It is shown that, for a multiloop sigma-delta modulator driven by a DC input, the statistical behavior (described by long-term time averages) of the error process from an inner (multibit) quantizer is consistent with that of a signal-independent. white. uniform noise. The same result also holds for sinusoidal inputs, provided that the modulator has three or more loops. This behavior is exploited to obtain trade-offs between asymptotic system performances and oversampling ratios, for several sigma-delta modulator/decoding filter pairs. For an L-loop modulator with a simple sinc/sup L+1/ decoding filter, it is shown that doubling the oversampling ratio can result in an improvement of 3.01(2L+1) dB in the signal-to-quantization-noise ratio.>

A discrete-time model having a two-bit (2-b) quantizer is analyzed exactly, and an analytical expression for the quantizer noise sequence is found. Rigorous answers are then provided to two fundamental questions for a double-loop sigma-delta modulation system with DC input: (1) What is the long-term statistical behavior of the internal quantizer noise? (2) How does the asymptotic mean square sigma-delta quantization error vary as a function of the oversampling ratio?.>

New and simpler achievability proofs that are based on the backward decoding technique are presented for the well-known coding theorems for the multiple-access channel (MAC) with perfect feedback and the degraded relay channel. A class of MACs with different generalized feedback signals is also considered, and achievable-rate regions that are larger than those previously presented in the literature are established.>

An exact spectral analysis of oversampled discrete-time L-loop (L>or=2) sigma-delta modulator ( Sigma Delta M) with DC (constant) input is presented. It is shown by a novel application of ergodic theory that the embedded quantizer error sequence is asymptotically uniformly distributed, white and uncorrelated with the input, a fact usually assumed a priori in the linearized models. It was found that for the following linear demodulators: (1) optimum FIR (finite-impulse response) filter, (2) sinc/sup k/ filters (k>or=L+1), and (3) the ideal low-pass filter, the overall system ( Sigma Delta M plus demodulator) can provide a normalized average quantization noise power inversely proportional to the (2L+1)-st power of the oversampling ratio, confirming the performance superiority of multiloop Sigma Delta M over single-loop Sigma Delta M.>

The discrete short-time Fourier transform (DS TFT) has been widely used to study and analyze several speech signal characteristics. However, this Scheme has not been used as successfully in speech compression based on scalar quantization. On the other hand, most speech perception concepts have very interesting frequency-domain interpretations, which suggest the design of compression schemes based on frequency domain analysis. In this paper we apply vector quantization techniques for designing and simulating discrete short-time Fourier transform based speech compression systems. The main conclusion we can draw is that a low to medium rate compressor, in which the reproduction signal has at least communications quality, and in which the complexity is moderate, is at least in principle possible.

In many linear predictive coding (LPC) speech compression systems the encoding of the LPC parameters is performed using a product code book scheme. One possible approach consists in organizing the set of LPC reproduction models as the Cartesian product of a vector code book describing the shape of each reproduction LPC model and a scalar codebook describing the gain or energy. We first present a formal development of rate-distortion theoretic results for distortion functions based on quotients of input and reproduction symbols. We also obtain theoretical bounds for the rate-distortion function of the gain term of LPC systems (which, depending on the quantization scheme, can use about10-25percent of the transmission rate), as well as asymptotic (small distortions) approximations to the performance of the optimal scalar quantizer for these gain terms, when the overall fidelity criterion is the Itakura-Saito distortion measure, which is a member of the class of quotient distortion measures. The approximations and bounds are compared finally with experimental results.

A speaker-independent isolated word recognition system is described which is based on some techniques and results from rate-distortion speech coders. The recognition system can be viewed as a minimum distortion or nearest-neighbor system where the distortion measure is defined between an observed sequence of frames of speech and a reference pattern. The patterns are sequences of sets of LPC models. Every one of the sets of each pattern consist of a collection of LPC models that "best" reproduces a given frame of a word from a training sequence. The Itakura Saito distortion measure is used to design the system (or selection of the patterns) and for the decision step.

We describe an application of a recently developed speech compression technique to automatic recognition of isolated words (from a given dictionary). The scheme maps sampled speech, from a given word, into a finite codebook (of the same size as the dictionary) using linear predictive coding (LPC) all-pole models and a minimum distortion or nearest neighbor rule between all samples from the given word and the codewords from the codebook. Standard LPC techniques are used to design the codebook, but the final system requires no on-line LPC analysis.

With rare exception, all presently available narrowband speech coding systems implement scalar quantization (independent quantization) of the transmission parameters (such as reflection coefficients or transformed reflection coefficients in LPC systems). In this paper a new approach called Vector Quantizatlon is presented. For very low data rates, realistic experiments have shown that vector quantization can achieve a given level of average distortion with fifteen to twenty fewer bits per frame than that required for optimized scalar quantizing approachs presently in use.

An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data. The basic properties of the algorithm are discussed and demonstrated by examples. Quite general distortion measures and long blocklengths are allowed, as exemplified by the design of parameter vector quantizers of ten-dimensional vectors arising in Linear Predictive Coded (LPC) speech compression with a complicated distortion measure arising in LPC analysis that does not depend only on the error vector.

A training sequence of speech data is used to design a two-step speech compression system, based upon either single speakers or multiple speakers. The system is designed to minimize an average spectral distortion over the training sequence, leading to an identification step using linear prediction techniques followed by a vector quantizer. The system is then used to compress test sequences of speech data, leading to much lower bit rates than obtained using scalar quantization for equivalent distortions. For the same numerical distortion, 20-bits/frame were required using optimal scalar bit allocation and quantization, whereas 8-bits/frame were required using vector quantization. Results are presented in the form of numerical distortion measures and analog tapes of synthesized speech.