William R. Gardner

Variable rate vocoder

A method of speech signal compression, by variable rate coding of frames of digitized speech samples, comprising the steps of: determining a level of speech activity for a frame of digitized speech samples; selecting an encoding rate from a set of rates based upon said determined level of speech activity for said frame; coding said frame according to a coding format of a set of coding formats for said selected rate wherein each rate has a corresponding different coding format and wherein each coding format provides for a different plurality of parameter signals representing said digitized speech samples in accordance with a speech model; and generating for said frame a data packet of said parameter signals.

QCELP: A Variable Rate Speech Coder for CDMA Digital Cellular

Digital cellular telephone systems require efficient encoding of speech to achieve capacity improvements required of the next generation of cellular systems. The use of a variable rate speech coder can reduce the average data rate required to transmit conversational speech by a factor of two or more, while providing many other advantages. This reduction in average data rate leads to a factor of two increase in the capacity of a Code Division Multiple Access, or CDMA, based digital cellular telephone system by decreasing the mutual interference among users. This chapter describes “QCELP,” a variable rate speech coder which has been selected as the speech coding algorithm for the TIA North American digital cellular standard based on CDMA technology.

Techniques for the regeneration of wideband speech from narrowband speech

This paper addresses the problem of reconstructing wideband speech signals from observed narrowband speech signals. The goal of this work is to improve the perceived quality of speech signals which have been transmitted through narrowband channels or degraded during acquisition. We describe a system, based on linear predictive coding, for estimating wideband speech from narrowband. This system employs both previously identified and novel techniques. Experimental results are provided in order to illustrate the systems ability to improve speech quality. Both objective and subjective criteria are used to evaluate the quality of the processed speech signals.

Low-Complexity Source Coding Using Gaussian Mixture Models, Lattice Vector Quantization, and Recursive Coding with Application to Speech Spectrum Quantization

In this paper, we use the Gaussian mixture model (GMM) based multidimensional companding quantization framework to develop two important quantization schemes. In the first scheme, the scalar quantization in the companding framework is replaced by more efficient lattice vector quantization. Low-complexity lattice pruning and quantization schemes are provided for the

Noncausal all-pole modeling of voiced speech

E_8

Joint source-channel decoding of speech spectrum parameters over erasure channels using Gaussian mixture models

Gossett lattice. At moderate to high bit rates, the proposed scheme recovers much of the space-filling loss due to the product vector quantizers (PVQ) employed in earlier work, and thereby, provides improved performance with a marginal increase in complexity. In the second scheme, we generalize the compression framework to accommodate recursive coding. In this approach, the joint probability density function (PDF) of the parameter vectors of successive source frames is modeled using a GMM. The conditional density of the parameter vector of the current source frame based on the quantized values of the parameter vector of the previous source frames is used to generate a new codebook for every current source frame. We demonstrate the efficacy of the proposed schemes in the application of speech spectrum quantization. The proposed scheme is shown to provide superior performance with moderate increase in complexity when compared with conventional one-step linear prediction based compression schemes for both narrow-band and wide-band speech.

Low complexity recursive coding of spectrum parameters

This paper introduces noncausal all-pole models that are capable of efficiently capturing both the magnitude and phase information of voiced speech. It is shown that noncausal all-pole filter models are better able to match both magnitude and phase information and are particularly appropriate for voiced speech due to the nature of the glottal excitation. By modeling speech in the frequency domain, the standard difficulties that occur when using noncausal all-pole filters are avoided. Several algorithms for determining the model parameters based on frequency-domain information and the masking effects of the ear are described. Our work suggests that high-quality voiced speech can be produced using a 14th-order noncausal all-pole model.

Iterative joint source-channel decoding of speech spectrum parameters over an additive white Gaussian noise channel

A joint source-channel decoding scheme that improves the performance of conventional channel decoders over erasure channels by exploiting the cross-correlation between successive speech frames is presented. Speech spectrum parameters are quantized using the scheme presented in Subramaniam and Rao (2001). The joint probability density function (PDF) of the spectrum parameters of successive speech frames is modelled using a Gaussian mixture model (GMM). This model is then used to process the channel decoder output over erasure channels. The performance of two decoding strategies, namely, maximum likelihood decoding (ML) and minimum mean squared error decoding (MMSE) is shown to provide significantly better performance than prediction based schemes.

Non-causal linear prediction of voiced speech

A low complexity vector quantization scheme for the recursive coding of speech spectrum parameters is proposed. In this scheme, the joint probability density function (pdf) of the spectrum parameters of successive speech frames is modelled using a Gaussian mixture model whose parameters are estimated using the Expectation Maximization (EM) algorithm. The conditional density of the spectrum parameters of the current speech frame based on the quantized values of the spectrum parameters of previous speech frames is used to generate a new codebook for every current speech frame. An efficient quantization scheme using transform coding and bit allocation techniques which allows easy and computationally efficient mapping from observation to quantized value is developed. Transparent quality speech for the first order fixed rate recursive coding case is achieved at 20 bits per frame.

Mixed-phase AR models for voiced speech and perceptual cost functions

In this paper, we show how the Gaussian mixture modeling framework used to develop efficient source encoding schemes can be further exploited to model source statistics during channel decoding in an iterative framework to develop an effective joint source-channel decoding scheme. The joint probability density function (PDF) of successive source frames is modeled as a Gaussian mixture model (GMM). Based on previous work, the marginal source statistics provided by the GMM is used at the encoder to design a low-complexity memoryless source encoding scheme. The source encoding scheme has the specific advantage of providing good estimates to the probability of occurrence of a given source code-point based on the GMM. The proposed iterative decoding procedure works with any channel code whose decoder can implement the soft-output Viterbi algorithm that uses a priori information (APRI-SOVA) or the BCJR algorithm to provide extrinsic information on each source encoded bit. The source decoder uses the GMM model and the channel decoder output to provide a priori information back to the channel decoder. Decoding is done in an iterative manner by trading extrinsic information between the source and channel decoders. Experimental results showing improved decoding performance are provided in the application of speech spectrum parameter compression and communication.