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.

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$E_8$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.

In this paper, a low-complexity, high-quality recursive vector quantizer based on a generalized hidden Markov model of the source is presented. Capitalizing on recent developments in vector quantization based on Gaussian mixture models, we extend previous work on HMM-based quantizers to the case of continuous vector-valued sources, and also formulate a generalization of the standard HMM. This leads us to a family of parametric source models with very flexible modelling capabilities, with which are associated low-complexity recursive quantization structures. The performance of these schemes is demonstrated for the problem of wideband speech spectrum quantization, and shown to compare favorably to existing state-of-the-art schemes.

We show how the Gaussian mixture modelling 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 modelled 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) 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.

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.

A computationally efficient, high quality, vector quantization scheme based on a parametric probability density function (PDF) is proposed. In this scheme, the observations are modeled as i.i.d realizations of a multivariate Gaussian mixture density. The mixture model parameters are efficiently estimated using the expectation maximization (EM) algorithm. A low complexity quantization scheme using transform coding and bit allocation techniques which allows for easy mapping from observation to quantized value is developed for both fixed rate and variable rate systems. An attractive feature of this method is that source encoding using the resultant codebook involves very few searches and its computational complexity is minimal and independent of the rate of the system. Furthermore, the proposed scheme is bit scalable and can switch seamlessly between a memoryless quantizer and a quantizer with memory. The usefulness of the approach is demonstrated for speech coding where Gaussian mixture models are used to model speech line spectral frequencies. The performance of the memoryless quantizer is 1-3 bits better than conventional quantization schemes.

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.

A computationally efficient, high quality, vector quantization scheme based on a parametric probability density function (PDF) is proposed. In this scheme, speech line spectral frequencies (LSF) are modeled as i.i.d realizations of a multivariate Gaussian mixture density. The mixture model parameters are efficiently estimated using the expectation maximization (EM) algorithm. An efficient quantization scheme using transform coding and bit allocation techniques which allows for easy and computationally efficient mapping from observation to quantized value is developed for both fixed rate and variable rate systems. An attractive feature of this method is that source encoding using the resultant codebook involves very few searches and its computational complexity is minimal and independent of the rate of the system. Furthermore, the proposed scheme is bit scalable and can switch between memoryless and quantizer with memory seamlessly. The performance of the memoryless quantizer is 2-3 bits better than conventional quantization schemes.