Temporally Varying Weight Regression for Speech Recognition

Ph.DDOCTOR OF PHILOSOPH

Frame-level features conveying phonetic information for language and speaker recognition

150 p.This Thesis, developed in the Software Technologies Working Group of the Departmentof Electricity and Electronics of the University of the Basque Country, focuseson the research eld of spoken language and speaker recognition technologies.More specically, the research carried out studies the design of a set of featuresconveying spectral acoustic and phonotactic information, searches for the optimalfeature extraction parameters, and analyses the integration and usage of the featuresin language recognition systems, and the complementarity of these approacheswith regard to state-of-the-art systems. The study reveals that systems trained onthe proposed set of features, denoted as Phone Log-Likelihood Ratios (PLLRs), arehighly competitive, outperforming in several benchmarks other state-of-the-art systems.Moreover, PLLR-based systems also provide complementary information withregard to other phonotactic and acoustic approaches, which makes them suitable infusions to improve the overall performance of spoken language recognition systems.The usage of this features is also studied in speaker recognition tasks. In this context,the results attained by the approaches based on PLLR features are not as remarkableas the ones of systems based on standard acoustic features, but they still providecomplementary information that can be used to enhance the overall performance ofthe speaker recognition systems

Histogram equalization for robust text-independent speaker verification in telephone environments

Word processed copy. Includes bibliographical references

Discriminative classifiers for speaker recognition

Speaker Recognition, Speaker Verification, Sparse Kernel Logistic Regression, Support Vector MachineMagdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2008von Marcel Kat

Spoken Term Detection on Low Resource Languages

Developing efficient speech processing systems for low-resource languages is an immensely challenging problem. One potentially effective approach to address the lack of resources for any particular language, is to employ data from multiple languages for building speech processing sub-systems. This thesis investigates possible methodologies for Spoken Term Detection (STD) from low- resource Indian languages. The task of STD intend to search for a query keyword, given in text form, from a considerably large speech database. This is usually done by matching templates of feature vectors, representing sequence of phonemes from the query word and the continuous speech from the database. Typical set of features used to represent speech signals in most of the speech processing systems are the mel frequency cepstral coefficients (MFCC). As speech is a very complexsignal, holding information about the textual message, speaker identity, emotional and health state of the speaker, etc., the MFCC features derived from it will also contain information about all these factors. For eficient template matching, we need to neutralize the speaker variability in features and stabilize them to represent the speech variability alone

Methods for Addressing Data Diversity in Automatic Speech Recognition

The performance of speech recognition systems is known to degrade in mismatched conditions, where the acoustic environment and the speaker population significantly differ between the training and target test data. Performance degradation due to the mismatch is widely reported in the literature, particularly for diverse datasets. This thesis approaches the mismatch problem in diverse datasets with various strategies including data refinement, variability modelling and speech recognition model adaptation. These strategies are realised in six novel contributions. The first contribution is a data subset selection technique using likelihood ratio derived from a target test set quantifying mismatch. The second contribution is a multi-style training method using data augmentation. The existing training data is augmented using a distribution of variabilities learnt from a target dataset, resulting in a matched set. The third contribution is a new approach for genre identification in diverse media data with the aim of reducing the mismatch in an adaptation framework. The fourth contribution is a novel method which performs an unsupervised domain discovery using latent Dirichlet allocation. Since the latent domains have a high correlation with some subjective meta-data tags, such as genre labels of media data, features derived from the latent domains are successfully applied to the genre and broadcast show identification tasks. The fifth contribution extends the latent modelling technique for acoustic model adaptation, where latent-domain specific models are adapted from a base model. As the sixth contribution, an alternative adaptation approach is proposed where subspace adaptation of deep neural network acoustic models is performed using the proposed latent-domain aware training procedure. All of the proposed techniques for mismatch reduction are verified using diverse datasets. Using data selection, data augmentation and latent-domain model adaptation methods the mismatch between training and testing conditions of diverse ASR systems are reduced, resulting in more robust speech recognition systems

Speaker characterization using adult and children’s speech

Speech signals contain important information about a speaker, such as age, gender, language, accent, and emotional/psychological state. Automatic recognition of these types of characteristics has a wide range of commercial, medical and forensic applications such as interactive voice response systems, service customization, natural human-machine interaction, recognizing the type of pathology of speakers, and directing the forensic investigation process. Many such applications depend on reliable systems using short speech segments without regard to the spoken text (text-independent). All these applications are also applicable using children’s speech. This research aims to develop accurate methods and tools to identify different characteristics of the speakers. Our experiments cover speaker recognition, gender recognition, age-group classification, and accent identification. However, similar approaches and techniques can be applied to identify other characteristics such as emotional/psychological state. The main focus of this research is on detecting these characteristics from children’s speech, which is previously reported as a more challenging subject compared to adult. Furthermore, the impact of different frequency bands on the performances of several recognition systems is studied, and the performance obtained using children’s speech is compared with the corresponding results from experiments using adults’ speech. Speaker characterization is performed by fitting a probability density function to acoustic features extracted from the speech signals. Since the distribution of acoustic features is complex, Gaussian mixture models (GMM) are applied. Due to lack of data, parametric model adaptation methods have been applied to adapt the universal background model (UBM) to the char acteristics of utterances. An effective approach involves adapting the UBM to speech signals using the Maximum-A-Posteriori (MAP) scheme. Then, the Gaussian means of the adapted GMM are concatenated to form a Gaussian mean super-vector for a given utterance. Finally, a classification or regression algorithm is used to identify the speaker characteristics. While effective, Gaussian mean super-vectors are of a high dimensionality resulting in high computational cost and difficulty in obtaining a robust model in the context of limited data. In the field of speaker recognition, recent advances using the i-vector framework have increased the classification accuracy. This framework, which provides a compact representation of an utterance in the form of a low dimensional feature vector, applies a simple factor analysis on GMM means

A motion-based approach for audio-visual automatic speech recognition

The research work presented in this thesis introduces novel approaches for both visual region of interest extraction and visual feature extraction for use in audio-visual automatic speech recognition. In particular, the speaker‘s movement that occurs during speech is used to isolate the mouth region in video sequences and motionbased features obtained from this region are used to provide new visual features for audio-visual automatic speech recognition. The mouth region extraction approach proposed in this work is shown to give superior performance compared with existing colour-based lip segmentation methods. The new features are obtained from three separate representations of motion in the region of interest, namely the difference in luminance between successive images, block matching based motion vectors and optical flow. The new visual features are found to improve visual-only and audiovisual speech recognition performance when compared with the commonly-used appearance feature-based methods. In addition, a novel approach is proposed for visual feature extraction from either the discrete cosine transform or discrete wavelet transform representations of the mouth region of the speaker. In this work, the image transform is explored from a new viewpoint of data discrimination; in contrast to the more conventional data preservation viewpoint. The main findings of this work are that audio-visual automatic speech recognition systems using the new features extracted from the frequency bands selected according to their discriminatory abilities generally outperform those using features designed for data preservation. To establish the noise robustness of the new features proposed in this work, their performance has been studied in presence of a range of different types of noise and at various signal-to-noise ratios. In these experiments, the audio-visual automatic speech recognition systems based on the new approaches were found to give superior performance both to audio-visual systems using appearance based features and to audio-only speech recognition systems