Application of artificial neural network techniques to low bit-rate speech coding

Research has been undertaken to investigate the use of artificial neural network (ANN) techniques to improve the performance of a low bit-rate vector transform coder. Considerable improvements in the perceptual quality of the coded speech have been obtained. New ANN-based methods for vector quantiser (VQ) design and for the adaptive updating of VQ codebook are introduced for use in speech coding applications.

Coding speech signals at very low rates (below 1 kb/s) with high intelligibility

This thesis presents an original approach to parametric speech coding at rates below 1 kbitsjsec, primarily for speech storage applications. Essential processes considered in this research encompass efficient characterization of evolutionary configuration of vocal tract to follow phonemic features with high fidelity, representation of speech excitation using minimal parameters with minor degradation in naturalness of synthesized speech, and finally, quantization of resulting parameters at the nominated rates. For encoding speech spectral features, a new method relying on Temporal Decomposition (TD) is developed which efficiently compresses spectral information through interpolation between most steady points over time trajectories of spectral parameters using a new basis function. The compression ratio provided by the method is independent of the updating rate of the feature vectors, hence allows high resolution in tracking significant temporal variations of speech formants with no effect on the spectral data rate. Accordingly, regardless of the quantization technique employed, the method yields a high compression ratio without sacrificing speech intelligibility. Several new techniques for improving performance of the interpolation of spectral parameters through phonetically-based analysis are proposed and implemented in this research, comprising event approximated TD, near-optimal shaping event approximating functions, efficient speech parametrization for TD on the basis of an extensive investigation originally reported in this thesis, and a hierarchical error minimization algorithm for decomposition of feature parameters which significantly reduces the complexity of the interpolation process. Speech excitation in this work is characterized based on a novel Multi-Band Excitation paradigm which accurately determines the harmonic structure in the LPC (linear predictive coding) residual spectra, within individual bands, using the concept 11 of Instantaneous Frequency (IF) estimation in frequency domain. The model yields aneffective two-band approximation to excitation and computes pitch and voicing with high accuracy as well. New methods for interpolative coding of pitch and gain contours are also developed in this thesis. For pitch, relying on the correlation between phonetic evolution and pitch variations during voiced speech segments, TD is employed to interpolate the pitch contour between critical points introduced by event centroids. This compresses pitch contour in the ratio of about 1/10 with negligible error. To approximate gain contour, a set of uniformly-distributed Gaussian event-like functions is used which reduces the amount of gain information to about 1/6 with acceptable accuracy. The thesis also addresses a new quantization method applied to spectral features on the basis of statistical properties and spectral sensitivity of spectral parameters extracted from TD-based analysis. The experimental results show that good quality speech, comparable to that of conventional coders at rates over 2 kbits/sec, can be achieved at rates 650-990 bits/sec.

Low bit-rate speech encoding for digital mobile radio

The need for low bit-rate speech coding is the result of growing demand on the available radio bandwidth for mobile communications both for military purposes and for the public sector. To meet this growing demand it is required that the available bandwidth be utilized in the most economic way to accommodate more services. Two low bit-rate speech coders have been built and tested in this project. The two coders combine predictive coding with delta modulation, a property which enables them to achieve simultaneously the low bit-rate and good speech quality requirements. To enhance their efficiency, the predictor coefficients and the quantizer step size are updated periodically in each coder. This enables the coders to keep up with changes in the characteristics of the speech signal with time and with changes in the dynamic range of the speech waveform. However, the two coders differ in the method of updating their predictor coefficients. One updates the coefficients once every one hundred sampling periods and extracts the coefficients from input speech samples. This is known in this project as the Forward Adaptive Coder. Since the coefficients are extracted from input speech samples, these must be transmitted to the receiver to reconstruct the transmitted speech sample, thus adding to the transmission bit rate. The other updates its coefficients every sampling period, based on information of output data. This coder is known as the Backward Adaptive Coder. Results of subjective tests showed both coders to be reasonably robust to quantization noise. Both were graded quite good, with the Forward Adaptive performing slightly better, but with a slightly higher transmission bit rate for the same speech quality, than its Backward counterpart. The coders yielded acceptable speech quality of 9.6kbps for the Forward Adaptive and 8kbps for the Backward Adaptive.

Two Stage Transform Vector Quantization of LSFs for Wideband Speech Coding

We investigate the use of a two stage transform vector quantizer (TSTVQ) for coding of line spectral frequency (LSF) parameters in wideband speech coding. The first stage quantizer of TSTVQ, provides better matching of source distribution and the second stage quantizer provides additional coding gain through using an individual cluster specific decorrelating transform and variance normalization. Further coding gain is shown to be achieved by exploiting the slow time-varying nature of speech spectra and thus using inter-frame cluster continuity (ICC) property in the first stage of TSTVQ method. The proposed method saves 3-4 bits and reduces the computational complexity by 58-66%, compared to the traditional split vector quantizer (SVQ), but at the expense of 1.5-2.5 times of memory.

On the Impact of Low-Rate Attacks

Recent research have exposed new breeds of attacks that are capable of denying service or inflicting significant damage to TCP flows, without sustaining the attack traffic. Such attacks are often referred to as "low-rate" attacks and they stand in sharp contrast against traditional Denial of Service (DoS) attacks that can completely shut off TCP flows by flooding an Internet link. In this paper, we study the impact of these new breeds of attacks and the extent to which defense mechanisms are capable of mitigating the attack's impact. Through adopting a simple discrete-time model with a single TCP flow and a nonoblivious adversary, we were able to expose new variants of these low-rate attacks that could potentially have high attack potency per attack burst. Our analysis is focused towards worst-case scenarios, thus our results should be regarded as upper bounds on the impact of low-rate attacks rather than a real assessment under a specific attack scenario.

Phoneme analysis of cochlear implant recipients' responses to CNC words using the MPEAK and SPEAK speech coding strategies of the Nucleus cochlear implant system

This paper discusses a study done to determine how cochlear implant users perceive speech sounds using MPEAK or SPEAK speech coding strategy.

Low rate of methicillin-resistant coagulase-positive staphylococcal colonization of veterinary personnel in Hong Kong

Elevated rates of methicillin-resistant Staphylococcus aureus (MRSA) carriage have been reported in veterinary personnel, suggesting an occupational colonization risk. Hong Kong veterinary personnel (n = 150) were sampled for coagulase-positive staphylococci (CPS) nasal colonization. Risk factors for colonization were assessed by questionnaire. Isolates were identified and antibiotic susceptibility determined. All CPS isolates were investigated for mecA carriage, SCCmec type and PVL genes. Two subjects were colonized with methicillin-resistant CPS: one with MRSA (spa type t002 (CC5), SCCmec type II) and one with methicillin-resistant Staphylococcus pseudintermedius (MRSP) (MLST type ST71, SCCmec type II-III). MLST type ST71 S. pseudintermedius strain is the predominant MRSP clone circulating in dogs in Europe and in Hong Kong. The low MR-CPS colonization rate may be associated with low levels of large animal exposure or low rates of MRSA colonization of companion animals in Hong Kong. Colonization with non-aureus CPS, which may cause human infection, must also be considered in veterinary personnel.

Comparison of musical activities of cochlear implant users with different speech-coding strategies

Speech coding might have an impact on music perception of cochlear implant users. This questionnaire study compares the musical activities and perception of postlingually deafened cochlear implant users with three different coding strategies (CIS, ACE, SPEAK) using the Munich Music Questionnaire. Overall, the self-reported perception of music of CIS, SPEAK, and ACE users did not differ by very much.

Spectral coding methods for speech compression and speaker identification

This thesis investigates aspects of encoding the speech spectrum at low bit rates, with extensions to the effect of such coding on automatic speaker identification. Vector quantization (VQ) is a technique for jointly quantizing a block of samples at once, in order to reduce the bit rate of a coding system. The major drawback in using VQ is the complexity of the encoder. Recent research has indicated the potential applicability of the VQ method to speech when product code vector quantization (PCVQ) techniques are utilized. The focus of this research is the efficient representation, calculation and utilization of the speech model as stored in the PCVQ codebook. In this thesis, several VQ approaches are evaluated, and the efficacy of two training algorithms is compared experimentally. It is then shown that these productcode vector quantization algorithms may be augmented with lossless compression algorithms, thus yielding an improved overall compression rate. An approach using a statistical model for the vector codebook indices for subsequent lossless compression is introduced. This coupling of lossy compression and lossless compression enables further compression gain. It is demonstrated that this approach is able to reduce the bit rate requirement from the current 24 bits per 20 millisecond frame to below 20, using a standard spectral distortion metric for comparison. Several fast-search VQ methods for use in speech spectrum coding have been evaluated. The usefulness of fast-search algorithms is highly dependent upon the source characteristics and, although previous research has been undertaken for coding of images using VQ codebooks trained with the source samples directly, the product-code structured codebooks for speech spectrum quantization place new constraints on the search methodology. The second major focus of the research is an investigation of the effect of lowrate spectral compression methods on the task of automatic speaker identification. The motivation for this aspect of the research arose from a need to simultaneously preserve the speech quality and intelligibility and to provide for machine-based automatic speaker recognition using the compressed speech. This is important because there are several emerging applications of speaker identification where compressed speech is involved. Examples include mobile communications where the speech has been highly compressed, or where a database of speech material has been assembled and stored in compressed form. Although these two application areas have the same objective - that of maximizing the identification rate - the starting points are quite different. On the one hand, the speech material used for training the identification algorithm may or may not be available in compressed form. On the other hand, the new test material on which identification is to be based may only be available in compressed form. Using the spectral parameters which have been stored in compressed form, two main classes of speaker identification algorithm are examined. Some studies have been conducted in the past on bandwidth-limited speaker identification, but the use of short-term spectral compression deserves separate investigation. Combining the major aspects of the research, some important design guidelines for the construction of an identification model when based on the use of compressed speech are put forward.

SUB-BAND CODING TECHNIQUES FOR ROBUST 4. 8 TO 8 kb/s SPEECH CODERS.

This paper describes a speech coding technique that has been developed in order to provide a method of digitising speech at bit rates in the range 4. 8 to 8 kb/s, that is insensitive to the effects of acoustic background noise and bit errors on the digital link. The main aim has been to develop a coding scheme which provides speech quality and robustness against noise and errors that is similar to a 16000 b/s continuously variable slope delta (CVSD) coder, but which operates at half its data rate or less. A desirable aim was to keep the complexity of the coding scheme within the scope of what could reasonably be handled by current signal processing chips or by a single custom integrated circuit. Applications areas include mobile radio and small Satcomms terminals.