Linear predictive modelling of speech : constraints and line spectrum pair decomposition

In an exploration of the spectral modelling of speech, this thesis presents theory and applications of constrained linear predictive (LP) models. Spectral models are essential in many applications of speech technology, such as speech coding, synthesis and recognition. At present, the prevailing approach in speech spectral modelling is linear prediction. In speech coding, spectral models obtained by LP are typically quantised using a polynomial transform called the Line Spectrum Pair (LSP) decomposition. An inherent drawback of conventional LP is its inability to include speech specific a priori information in the modelling process. This thesis, in contrast, presents different constraints applied to LP models, which are then shown to have relevant properties with respect to root loci of the model in its all-pole form. Namely, we show that LSP polynomials correspond to time domain constraints that force the roots of the model to the unit circle. Furthermore, this result is used in the development of advanced spectral models of speech that are represented by stable all-pole filters. Moreover, the theoretical results also include a generic framework for constrained linear predictive models in matrix notation. For these models, we derive sufficient criteria for stability of their all-pole form. Such models can be used to include a priori information in the generation of any application specific, linear predictive model. As a side result, we present a matrix decomposition rule for Toeplitz and Hankel matrices.reviewe

Robust speaker identification using artificial neural networks

This research mainly focuses on recognizing the speakers through their speech samples. Numerous Text-Dependent or Text-Independent algorithms have been developed by people so far, to recognize the speaker from his/her speech. In this thesis, we concentrate on the recognition of the speaker from the fixed text i.e. Text-Dependent . Possibility of extending this method to variable text i.e. Text-Independent is also analyzed. Different feature extraction algorithms are employed and their performance with Artificial Neural Networks as a Data Classifier on a fixed training set is analyzed. We find a way to combine all these individual feature extraction algorithms by incorporating their interdependence. The efficiency of these algorithms is determined after the input speech is classified using Back Propagation Algorithm of Artificial Neural Networks. A special case of Back Propagation Algorithm which improves the efficiency of the classification is also discussed

Spectral Envelope Modelling for Full-Band Speech Coding

Speech coding considering historically narrow-band was in the latest years significantly improved by widening the coded audio bandwidth. However, existing speech coders still employ a limited band source-filter model extended by parametric coding of the higher band. In this thesis, a full-band source-filter model is considered and especially its spectral magnitude envelope modelling. To match full-band operating mode, we modified, tuned and compared two methods, Linear Predictive Coding (LPC) and Distribution Quantization (DQ). LPC uses autoregressive modeling, while DQ quantifies the energy ratios between parts of the spectrum. Parameters of both methods were quantized with multi-stage vector quantization. Objective and subjective evaluations indicate the two methods used in a full-band source-filter coding scheme perform on the same range and are competitive against conventional speech coders requiring an extra bandwidth extension

Speech coding

Speech is the predominant means of communication between human beings and since the invention of the telephone by Alexander Graham Bell in 1876, speech services have remained to be the core service in almost all telecommunication systems. Original analog methods of telephony had the disadvantage of speech signal getting corrupted by noise, cross-talk and distortion Long haul transmissions which use repeaters to compensate for the loss in signal strength on transmission links also increase the associated noise and distortion. On the other hand digital transmission is relatively immune to noise, cross-talk and distortion primarily because of the capability to faithfully regenerate digital signal at each repeater purely based on a binary decision. Hence end-to-end performance of the digital link essentially becomes independent of the length and operating frequency bands of the link Hence from a transmission point of view digital transmission has been the preferred approach due to its higher immunity to noise. The need to carry digital speech became extremely important from a service provision point of view as well. Modem requirements have introduced the need for robust, flexible and secure services that can carry a multitude of signal types (such as voice, data and video) without a fundamental change in infrastructure. Such a requirement could not have been easily met without the advent of digital transmission systems, thereby requiring speech to be coded digitally. The term Speech Coding is often referred to techniques that represent or code speech signals either directly as a waveform or as a set of parameters by analyzing the speech signal. In either case, the codes are transmitted to the distant end where speech is reconstructed or synthesized using the received set of codes. A more generic term that is applicable to these techniques that is often interchangeably used with speech coding is the term voice coding. This term is more generic in the sense that the coding techniques are equally applicable to any voice signal whether or not it carries any intelligible information, as the term speech implies. Other terms that are commonly used are speech compression and voice compression since the fundamental idea behind speech coding is to reduce (compress) the transmission rate (or equivalently the bandwidth) And/or reduce storage requirements In this document the terms speech and voice shall be used interchangeably

A Parametric Approach for Efficient Speech Storage, Flexible Synthesis and Voice Conversion

During the past decades, many areas of speech processing have benefited from the vast increases in the available memory sizes and processing power. For example, speech recognizers can be trained with enormous speech databases and high-quality speech synthesizers can generate new speech sentences by concatenating speech units retrieved from a large inventory of speech data. However, even in today's world of ever-increasing memory sizes and computational resources, there are still lots of embedded application scenarios for speech processing techniques where the memory capacities and the processor speeds are very limited. Thus, there is still a clear demand for solutions that can operate with limited resources, e.g., on low-end mobile devices. This thesis introduces a new segmental parametric speech codec referred to as the VLBR codec. The novel proprietary sinusoidal speech codec designed for efficient speech storage is capable of achieving relatively good speech quality at compression ratios beyond the ones offered by the standardized speech coding solutions, i.e., at bitrates of approximately 1 kbps and below. The efficiency of the proposed coding approach is based on model simplifications, mode-based segmental processing, and the method of adaptive downsampling and quantization. The coding efficiency is also further improved using a novel flexible multi-mode matrix quantizer structure and enhanced dynamic codebook reordering. The compression is also facilitated using a new perceptual irrelevancy removal method. The VLBR codec is also applied to text-to-speech synthesis. In particular, the codec is utilized for the compression of unit selection databases and for the parametric concatenation of speech units. It is also shown that the efficiency of the database compression can be further enhanced using speaker-specific retraining of the codec. Moreover, the computational load is significantly decreased using a new compression-motivated scheme for very fast and memory-efficient calculation of concatenation costs, based on techniques and implementations used in the VLBR codec. Finally, the VLBR codec and the related speech synthesis techniques are complemented with voice conversion methods that allow modifying the perceived speaker identity which in turn enables, e.g., cost-efficient creation of new text-to-speech voices. The VLBR-based voice conversion system combines compression with the popular Gaussian mixture model based conversion approach. Furthermore, a novel method is proposed for converting the prosodic aspects of speech. The performance of the VLBR-based voice conversion system is also enhanced using a new approach for mode selection and through explicit control of the degree of voicing. The solutions proposed in the thesis together form a complete system that can be utilized in different ways and configurations. The VLBR codec itself can be utilized, e.g., for efficient compression of audio books, and the speech synthesis related methods can be used for reducing the footprint and the computational load of concatenative text-to-speech synthesizers to levels required in some embedded applications. The VLBR-based voice conversion techniques can be used to complement the codec both in storage applications and in connection with speech synthesis. It is also possible to only utilize the voice conversion functionality, e.g., in games or other entertainment applications

Nouvelles techniques de quantification vectorielle algébrique basées sur le codage de Voronoi : application au codage AMR-WB+

L'objet de cette thèse est l'étude de la quantification (vectorielle) par réseau de points et de son application au modèle de codage audio ACELP/TCX multi-mode. Le modèle ACELP/TCX constitue une solution possible au problème du codage audio universel---par codage universel, on entend la représentation unifiée de bonne qualité des signaux de parole et de musique à différents débits et fréquences d'échantillonnage. On considère ici comme applications la quantification des coefficients de prédiction linéaire et surtout le codage par transformée au sein du modèle TCX; l'application au codage TCX a un fort intérêt pratique, car le modèle TCX conditionne en grande partie le caractère universel du codage ACELP/TCX. La quantification par réseau de points est une technique de quantification par contrainte, exploitant la structure linéaire des réseaux réguliers. Elle a toujours été considérée, par rapport à la quantification vectorielle non structurée, comme une technique prometteuse du fait de sa complexité réduite (en stockage et quantité de calculs). On montre ici qu'elle possède d'autres avantages importants: elle rend possible la construction de codes efficaces en dimension relativement élevée et à débit arbitrairement élevé, adaptés au codage multi-débit (par transformée ou autre); en outre, elle permet de ramener la distorsion à la seule erreur granulaire au prix d'un codage à débit variable. Plusieurs techniques de quantification par réseau de points sont présentées dans cette thèse. Elles sont toutes élaborées à partir du codage de Voronoï. Le codage de Voronoï quasi-ellipsoïdal est adapté au codage d'une source gaussienne vectorielle dans le contexte du codage paramétrique de coefficients de prédiction linéaire à l'aide d'un modèle de mélange gaussien. La quantification vectorielle multi-débit par extension de Voronoï ou par codage de Voronoï à troncature adaptative est adaptée au codage audio par transformée multi-débit. L'application de la quantification vectorielle multi-débit au codage TCX est plus particulièrement étudiée. Une nouvelle technique de codage algébrique de la cible TCX est ainsi conçue à partir du principe d'allocation des bits par remplissage inverse des eaux