20 research outputs found
Voice source characterization for prosodic and spectral manipulation
The objective of this dissertation is to study and develop techniques to decompose the speech signal into its two main
components: voice source and vocal tract. Our main efforts are on the glottal pulse analysis and characterization. We want to
explore the utility of this model in different areas of speech processing: speech synthesis, voice conversion or emotion detection
among others. Thus, we will study different techniques for prosodic and spectral manipulation. One of our requirements is that
the methods should be robust enough to work with the large databases typical of speech synthesis. We use a speech production
model in which the glottal flow produced by the vibrating vocal folds goes through the vocal (and nasal) tract cavities and its
radiated by the lips. Removing the effect of the vocal tract from the speech signal to obtain the glottal pulse is known as inverse
filtering. We use a parametric model fo the glottal pulse directly in the source-filter decomposition phase.
In order to validate the accuracy of the parametrization algorithm, we designed a synthetic corpus using LF glottal parameters
reported in the literature, complemented with our own results from the vowel database. The results show that our method gives
satisfactory results in a wide range of glottal configurations and at different levels of SNR. Our method using the whitened
residual compared favorably to this reference, achieving high quality ratings (Good-Excellent). Our full parametrized system
scored lower than the other two ranking in third place, but still higher than the acceptance threshold (Fair-Good).
Next we proposed two methods for prosody modification, one for each of the residual representations explained above. The first
method used our full parametrization system and frame interpolation to perform the desired changes in pitch and duration. The
second method used resampling on the residual waveform and a frame selection technique to generate a new sequence of
frames to be synthesized. The results showed that both methods are rated similarly (Fair-Good) and that more work is needed in
order to achieve quality levels similar to the reference methods.
As part of this dissertation, we have studied the application of our models in three different areas: voice conversion, voice quality
analysis and emotion recognition. We have included our speech production model in a reference voice conversion system, to
evaluate the impact of our parametrization in this task. The results showed that the evaluators preferred our method over the
original one, rating it with a higher score in the MOS scale. To study the voice quality, we recorded a small database consisting of
isolated, sustained Spanish vowels in four different phonations (modal, rough, creaky and falsetto) and were later also used in
our study of voice quality. Comparing the results with those reported in the literature, we found them to generally agree with
previous findings. Some differences existed, but they could be attributed to the difficulties in comparing voice qualities produced
by different speakers. At the same time we conducted experiments in the field of voice quality identification, with very good
results. We have also evaluated the performance of an automatic emotion classifier based on GMM using glottal measures. For
each emotion, we have trained an specific model using different features, comparing our parametrization to a baseline system
using spectral and prosodic characteristics. The results of the test were very satisfactory, showing a relative error reduction of
more than 20% with respect to the baseline system. The accuracy of the different emotions detection was also high, improving
the results of previously reported works using the same database. Overall, we can conclude that the glottal source parameters
extracted using our algorithm have a positive impact in the field of automatic emotion classification
Multi-parametric source-filter separation of speech and prosodic voice restoration
In this thesis, methods and models are developed and presented aiming at the estimation, restoration and transformation of the characteristics of human speech. During a first period of the thesis, a concept was developed that allows restoring prosodic voice features and reconstruct more natural sounding speech from pathological voices using a multi-resolution approach. Inspired from observations with respect to this approach, the necessity of a novel method for the separation of speech into voice source and articulation components emerged in order to improve the perceptive quality of the restored speech signal. This work subsequently represents the main part of this work and therefore is presented first in this thesis. The proposed method is evaluated on synthetic, physically modelled, healthy and pathological speech. A robust, separate representation of source and filter characteristics has applications in areas that go far beyond the reconstruction of alaryngeal speech. It is potentially useful for efficient speech coding, voice biometrics, emotional speech synthesis, remote and/or non-invasive voice disorder diagnosis, etc. A key aspect of the voice restoration method is the reliable separation of the speech signal into voice source and articulation for it is mostly the voice source that requires replacement or enhancement in alaryngeal speech. Observations during the evaluation of above method highlighted that this separation is insufficient with currently known methods. Therefore, the main part of this thesis is concerned with the modelling of voice and vocal tract and the estimation of the respective model parameters. Most methods for joint source filter estimation known today represent a compromise between model complexity, estimation feasibility and estimation efficiency. Typically, single-parametric models are used to represent the source for the sake of tractable optimization or multi-parametric models are estimated using inefficient grid searches over the entire parameter space. The novel method presented in this work proposes advances in the direction of efficiently estimating and fitting multi-parametric source and filter models to healthy and pathological speech signals, resulting in a more reliable estimation of voice source and especially vocal tract coefficients. In particular, the proposed method is exhibits a largely reduced bias in the estimated formant frequencies and bandwidths over a large variety of experimental conditions such as environmental noise, glottal jitter, fundamental frequency, voice types and glottal noise. The methods appears to be especially robust to environmental noise and improves the separation of deterministic voice source components from the articulation. Alaryngeal speakers often have great difficulty at producing intelligible, not to mention prosodic, speech. Despite great efforts and advances in surgical and rehabilitative techniques, currently known methods, devices and modes of speech rehabilitation leave pathological speakers with a lack in the ability to control key aspects of their voice. The proposed multiresolution approach presented at the end of this thesis provides alaryngeal speakers an intuitive manner to increase prosodic features in their speech by reconstructing a more intelligible, more natural and more prosodic voice. The proposed method is entirely non-invasive. Key prosodic cues are reconstructed and enhanced at different temporal scales by inducing additional volatility estimated from other, still intact, speech features. The restored voice source is thus controllable in an intuitive way by the alaryngeal speaker. Despite the above mentioned advantages there is also a weak point of the proposed joint source-filter estimation method to be mentioned. The proposed method exhibits a susceptibility to modelling errors of the glottal source. On the other hand, the proposed estimation framework appears to be well suited for future research on exactly this topic. A logical continuation of this work is the leverage the efficiency and reliability of the proposed method for the development of new, more accurate glottal source models
Time and frequency domain algorithms for speech coding
The promise of digital hardware economies (due to recent advances in
VLSI technology), has focussed much attention on more complex and sophisticated
speech coding algorithms which offer improved quality at relatively
low bit rates.
This thesis describes the results (obtained from computer simulations)
of research into various efficient (time and frequency domain) speech
encoders operating at a transmission bit rate of 16 Kbps.
In the time domain, Adaptive Differential Pulse Code Modulation (ADPCM)
systems employing both forward and backward adaptive prediction were
examined. A number of algorithms were proposed and evaluated, including
several variants of the Stochastic Approximation Predictor (SAP). A
Backward Block Adaptive (BBA) predictor was also developed and found to
outperform the conventional stochastic methods, even though its complexity
in terms of signal processing requirements is lower. A simplified
Adaptive Predictive Coder (APC) employing a single tap pitch predictor
considered next provided a slight improvement in performance over ADPCM,
but with rather greater complexity.
The ultimate test of any speech coding system is the perceptual performance
of the received speech. Recent research has indicated that this
may be enhanced by suitable control of the noise spectrum according to
the theory of auditory masking. Various noise shaping ADPCM
configurations were examined, and it was demonstrated that a proposed
pre-/post-filtering arrangement which exploits advantageously the
predictor-quantizer interaction, leads to the best subjective
performance in both forward and backward prediction systems.
Adaptive quantization is instrumental to the performance of ADPCM systems.
Both the forward adaptive quantizer (AQF) and the backward oneword
memory adaptation (AQJ) were examined. In addition, a novel method
of decreasing quantization noise in ADPCM-AQJ coders, which involves the
application of correction to the decoded speech samples, provided
reduced output noise across the spectrum, with considerable high frequency
noise suppression.
More powerful (and inevitably more complex) frequency domain speech
coders such as the Adaptive Transform Coder (ATC) and the Sub-band Coder
(SBC) offer good quality speech at 16 Kbps. To reduce complexity and
coding delay, whilst retaining the advantage of sub-band coding, a novel
transform based split-band coder (TSBC) was developed and found to compare
closely in performance with the SBC.
To prevent the heavy side information requirement associated with a
large number of bands in split-band coding schemes from impairing coding
accuracy, without forgoing the efficiency provided by adaptive bit
allocation, a method employing AQJs to code the sub-band signals together
with vector quantization of the bit allocation patterns was also
proposed.
Finally, 'pipeline' methods of bit allocation and step size estimation
(using the Fast Fourier Transform (FFT) on the input signal) were examined.
Such methods, although less accurate, are nevertheless useful in
limiting coding delay associated with SRC schemes employing Quadrature
Mirror Filters (QMF)
Orthogonal transforms and their application to image coding
Imperial Users onl
Models and analysis of vocal emissions for biomedical applications
This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies
Mathematical linguistics
but in fact this is still an early draft, version 0.56, August 1 2001. Please d