Variable Rate Speech Coding Research Articles

The Voice Activity Detection Algorithm Based on Spectral Entropy and High-Order Statistics

The voice activity detection is one of the key technologies of variable rate speech coding. The development of speech coding technology requires higher performance of the detection. Based on the analysis of spectral entropy and high-order statistics of the basic definition and property of the foundation, this article proposes a voice activity detection algorithm which combines spectral entropy with high-order statistics. The algorithm can effectively detect the speech and non-speech segments, and can get reasonable results in a complex background noise environment.

A Statistical Approach for Voiced Speech Detection

Detection of Voice in speech signal is a challenging problem in developing high-performance systems used in noisy environments. In this paper, we present an efficient algorithm for robust voiced speech detection and for the application to variable-rate speech coding. The key idea of the algorithm is considering speech energy and zero crossings rate (ZCR) information simultaneously when processing speech signals and finding the end point of the signal. Next to it a decision rule and a background noise statistics estimator, by applying a statistical model. A robust decision rule is derived from the generalized likelihood ratio test (LRT) by assuming that the noise statistics are known a priori. The algorithm is most efficient for the time-varying noise. According to our simulation results, the proposed algorithm shows significantly better performance in low signal-to-noise ratio and in noisy environments.

KLT-based adaptive entropy-constrained vector quantization for the speech signals

For efficient variable-rate speech coding, Karhunen-Loeve transform based adaptive entropy-constrained vector quantization (KLT-AECVQ) is proposed. The proposed method consists of backward-adaptive linear predictive coding (LPC) analysis, KLT estimation based on LPC coefficients, and lattice vector quantization followed by Huffman coding according to KLT statistics. As different statistics in an original-signal domain can be mapped into identical statistics in a KLT domain, only a few classified Huffman codebooks are sufficient to represent KLT-domain source statistics. KLT-AECVQ with 32 Huffman codebooks has comparable rate-distortion performance with theoretically optimal AECVQ with infinite number of Huffman codebooks. KLT-AECVQ also produces superior perceptual quality to KLT-based classified vector quantization (KLTCVQ) that yielded better quality than conventional code excited linear predictive (CELP) codec. Under five-sample delay constraints, KLT-AECVQ has also three times lower complexity than CELP codec.

Limited error based event localizing temporal decomposition and its application to variable-rate speech coding

This paper proposes a novel algorithm for temporal decomposition (TD) of speech, called ‘limited error based event localizing temporal decomposition’ (LEBEL-TD), and its application to variable-rate speech coding. In previous work with TD, TD analysis was usually performed on each speech segment of about 200–300 ms or more, making it impractical for online applications. In this present work, the event localization is determined based on a limited error criterion and a local optimization strategy, which results in an average algorithmic delay of 65 ms. Simulation results show that an average log spectral distortion of about 1.5 dB can be achievable at an event rate of 20 events/s. Also, LEBEL-TD uses neither the computationally costly singular value decomposition routine nor the event refinement process, thus reducing significantly the computational cost of TD. Further, a method for variable-rate speech coding an average rate of around 1.8 kbps based on STRAIGHT (Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum), which is a high-quality speech analysis–synthesis framework, using LEBEL-TD is also realized. Subjective test results indicate that the performance of the proposed speech coding method is comparable to that of the 4.8 kbps FS-1016 CELP coder.

Pitch adaptive windows for improved excitation coding in low-rate CELP coders

A novel paradigm based on pitch-adaptive windows is proposed for solving the problem of encoding the fixed codebook excitation in low bit-rate CELP coders. In this method, the nonzero excitation in the fixed codebook is substantially localized to a set of time intervals called windows. The positions of the windows are adaptive to the pitch peaks in the linear prediction residual signal. Thus, high coding efficiency is achieved by allocating most of the available FCB bits to the perceptually important segments of the excitation signal. The pitch-adaptive method is adopted in the design of a novel multimode variable-rate speech coder applicable to CDMA-based cellular telephony. Results demonstrate that the adaptive windows method yields excellent voice quality and intelligibility at average bit-rates in the range of 2.5-4.0 kbps.

A statistical model-based voice activity detection

In this letter, we develop a robust voice activity detector (VAD) for the application to variable-rate speech coding. The developed VAD employs the decision-directed parameter estimation method for the likelihood ratio test. In addition, we propose an effective hang-over scheme which considers the previous observations by a first-order Markov process modeling of speech occurrences. According to our simulation results, the proposed VAD shows significantly better performances than the G.729B VAD in low signal-to-noise ratio (SNR) and vehicular noise environments.

Voicing-specific LPC quantization for variable-rate speech coding

Phonetic classification of speech frames allows distinctive quantization and bit allocation schemes suited to the particular class. Separate quantization of the linear predictive coding (LPC) parameters for voiced and unvoiced speech frames is shown to offer useful gains for representing the synthesis filter commonly used in code-excited linear prediction (CELP) and other coders. Subjective test results are reported that determine the required bit rate and accuracy in the two classes of voiced and unvoiced LPC spectra for CELP coding with phonetic classification. It was found, in this context, that unvoiced spectra need 9 b/frame or more whereas voiced spectra need 25 b/frame or more with the quantization schemes used. New spectral distortion criteria needed to assure transparent LPC spectral quantization for each voicing class in CELP coders are presented. Similar subjective test results for speech synthesized from the true residual signal are also presented, leading to some interesting observations on the role of the analysis-by-synthesis structure of CELP. Objective performance assessments based on the spectral distortion measure are also presented. The theoretical distortion-rate function for the spectral distortion measure is estimated for voiced and unvoiced LPC parameters and compared with experimental results obtained with unstructured vector quantization (VQ). These results show a saving of at least 2 b/frame for unvoiced spectra compared to voiced spectra to achieve the same spectral distortion performance.

Capacity enhancement of cellular CDMA by traffic-based control of speech bit rate

Variable rate speech coding is now recognized as an important system component for high-capacity cellular networks because it exploits speech statistics to reduce the average bit rate, which results in reduced interference and increased capacity. Once a variable rate capability is available, an additional capacity enhancement can be achieved by introducing network control of the user bit rate in response to changing traffic levels. We introduce the concept of network control of rate and propose a particular network-control method for code-division multiple access (CDMA) systems. Based on an M/M//spl infin///M queueing model applied to a cell under heavy traffic conditions and a new performance measure called averaged speech quality, we obtain simulation results to demonstrate how network control of rate can achieve improved speech quality or increased capacity for a given quality objective.

Finite state CELP for variable rate speech coding

The performance of a variable rate code excited linear predictor system is investigated. The coding system is based on a finite state CELP (FSCELP) frame work. Each individual state is primarily identified with a LPC model order, LPC coefficients bit allocation, excitation code book population density and state encoding rate. Successive input speech vectors are encoded at a rate that depends on the current state of the FSCELP system and the input vector characteristics. The use of a finite state system involves implicit clustering of speech signals. The lower rate states are selected during highly correlated steady state speech segments when relatively few bits are required to obtain adequate fidelity. For speech signals with a strong glottal excitation, unvoiced signals and transient speech segments, a relatively greater quantisation accuracy is needed to obtain good fidelity and therefore higher rate states of the system are used. Further improvement is obtained by using gamma populated excitation codebooks, for those states that are mainly used to encode speech signals with a strong underlying glottal excitation pulses. Experiments focus on investigation of the varying encoding requirements of the excitation signal for low pass, voiced, unvoiced and transient speech signals. The parameters of the finite state CELP system are designed to match the encoding requirements of typical speech signals. The greater part of the coding gain is obtained from variable rate encoding of the excitation signal. Using a six-state FSCELP, good quality speech is obtained at an average, maximum and minimum bit rates of 4 kbit/s, 10 kbit/s and 2 kbit/s, respectively.

Performance of packet reservation multiple access for digital mobile radio using variable rate speech coder

The advantages of using adaptive bit rate voice coders to improve the performance of PRMA for digital cellular radio are considered. It is shown that such a technique forces a fairer distribution of packet loss between mobiles and is able to accommodate fluctuations in traffic levels within a cell.

Real-time speech segmentation using pitch and convexity jump models: application to variable rate speech coding

Convexity jump models are combined with period identification in the time domain to provide an efficient segmentation technique adapted to the needs of coders. The algorithm presented works online, with no training, and is speaker-independent. Structural problems found in previous systems (blank detection spots, long decision delays) have been overcome. Both the simplicity and the generality (information theory basis for the divergence test) allow applications of the automatic online segmentation method in other areas of signal processing. >