Porting concepts from DNNs back to GMMs

Deep neural networks (DNNs) have been shown to outperform Gaussian Mixture Models (GMM) on a variety of speech recognition benchmarks. In this paper we analyze the differences between the DNN and GMM modeling techniques and port the best ideas from the DNN-based modeling to a GMM-based system. By going both deep (multiple layers) and wide (multiple parallel sub-models) and by sharing model parameters, we are able to close the gap between the two modeling techniques on the TIMIT database. Since the 'deep' GMMs retain the maximum-likelihood trained Gaussians as first layer, advanced techniques such as speaker adaptation and model-based noise robustness can be readily incorporated. Regardless of their similarities, the DNNs and the deep GMMs still show a sufficient amount of complementarity to allow effective system combination

Integration of speech biometrics in a phone payment system: text-independent speaker verification

Integration of a speaker recognition system in a payment system by phone.Nowadays, the integration of biometrics in security systems is a prominent research and application field. Also, it is clear that speech is the most common form of communication, which makes a swell candidate. While using speech as a biometric, one could say there are two types of systems that should be analyzed: those systems which do know what the speaker is going to say upon verification and those that do not. This degree thesis offers an overview of both systems, focusing on those that do not know what the speaker is going to say beforehand, also known as textindependent systems. To be able to determine which would be the best approach to integrate speech biometrics into a security system, both types of systems are compared; and two methodologies are also analyzed for the text-independent system. To conclude, one of those methodologies is implemented in a software library which allows the creation a text-independent speaker verification system.En l’actualitat, la integració de biometries en els sistemes de seguretat és una branca d’investigació i aplicacions prominent. A més a més, la veu és un dels mitjans més comuns de comunicació, cosa que fa que sigui una bona candidata per a aquests sistemes. Si prenem la parla com a biometria, es pot dir que hi ha dos tipus de sistemes bastant diferenciats a analitzar: aquells sistemes els quals saben el que dirà la persona que s’intenta verificar i aquells que no saben el que dirà. Aquest treball ofereix una visió àmplia dels dos tipus de sistemes, centrant-se en els sistemes on no es sap el que es dirà, també coneguts com sistemes de text independent. Per decidir quin seria la millor manera d’integrar la parla com a biometria en un sistema de seguretat, es comparen ambdós sistemes i, en el cas del sistema de text independent, es comparen també dues metodologies diferents. Per acabar, s’implementa una d’aquestes metodologies a unes llibreries de software per dur a terme un sistema de verificació de locutor amb text independent.En la actualidad, la integración de biometrías en los sistemas de seguridad es una rama de investigación y de aplicaciones prominente. Además, está claro que la voz es el medio más común de comunicación y es por eso que es una buena candidata. Usando el habla como biometría, se podría decir que hay dos tipos de sistemas diferentes a analizar: aquellos sistemas que saben de antemano aquello que va a decir el locutor que intenta verificarse y aquellos que no lo saben. Este trabajo ofrece una visión amplia de los dos tipos de sistemas, centrándose en los sistemas donde aquello que se va a decir no se sabe, también conocidos como sistemas de texto independiente. Para decir cuál sería la mejor manera de integrar el habla como biometría en un sistema de seguridad se comparan ambos sistemas y, en el caso del sistema de texto independiente, se comparan también dos metodologías diferentes. Para finalizar, se implementa una de estas últimas en unas librerías de software para poder llevar a cabo un sistema de verificación de locutor de texto independiente

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

Increasing Accuracy Performance through Optimal Feature Extraction Algorithms

This research developed models and techniques to improve the three key modules of popular recognition systems: preprocessing, feature extraction, and classification. Improvements were made in four key areas: processing speed, algorithm complexity, storage space, and accuracy. The focus was on the application areas of the face, traffic sign, and speaker recognition. In the preprocessing module of facial and traffic sign recognition, improvements were made through the utilization of grayscaling and anisotropic diffusion. In the feature extraction module, improvements were made in two different ways; first, through the use of mixed transforms and second through a convolutional neural network (CNN) that best fits specific datasets. The mixed transform system consists of various combinations of the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT), which have a reliable track record for image feature extraction. In terms of the proposed CNN, a neuroevolution system was used to determine the characteristics and layout of a CNN to best extract image features for particular datasets. In the speaker recognition system, the improvement to the feature extraction module comprised of a quantized spectral covariance matrix and a two-dimensional Principal Component Analysis (2DPCA) function. In the classification module, enhancements were made in visual recognition through the use of two neural networks: the multilayer sigmoid and convolutional neural network. Results show that the proposed improvements in the three modules led to an increase in accuracy as well as reduced algorithmic complexity, with corresponding reductions in storage space and processing time

Speaker Independent Acoustic-to-Articulatory Inversion

Acoustic-to-articulatory inversion, the determination of articulatory parameters from acoustic signals, is a difficult but important problem for many speech processing applications, such as automatic speech recognition (ASR) and computer aided pronunciation training (CAPT). In recent years, several approaches have been successfully implemented for speaker dependent models with parallel acoustic and kinematic training data. However, in many practical applications inversion is needed for new speakers for whom no articulatory data is available. In order to address this problem, this dissertation introduces a novel speaker adaptation approach called Parallel Reference Speaker Weighting (PRSW), based on parallel acoustic and articulatory Hidden Markov Models (HMM). This approach uses a robust normalized articulatory space and palate referenced articulatory features combined with speaker-weighted adaptation to form an inversion mapping for new speakers that can accurately estimate articulatory trajectories. The proposed PRSW method is evaluated on the newly collected Marquette electromagnetic articulography - Mandarin Accented English (EMA-MAE) corpus using 20 native English speakers. Cross-speaker inversion results show that given a good selection of reference speakers with consistent acoustic and articulatory patterns, the PRSW approach gives good speaker independent inversion performance even without kinematic training data

Session varaibility compensation in automatic speaker and language recognition

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 201