15 research outputs found
Text-Independent Voice Conversion
This thesis deals with text-independent solutions for voice conversion. It first introduces the use of vocal tract length normalization (VTLN) for voice conversion. The presented variants of VTLN allow for easily changing speaker characteristics by means of a few trainable parameters. Furthermore, it is shown how VTLN can be expressed in time domain strongly reducing the computational costs while keeping a high speech quality. The second text-independent voice conversion paradigm is residual prediction. In particular, two proposed techniques, residual smoothing and the application of unit selection, result in essential improvement of both speech quality and voice similarity. In order to apply the well-studied linear transformation paradigm to text-independent voice conversion, two text-independent speech alignment techniques are introduced. One is based on automatic segmentation and mapping of artificial phonetic classes and the other is a completely data-driven approach with unit selection. The latter achieves a performance very similar to the conventional text-dependent approach in terms of speech quality and similarity. It is also successfully applied to cross-language voice conversion. The investigations of this thesis are based on several corpora of three different languages, i.e., English, Spanish, and German. Results are also presented from the multilingual voice conversion evaluation in the framework of the international speech-to-speech translation project TC-Star
Automatic Speech Recognition for ageing voices
With ageing, human voices undergo several changes which are typically characterised
by increased hoarseness, breathiness, changes in articulatory patterns and slower speaking
rate. The focus of this thesis is to understand the impact of ageing on Automatic
Speech Recognition (ASR) performance and improve the ASR accuracies for older
voices.
Baseline results on three corpora indicate that the word error rates (WER) for older
adults are significantly higher than those of younger adults and the decrease in accuracies
is higher for males speakers as compared to females.
Acoustic parameters such as jitter and shimmer that measure glottal source disfluencies
were found to be significantly higher for older adults. However, the hypothesis
that these changes explain the differences in WER for the two age groups is proven incorrect.
Experiments with artificial introduction of glottal source disfluencies in speech
from younger adults do not display a significant impact on WERs. Changes in fundamental
frequency observed quite often in older voices has a marginal impact on ASR
accuracies.
Analysis of phoneme errors between younger and older speakers shows a pattern
of certain phonemes especially lower vowels getting more affected with ageing. These
changes however are seen to vary across speakers. Another factor that is strongly associated
with ageing voices is a decrease in the rate of speech. Experiments to analyse
the impact of slower speaking rate on ASR accuracies indicate that the insertion errors
increase while decoding slower speech with models trained on relatively faster speech.
We then propose a way to characterise speakers in acoustic space based on speaker
adaptation transforms and observe that speakers (especially males) can be segregated
with reasonable accuracies based on age. Inspired by this, we look at supervised hierarchical
acoustic models based on gender and age. Significant improvements in word
accuracies are achieved over the baseline results with such models. The idea is then extended
to construct unsupervised hierarchical models which also outperform the baseline
models by a good margin.
Finally, we hypothesize that the ASR accuracies can be improved by augmenting
the adaptation data with speech from acoustically closest speakers. A strategy to select
the augmentation speakers is proposed. Experimental results on two corpora indicate
that the hypothesis holds true only when the amount of available adaptation is limited
to a few seconds. The efficacy of such a speaker selection strategy is analysed for both
younger and older adults
Automatic speech recognition: from study to practice
Today, automatic speech recognition (ASR) is widely used for different purposes such as robotics, multimedia, medical and industrial application. Although many researches have been performed in this field in the past decades, there is still a lot of room to work. In order to start working in this area, complete knowledge of ASR systems as well as their weak points and problems is inevitable. Besides that, practical experience improves the theoretical knowledge understanding in a reliable way. Regarding to these facts, in this master thesis, we have first reviewed the principal structure of the standard HMM-based ASR systems from technical point of view. This includes, feature extraction, acoustic modeling, language modeling and decoding. Then, the most significant challenging points in ASR systems is discussed. These challenging points address different internal components characteristics or external agents which affect the ASR systems performance. Furthermore, we have implemented a Spanish language recognizer using HTK toolkit. Finally, two open research lines according to the studies of different sources in the field of ASR has been suggested for future work
Representation Learning for Spoken term Detection
Spoken Term Detection (STD) is the task of searching a given spoken query word in large speech database. Applications of STD include speech data indexing, voice dialling, telephone monitoring and data mining. Performance of STD depends mainly on representation of speech signal and matching of represented signal. This work investigates methods for robust representation of speech signal, which is invariant to speaker variability, in the context of STD task. Here the representation is in the form of templates, a sequence of feature vectors. Typical representation in speech community Mel-Frequency CepstralCoe cients (MFCC) carry both speech-specific and speaker-specific information, so the need for better representation. Searching is done by matching sequence of feature vectors of query and reference utterances by using Subsequence Dynamic Time Warping (DTW). The performance of the proposed representation is evaluated on Telugu broadcast news data. In the absence of labelled data i.e., in unsupervised setting, we propose to capture joint density of acoustic space spanned by MFCCs using Gaussian Mixture Models (GMM) and Gaussian-Bernoulli Restricted Boltzmann Machines (GBRBM). Posterior features extracted from trained models are used to search the query word. It is noticed that 8% and 12% improvement in STD performance compared to MFCC by using GMM and GBRBM posterior features respectively. As transcribed data is not required, this approach is optimal solution to low-resource languages. But due to it’s intermediate performance, this method cannot be immediate solution to high resource language
Acoustic Approaches to Gender and Accent Identification
There has been considerable research on the problems of speaker and language recognition
from samples of speech. A less researched problem is that of accent recognition. Although this
is a similar problem to language identification, di�erent accents of a language exhibit more
fine-grained di�erences between classes than languages. This presents a tougher problem
for traditional classification techniques. In this thesis, we propose and evaluate a number of
techniques for gender and accent classification. These techniques are novel modifications and
extensions to state of the art algorithms, and they result in enhanced performance on gender
and accent recognition.
The first part of the thesis focuses on the problem of gender identification, and presents a
technique that gives improved performance in situations where training and test conditions are
mismatched.
The bulk of this thesis is concerned with the application of the i-Vector technique to accent
identification, which is the most successful approach to acoustic classification to have emerged
in recent years. We show that it is possible to achieve high accuracy accent identification without
reliance on transcriptions and without utilising phoneme recognition algorithms. The thesis
describes various stages in the development of i-Vector based accent classification that improve
the standard approaches usually applied for speaker or language identification, which are
insu�cient. We demonstrate that very good accent identification performance is possible with
acoustic methods by considering di�erent i-Vector projections, frontend parameters, i-Vector
configuration parameters, and an optimised fusion of the resulting i-Vector classifiers we can
obtain from the same data.
We claim to have achieved the best accent identification performance on the test corpus
for acoustic methods, with up to 90% identification rate. This performance is even better than
previously reported acoustic-phonotactic based systems on the same corpus, and is very close
to performance obtained via transcription based accent identification. Finally, we demonstrate
that the utilization of our techniques for speech recognition purposes leads to considerably
lower word error rates.
Keywords: Accent Identification, Gender Identification, Speaker Identification, Gaussian
Mixture Model, Support Vector Machine, i-Vector, Factor Analysis, Feature Extraction, British
English, Prosody, Speech Recognition
Biometrics
Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book
VTLN warping factor estimation using accumulation of sufficient statistics
ABSTRACT In this paper we present an efficient and flexible approach to VTLN warping factor estimation. Due to the equivalence of frequency warping and linear transformation of cepstral coefficients, warping factors can be efficiently estimated by accumulating the sufficient statistics for linear transformation estimation, and searching the constrained space of transformations given by the explicit mapping between warping factors and linear transformation matrices. We show that the positive effect of using a properly normalized optimization criterion for warping factor estimation, which has been previously demonstrated for a signal analysis front-end without a filterbank, carries over to a MFCC front-end, resulting in a net improvement in word error rate