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

Artificial Bandwidth Extension of Speech Signals using Neural Networks

Although mobile wideband telephony has been standardized for over 15 years, many countries still do not have a nationwide network with good coverage. As a result, many cellphone calls are still downgraded to narrowband telephony. The resulting loss of quality can be reduced by artificial bandwidth extension. There has been great progress in bandwidth extension in recent years due to the use of neural networks. The topic of this thesis is the enhancement of artificial bandwidth extension using neural networks. A special focus is given to hands-free calls in a car, where the risk is high that the wideband connection is lost due to the fast movement. The bandwidth of narrowband transmission is not only reduced towards higher frequencies above 3.5 kHz but also towards lower frequencies below 300 Hz. There are already methods that estimate the low-frequency components quite well, which will therefore not be covered in this thesis. In most bandwidth extension algorithms, the narrowband signal is initially separated into a spectral envelope and an excitation signal. Both parts are then extended separately in order to finally combine both parts again. While the extension of the excitation can be implemented using simple methods without reducing the speech quality compared to wideband speech, the estimation of the spectral envelope for frequencies above 3.5 kHz is not yet solved satisfyingly. Current bandwidth extension algorithms are just able to reduce the quality loss due to narrowband transmission by a maximum of 50% in most evaluations. In this work, a modification for an existing method for excitation extension is proposed which achieves slight improvements while not generating additional computational complexity. In order to enhance the wideband envelope estimation with neural networks, two modifications of the training process are proposed. On the one hand, the loss function is extended with a discriminative part to address the different characteristics of phoneme classes. On the other hand, by using a GAN (generative adversarial network) for the training phase, a second network is added temporarily to evaluate the quality of the estimation. The neural networks that were trained are compared in subjective and objective evaluations. A final listening test addressed the scenario of a hands-free call in a car, which was simulated acoustically. The quality loss caused by the missing high frequency components could be reduced by 60% with the proposed approach.Obwohl die mobile Breitbandtelefonie bereits seit über 15 Jahren standardisiert ist, gibt es oftmals noch kein flächendeckendes Netz mit einer guten Abdeckung. Das führt dazu, dass weiterhin viele Mobilfunkgespräche auf Schmalbandtelefonie heruntergestuft werden. Der damit einhergehende Qualitätsverlust kann mit künstlicher Bandbreitenerweiterung reduziert werden. Das Thema dieser Arbeit sind Methoden zur weiteren Verbesserungen der Qualität des erweiterten Sprachsignals mithilfe neuronaler Netze. Ein besonderer Fokus liegt auf der Freisprech-Telefonie im Auto, da dabei das Risiko besonders hoch ist, dass durch die schnelle Fortbewegung die Breitbandverbindung verloren geht. Bei der Schmalbandübertragung fehlen neben den hochfrequenten Anteilen (etwa 3.5–7 kHz) auch tiefe Frequenzen unterhalb von etwa 300 Hz. Diese tieffrequenten Anteile können mit bereits vorhandenen Methoden gut geschätzt werden und sind somit nicht Teil dieser Arbeit. In vielen Algorithmen zur Bandbreitenerweiterung wird das Schmalbandsignal zu Beginn in eine spektrale Einhüllende und ein Anregungssignal aufgeteilt. Beide Anteile werden dann separat erweitert und schließlich wieder zusammengeführt. Während die Erweiterung der Anregung nahezu ohne Qualitätsverlust durch einfache Methoden umgesetzt werden kann ist die Schätzung der spektralen Einhüllenden für Frequenzen über 3.5 kHz noch nicht zufriedenstellend gelöst. Mit aktuellen Methoden können im besten Fall nur etwa 50% der durch Schmalbandübertragung reduzierten Qualität zurückgewonnen werden. Für die Anregungserweiterung wird in dieser Arbeit eine Variation vorgestellt, die leichte Verbesserungen erzielt ohne dabei einen Mehraufwand in der Berechnung zu erzeugen. Für die Schätzung der Einhüllenden des Breitbandsignals mithilfe neuronaler Netze werden zwei Änderungen am Trainingsprozess vorgeschlagen. Einerseits wird die Kostenfunktion um einen diskriminativen Anteil erweitert, der das Netz besser zwischen verschiedenen Phonemen unterscheiden lässt. Andererseits wird als Architektur ein GAN (Generative adversarial network) verwendet, wofür in der Trainingsphase ein zweites Netz verwendet wird, das die Qualität der Schätzung bewertet. Die trainierten neuronale Netze wurden in subjektiven und objektiven Tests verglichen. Ein abschließender Hörtest diente zur Evaluierung des Freisprechens im Auto, welches akustisch simuliert wurde. Der Qualitätsverlust durch Wegfallen der hohen Frequenzanteile konnte dabei mit dem vorgeschlagenen Ansatz um etwa 60% reduziert werden

Speech de-identification with deep neural networks

Cloud-based speech services are powerful practical tools but the privacy of the speakers raises important legal concerns when exposed to the Internet. We propose a deep neural network solution that removes personal characteristics from human speech by converting it to the voice of a Text-to-Speech (TTS) system before sending the utterance to the cloud. The network learns to transcode sequences of vocoder parameters, delta and delta-delta features of human speech to those of the TTS engine. We evaluated several TTS systems, vocoders and audio alignment techniques. We measured the performance of our method by (i) comparing the result of speech recognition on the de-identified utterances with the original texts, (ii) computing the Mel-Cepstral Distortion of the aligned TTS and the transcoded sequences, and (iii) questioning human participants in A-not-B, 2AFC and 6AFC tasks. Our approach achieves the level required by diverse applications

Visual scene recognition with biologically relevant generative models

This research focuses on developing visual object categorization methodologies that are based on machine learning techniques and biologically inspired generative models of visual scene recognition. Modelling the statistical variability in visual patterns, in the space of features extracted from them by an appropriate low level signal processing technique, is an important matter of investigation for both humans and machines. To study this problem, we have examined in detail two recent probabilistic models of vision: a simple multivariate Gaussian model as suggested by (Karklin & Lewicki, 2009) and a restricted Boltzmann machine (RBM) proposed by (Hinton, 2002). Both the models have been widely used for visual object classification and scene analysis tasks before. This research highlights that these models on their own are not plausible enough to perform the classification task, and suggests Fisher kernel as a means of inducing discrimination into these models for classification power. Our empirical results on standard benchmark data sets reveal that the classification performance of these generative models could be significantly boosted near to the state of the art performance, by drawing a Fisher kernel from compact generative models that computes the data labels in a fraction of total computation time. We compare the proposed technique with other distance based and kernel based classifiers to show how computationally efficient the Fisher kernels are. To the best of our knowledge, Fisher kernel has not been drawn from the RBM before, so the work presented in the thesis is novel in terms of its idea and application to vision problem

Unsupervised speech processing with applications to query-by-example spoken term detection

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 163-173).This thesis is motivated by the challenge of searching and extracting useful information from speech data in a completely unsupervised setting. In many real world speech processing problems, obtaining annotated data is not cost and time effective. We therefore ask how much can we learn from speech data without any transcription. To address this question, in this thesis, we chose the query-by-example spoken term detection as a specific scenario to demonstrate that this task can be done in the unsupervised setting without any annotations. To build the unsupervised spoken term detection framework, we contributed three main techniques to form a complete working flow. First, we present two posteriorgram-based speech representations which enable speaker-independent, and noisy spoken term matching. The feasibility and effectiveness of both posteriorgram features are demonstrated through a set of spoken term detection experiments on different datasets. Second, we show two lower-bounding based methods for Dynamic Time Warping (DTW) based pattern matching algorithms. Both algorithms greatly outperform the conventional DTW in a single-threaded computing environment. Third, we describe the parallel implementation of the lower-bounded DTW search algorithm. Experimental results indicate that the total running time of the entire spoken detection system grows linearly with corpus size. We also present the training of large Deep Belief Networks (DBNs) on Graphical Processing Units (GPUs). The phonetic classification experiment on the TIMIT corpus showed a speed-up of 36x for pre-training and 45x for back-propagation for a two-layer DBN trained on the GPU platform compared to the CPU platform.by Yaodong Zhang.Ph.D

Human Action Recognition from Active Acoustics: Physics Modelling for Representation Learning and Inference Using Generative Probabilistic Graphical Models

This dissertation explores computational methods to address the problem of physics-based modeling and ultimately doing inference from data in multiple modalities where there exists large amounts of low dimensional data complementary to a much smaller set of high dimensional data. In this instance the low dimensional timeseries data are active acoustics from a micro-Doppler sensor that include no or very limited spatial information, and the high dimensional data is RGB-Depth skeleton data from a Microsoft Kinect sensor. The task is that of human action recognition from the active acoustic data. To accomplish this, statistical models, trained simultaneously on both the micro-Doppler modulations induced by human actions and symbolic representations of skeletal poses, are developed. This enables the model to learn correlations between the rich temporal structure of the micro-Doppler modulations and the high-dimensional motion sequences of human action. During runtime, the model then relies purely on the active acoustic data to infer the human action. In order to adapt this methodology to situations not observed in the training data, a physical model of the human body is combined with a physics-based simulation of the Doppler phenomenon to predict the acoustic data for a sequence of skeletal poses and a con gurable sensor geometry. The physics model is then combined with a generative statistical model for human actions to create a generative physics-based model of micro-Doppler modulations for human action