Switched Split Vector Quantization Research Articles

Robust coding of wideband speech immittance spectral frequencies

In this paper, we propose a reduced complexity stochastic joint source-channel coding system developed for efficient and robust coding of wideband speech ISF (Immittance Spectral Frequency) parameters. Initially, the aim of this encoding system was to achieve a transparent quantization of ISF parameters for ideal transmissions over a noiseless channels. It was designed based on the switched split vector quantization (SSVQ) technique and called “ISF-SSVQ coder”. After that, our interest was drawn to the improvement of the ISF-SSVQ robustness for transmissions over a noisy channel. To protect implicitly our ISF coders, we developed a stochastic joint source-channel coding system based on a reduced complexity version of the channel optimized SSVQ technique. Simulation results will show that our new encoding system, called ISF-SCOSSVQ coder, can provide a good implicit protection to ISF parameters. The ISF-SCOSSVQ coder was further used to encode the ISF parameters of the Adaptive Multi-rate Wideband (AMR-WB, ITU-T G.722.2) speech coder operating over a noisy channel. We will show that the proposed ISF-SCOSSVQ coder contribute significantly in the improvement of the AMR-WB performance by ensuring a good coding robustness of its ISF parameters.

GMM-Based KLT-Domain Switched-Split Vector Quantization for LSF Coding

For quantization of line spectral frequency (LSF), Gaussian mixture model (GMM) based switched split vector quantization (SSVQ) has been reported as the best performing intra-frame coding method. However, GMM-SSVQ partly recovers correlations between the subvectors of split vector quantization (SVQ). In the proposed GMM-SSVQ with the Karhunen-Loève Transform (KLT), KLT-domain quantization for each mixture with a novel region-clustering algorithm is applied to GMM-SSVQ. Compared with SVQ and GMM-SSVQ, it provides 4 and 1 bit higher performance in terms of average spectral distortion and outliers, respectively. Computational complexity and memory requirements are similar to GMM-SSVQ.

Speech Coding and Recognition

This paper investigates the performance of a speech recognizer in an interactive voice response system for various coded speech signals, coded by using a vector quantization technique namely Multi Switched Split Vector Quantization Technique. The process of recognizing the coded output can be used in Voice banking application. The recognition technique used for the recognition of the coded speech signals is the Hidden Markov Model technique. The spectral distortion performance, computational complexity, and memory requirements of Multi Switched Split Vector Quantization Technique and the performance of the speech recognizer at various bit rates have been computed. From results it is found that the speech recognizer is showing better performance at 24 bits/frame and it is found that the percentage of recognition is being varied from 100% to 93.33% for various bit rates. Keywords—Linear predictive coding, Speech Recognition, Voice banking, Multi Switched Split Vector Quantization, Hidden Markov Model, Linear Predictive Coefficients.

Reduced complexity two stage vector quantization

We address the issue of complexity for vector quantization (VQ) of wide-band speech LSF (line spectrum frequency) parameters. The recently proposed switched split VQ (SSVQ) method provides better rate-distortion (R/D) performance than the traditional split VQ (SVQ) method, even at the requirement of lower computational complexity, but at the expense of much higher memory. We develop the two stage SVQ (TsSVQ) method, by which we gain both the memory and computational advantages and still retain good R/D performance. The proposed TsSVQ method uses a full dimensional quantizer in its first stage for exploiting all the higher dimensional coding advantages and then, uses an SVQ method for quantizing the residual vector in the second stage so as to reduce the complexity. We also develop a transform domain residual coding method in this two stage architecture such that it further reduces the computational complexity. To design an effective residual codebook in the second stage, variance normalization of Voronoi regions is carried out which leads to the design of two new methods, referred to as normalized two stage SVQ (NTsSVQ) and normalized two stage transform domain SVQ (NTsTrSVQ). These two new methods have complimentary strengths and hence, they are combined in a switched VQ mode which leads to the further improvement in R/D performance, but retaining the low complexity requirement. We evaluate the performances of new methods for wide-band speech LSF parameter quantization and show their advantages over established SVQ and SSVQ methods.

Optimum switched split vector quantization of LSF parameters

We address the issue of rate–distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based optimum parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.

Switched Conditional PDF-Based Split VQ Using Gaussian Mixture Model

In this letter, we develop switched conditional PDF-based split vector quantization (SCSVQ) method using the recently proposed conditional PDF-based split vector quantizer (CSVQ). The use of CSVQ allows us to alleviate the coding loss by exploiting the correlation between subvectors, in each switching region. Using the Gaussian mixture model (GMM)-based parametric framework, we also address the rate-distortion (R/D) performance optimality of the proposed SCSVQ method by allocating the bits optimally among the switching regions. For the wideband speech line spectrum frequency (LSF) parameter quantization, it is shown that the optimum parametric SCSVQ method provides nearly 2 bits/vector advantage over the recently proposed nonparametric switched split vector quantization (SSVQ) method.

Conditional PDF-Based Split Vector Quantization of Wideband LSF Parameters

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The commonly used split vector quantization (SVQ) method is inferior to unconstrained quantization due to independent coding of the split subvectors, resulting in a coding loss. In this paper, we propose a conditional pdf-based split vector quantization (CSVQ) method to recover the coding loss. The CSVQ method is developed assuming the line spectrum frequency source distribution as a multivariate Gaussian and the subvectors are quantized sequentially to exploit the correlation between the subvectors. The new CSVQ method is evaluated for wideband speech LSF quantization; CSVQ is shown to outperform traditional SVQ and provide comparable performance to the recently proposed switched split vector quantization (SSVQ) method. In addition, the transform domain SVQ method is also realized to show that its performance is limited by the distance measure used in the transform domain. </para>