces error which ultimately hurts clustering. We propose a clustering algorithm which is capable of associating each conversation with two clusters (and therefore two-speakers) obviating the need for splitting. Results are given for two speaker conversations culled from the Switchboard corpus, and comparisons are made to results obtained on single-speaker utterances. We conclude that although the approach is promising, our technique for computing inter-conversation similarities prior to clustering needs improvement.

A method is described for estimating telephone handset nonlinearity by matching the spectral magnitude of the distorted signal to the output of a nonlinear channel model, driven by an undistorted reference. This "magnitude-only" representation allows the model to directly match unwanted speech formants that arise over nonlinear channels and that are a potential source of degradation in speaker and speech recognition algorithms. As such, the method is particularly suited to algorithms that use only spectral magnitude information. The distortion model consists of a memoryless polynomial nonlinearity sandwiched between two finite-length linear filters. Minimization of a mean-squared spectral magnitude error, with respect to model parameters, relies on iterative estimation via a gradient descent technique, using a Jacobian in the iterative correction term with gradients calculated by finite-element approximation. Initial work has demonstrated the algorithm's usefulness in speaker recognition over telephone channels by reducing mismatch between high- and low-quality handset conditions.

An approach to the joint estimation of sine-wave amplitude modulation (AM) and frequency modulation (FM) is described based on the transduction of frequency modulation into amplitude modulation by linear filters, being motivated by the hypothesis that the auditory system uses a similar transduction mechanism in measuring sine-wave FM. An AM-FM estimation algorithm is described that uses the amplitude envelope of the output of two transduction filters of piecewise-linear spectral shape. The piecewise-linear constraint is then relaxed, allowing a wider class of transduction-filter pairs for AM-FM separation under a monotonicity constraint on the filters' quotient. The particular case of Gaussian filters is shown to yield a closed-form solution to AM-FM estimation while gammatone filters, used as a simplified model of auditory filters, and measured auditory filters, although not leading to a solution in a closed form, provide for iterative AM-FM estimation. Solution stability analysis and error evaluation are performed and the FM transduction method is compared with the energy separation algorithm, based on the Teager energy operator, and the Hilbert transform method for AM-FM estimation. Finally, a generalization to two-dimensional (2-D) filters is described.

A new approach to the joint estimation of sine-wave amplitude modulation (AM) and frequency modulation (FM) is presented based on the transduction of frequency modulation into amplitude modulation by digital filters, being motivated by the hypothesis that the auditory front end uses a similar transduction mechanism in measuring sine-wave FM. An AM-FM estimation algorithm is derived which uses the amplitude envelope of the output of two transduction filters of piecewise-linear spectral shape. The piecewise-linear constraint is then relaxed, allowing a wider class of transduction filters for AM-FM separation. The particular class of Gaussian filters is shown to yield a closed form solution to AM-FM estimation. Solution stability analysis and error evaluation are performed, and the FM transduction method is shown to typically compare favorably with the Teager energy operator and the Hilbert transform for AM-FM estimation. Finally, the amenability of cochlear gammatone-filter pairs to separate AM and FM is demonstrated.

The two largest factors affecting automatic speaker identification performance are the size of the population and the degradations introduced by noisy communication channels (e.g., telephone transmission). To examine experimentally these two factors, this paper presents text-independent speaker identification results for varying speaker population sizes up to 630 speakers for both clean, wideband speech and telephone speech. A system based on Gaussian mixture speaker models is used for speaker identification and experiments are conducted on the TIMIT and NTIMIT databases. This is believed to be the first speaker identification experiments on the complete 630 speaker TIMIT and NTIMIT databases and the largest text-independent speaker identification task reported to date. Identification accuracies of 99.5% and 60.7% are achieved on the TIMIT and NTIMIT databases, respectively. This paper also presents experiments which examine and attempt to quantify the performance loss associated with various telephone degradations by systematically degrading the TIMIT speech in a manner consistent with measured NTIMIT degradations and measuring the performance loss at each step. It is found that the standard degradations of filtering and additive noise do not account for all of the performance gap between the TIMIT and NTIMIT data. Measurements of nonlinear microphone distortions are also described which may explain the additional performance loss.

A design is presented for a full-duplex echo-cancelling data modem based on a combined adaptive reference echo canceller and adaptive channel equalizer. The adaptive reference algorithm has the advantage that interference to the echo canceller caused by the far-end signal can be eliminated by subtracting an estimate of the far-end signal based on receiver decisions. This technique provides a novel approach for full-duplex far-echo cancellation in which the far echo can be cancelled in spite of carrier-frequency offset. To estimate the frequency offset, the system uses a separate receiver structure for the far echo which provides equalization of the far echo channel and tracks the frequency offset in the far echo. The feasibility of the echo-cancelling algorithms is demonstrated by computer simulation with realistic channel distortions and with 4800-b/s data transmission, at which rate the frequency offset in the far echo becomes important.>

A Block Diagram Compiler (BOC) has been designed and implemented for converting graphic block diagram descriptions of signal processing tasks into source code to be executed on a Multiple Instruction Stream - Multiple Data Stream (MIMD) array computer. The compiler takes as input a block diagram of a real-time DSP application, entered on a graphics CAE workstation, and translates it into efficient real-time assembly language code for the target multiprocessor array. The current implementation produces code for a rectangular grid of Texas Instruments TMS32010 signal processors built at Lincoln Laboratory, but the concept could be extended to other processors or other geometries in the same way that a good assembly language programmer would write it. This report begins by examining the current implementation of the BOC including relevant aspects of the target hardware. Next, we describe the task-assignment module, which uses a simulated annealing algorithm to assign the processing tasks of the DSP application to individual processors in the array. Finally, our experiences with the current version of the BOC software and hardware are reported.

In this paper, we present a design for a full-duplex near-echo-cancelling data modem based on a cambined "decision-directed" (DD) adaptive echo canceller and adaptive channel equalizer. This algorithm has the important feature that the interference to the canceller caused by the far-end signal is eliminated by subtracting an estimate of the far-end signal based on receiver decisions. This technique is extended to provide a new approach to full-duplex far-echo cancellation in which frequency offset of the far echo is adaptively estimated with little interference from the far-end signal and near echo. The approach uses receiver decisions on the far-end signal, but in addition, relies on a separate receiver structure devoted to joint estimation of the far-echo channel and the frequency offset in the far echo. The feasibility of both echo-cancelling modem algorithms is demonstrated by computer simulation with realistic channel distortions and with 4800 bps data transmission at which rate frequency offset in the far echo becomes important.

A technique for local-area communication among a large number of digital speech terminals is presented. The terminals communicate over a common wide-band broadcast transmission medium using packets. Access to the medium is controlled by a carrier-sense multiple-access protocol with collision detection. Analytic results indicate that many speech terminals having different characteristics can be supported without the necessity of buffering more than one packet. Several distributed access control algorithms are described, and a technique for predicting the performance of these algorithms is presented. Computer simulations were performed to verify the feasibility of these ideas. The simulation results agree with the prediction of the analysis.