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

Word processed copy. Includes bibliographical references

Segment phoneme classification from speech under noisy conditions: Using amplitude-frequency modulation based two-dimensional auto-regressive features with deep neural networks

This thesis investigates at the acoustic-phonetic level the noise robustness of features derived using the AM-FM analysis of speech signals. The analysis on the noise robustness of these features is done using various neural network models and is based on the segment classification of phonemes. This analysis is also extended and the robustness of the AM-FM based features is compared under similar noise conditions with the traditional features such as the Mel-frequency cepstral coefficients(MFCC). We begin with an important aspect of segment phoneme classification experiments which is the study of architectural and training strategies of the various neural network models used. The results of these experiments showed that there is a difference in the training pattern adopted by the various neural network models. Before over-fitting, models that undergo pre-training are seen to train for many epochs more than their opposite models that do not undergo pre-training. Taking this difference in training pattern into perspective and based on phoneme classification rate the Gaussian restricted Boltzmann machine and the single layer perceptron are selected as the best performing model of the two groups, respectively. Using the two best performing models for classification, segment phoneme classification experiments under different noise conditions are performed for both the AM-FM based and traditional features. The experiments showed that AM-FM based frequency domain linear prediction features with or without feature compensation are more robust in the classification of 61 phonemes under white noise and 0 $dB$ signal-to-noise ratio(SNR) conditions compared to the traditional features. However, when the phonemes are folded to 39 phonemes, the results are ambiguous under all noise conditions and there is no unanimous conclusion as to which feature is most robust

Statistical Models of Reconstructed Phase Spaces for Signal Classification

This paper introduces a novel approach to the analysis and classification of time series signals using statistical models of reconstructed phase spaces. With sufficient dimension, such reconstructed phase spaces are, with probability one, guaranteed to be topologically equivalent to the state dynamics of the generating system, and, therefore, may contain information that is absent in analysis and classification methods rooted in linear assumptions. Parametric and nonparametric distributions are introduced as statistical representations over the multidimensional reconstructed phase space, with classification accomplished through methods such as Bayes maximum likelihood and artificial neural networks (ANNs). The technique is demonstrated on heart arrhythmia classification and speech recognition. This new approach is shown to be a viable and effective alternative to traditional signal classification approaches, particularly for signals with strong nonlinear characteristics

Modelling and extraction of fundamental frequency in speech signals

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.One of the most important parameters of speech is the fundamental frequency of vibration of voiced sounds. The audio sensation of the fundamental frequency is known as the pitch. Depending on the tonal/non-tonal category of language, the fundamental frequency conveys intonation, pragmatics and meaning. In addition the fundamental frequency and intonation carry speaker gender, age, identity, speaking style and emotional state. Accurate estimation of the fundamental frequency is critically important for functioning of speech processing applications such as speech coding, speech recognition, speech synthesis and voice morphing. This thesis makes contributions to the development of accurate pitch estimation research in three distinct ways: (1) an investigation of the impact of the window length on pitch estimation error, (2) an investigation of the use of the higher order moments and (3) an investigation of an analysis-synthesis method for selection of the best pitch value among N proposed candidates. Experimental evaluations show that the length of the speech window has a major impact on the accuracy of pitch estimation. Depending on the similarity criteria and the order of the statistical moment a window length of 37 to 80 ms gives the least error. In order to avoid excessive delay as a consequence of using a longer window, a method is proposed ii where the current short window is concatenated with the previous frames to form a longer signal window for pitch extraction. The use of second order and higher order moments, and the magnitude difference function, as the similarity criteria were explored and compared. A novel method of calculation of moments is introduced where the signal is split, i.e. rectified, into positive and negative valued samples. The moments for the positive and negative parts of the signal are computed separately and combined. The new method of calculation of moments from positive and negative parts and the higher order criteria provide competitive results. A challenging issue in pitch estimation is the determination of the best candidate from N extrema of the similarity criteria. The analysis-synthesis method proposed in this thesis selects the pitch candidate that provides the best reproduction (synthesis) of the harmonic spectrum of the original speech. The synthesis method must be such that the distortion increases with the increasing error in the estimate of the fundamental frequency. To this end a new method of spectral synthesis is proposed using an estimate of the spectral envelop and harmonically spaced asymmetric Gaussian pulses as excitation. The N-best method provides consistent reduction in pitch estimation error. The methods described in this thesis result in a significant improvement in the pitch accuracy and outperform the benchmark YIN method

Cepstral trajectories in linguistic units for text-independent speaker recognition

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-35292-8_3Proceedings of IberSPEECH, held in Madrid (Spain) on 2012.In this paper, the contributions of different linguistic units to the speaker recognition task are explored by means of temporal trajectories of their MFCC features. Inspired by successful work in forensic speaker identification, we extend the approach based on temporal contours of formant frequencies in linguistic units to design a fully automatic system that puts together both forensic and automatic speaker recognition worlds. The combination of MFCC features and unit-dependent trajectories provides a powerful tool to extract individualizing information. At a fine-grained level, we provide a calibrated likelihood ratio per linguistic unit under analysis (extremely useful in applications such as forensics), and at a coarse-grained level, we combine the individual contributions of the different units to obtain a highly discriminative single system. This approach has been tested with NIST SRE 2006 datasets and protocols, consisting of 9,720 trials from 219 male speakers for the 1side-1side English-only task, and development data being extracted from 367 male speakers from 1,808 conversations from NIST SRE 2004 and 2005 datasetsSupported by MEC grant PR-2010-123, MICINN project TEC09-14179, ForBayes project CCG10-UAM/TIC-5792 and Cátedra UAM-Telefónica

Methods for speaking style conversion from normal speech to high vocal effort speech

This thesis deals with vocal-effort-focused speaking style conversion (SSC). Specifically, we studied two topics on conversion of normal speech to high vocal effort. The first topic involves the conversion of normal speech to shouted speech. We employed this conversion in a speaker recognition system with vocal effort mismatch between test and enrollment utterances (shouted speech vs. normal speech). The mismatch causes a degradation of the system's speaker identification performance. As solution, we proposed a SSC system that included a novel spectral mapping, used along a statistical mapping technique, to transform the mel-frequency spectral energies of normal speech enrollment utterances towards their counterparts in shouted speech. We evaluated the proposed solution by comparing speaker identification rates for a state-of-the-art i-vector-based speaker recognition system, with and without applying SSC to the enrollment utterances. Our results showed that applying the proposed SSC pre-processing to the enrollment data improves considerably the speaker identification rates. The second topic involves a normal-to-Lombard speech conversion. We proposed a vocoder-based parametric SSC system to perform the conversion. This system first extracts speech features using the vocoder. Next, a mapping technique, robust to data scarcity, maps the features. Finally, the vocoder synthesizes the mapped features into speech. We used two vocoders in the conversion system, for comparison: a glottal vocoder and the widely used STRAIGHT. We assessed the converted speech from the two vocoder cases with two subjective listening tests that measured similarity to Lombard speech and naturalness. The similarity subjective test showed that, for both vocoder cases, our proposed SSC system was able to convert normal speech to Lombard speech. The naturalness subjective test showed that the converted samples using the glottal vocoder were clearly more natural than those obtained with STRAIGHT