Synergy of Acoustic-Phonetics and Auditory Modeling Towards Robust Speech Recognition

The problem addressed in this work is that of enhancing speech signals corrupted by additive noise and improving the performance of automatic speech recognizers in noisy conditions. The enhanced speech signals can also improve the intelligibility of speech in noisy conditions for human listeners with hearing impairment as well as for normal listeners. The original Phase Opponency (PO) model, proposed to detect tones in noise, simulates the processing of the information in neural discharge times and exploits the frequency-dependent phase properties of the tuned filters in the auditory periphery along with the cross-auditory-nerve-fiber coincidence detection to extract temporal cues. The Modified Phase Opponency (MPO) proposed here alters the components of the PO model in such a way that the basic functionality of the PO model is maintained but the various properties of the model can be analyzed and modified independently of each other. This work presents a detailed mathematical formulation of the MPO model and the relation between the properties of the narrowband signal that needs to be detected and the properties of the MPO model. The MPO speech enhancement scheme is based on the premise that speech signals are composed of a combination of narrow band signals (i.e. harmonics) with varying amplitudes. The MPO enhancement scheme outperforms many of the other speech enhancement techniques when evaluated using different objective quality measures. Automatic speech recognition experiments show that replacing noisy speech signals by the corresponding MPO-enhanced speech signals leads to an improvement in the recognition accuracies at low SNRs. The amount of improvement varies with the type of the corrupting noise. Perceptual experiments indicate that: (a) there is little perceptual difference in the MPO-processed clean speech signals and the corresponding original clean signals and (b) the MPO-enhanced speech signals are preferred over the output of the other enhancement methods when the speech signals are corrupted by subway noise but the outputs of the other enhancement schemes are preferred when the speech signals are corrupted by car noise

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Robust automatic transcription of lectures

Automatic transcription of lectures is becoming an important task. Possible applications can be found in the fields of automatic translation or summarization, information retrieval, digital libraries, education and communication research. Ideally those systems would operate on distant recordings, freeing the presenter from wearing body-mounted microphones. This task, however, is surpassingly difficult, given that the speech signal is severely degraded by background noise and reverberation

Robust Automatic Transcription of Lectures

Die automatische Transkription von Vorträgen, Vorlesungen und Präsentationen wird immer wichtiger und ermöglicht erst die Anwendungen der automatischen Übersetzung von Sprache, der automatischen Zusammenfassung von Sprache, der gezielten Informationssuche in Audiodaten und somit die leichtere Zugänglichkeit in digitalen Bibliotheken. Im Idealfall arbeitet ein solches System mit einem Mikrofon das den Vortragenden vom Tragen eines Mikrofons befreit was der Fokus dieser Arbeit ist

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

Brain-Inspired Computing

This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures

Applications of broad class knowledge for noise robust speech recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 157-164).This thesis introduces a novel technique for noise robust speech recognition by first describing a speech signal through a set of broad speech units, and then conducting a more detailed analysis from these broad classes. These classes are formed by grouping together parts of the acoustic signal that have similar temporal and spectral characteristics, and therefore have much less variability than typical sub-word units used in speech recognition (i.e., phonemes, acoustic units). We explore broad classes formed along phonetic and acoustic dimensions. This thesis first introduces an instantaneous adaptation technique to robustly recognize broad classes in the input signal. Given an initial set of broad class models and input speech data, we explore a gradient steepness metric using the Extended Baum-Welch (EBW) transformations to explain how much these initial model must be adapted to fit the target data. We incorporate this gradient metric into a Hidden Markov Model (HMM) framework for broad class recognition and illustrate that this metric allows for a simple and effective adaptation technique which does not suffer from issues such as data scarcity and computational intensity that affect other adaptation methods such as Maximum a-Posteriori (MAP), Maximum Likelihood Linear Regression (MLLR) and feature-space Maximum Likelihood Linear Regression (fM-LLR). Broad class recognition experiments indicate that the EBW gradient metric method outperforms the standard likelihood technique, both when initial models are adapted via MLLR and without adaptation.(cont.) Next, we explore utilizing broad class knowledge as a pre-processor for segmentbased speech recognition systems, which have been observed to be quite sensitive to noise. The experiments are conducted with the SUMMIT segment-based speech recognizer, which detects landmarks - representing possible transitions between phonemes - from large energy changes in the acoustic signal. These landmarks are often poorly detected in noisy conditions. We investigate using the transitions between broad classes, which typically occur at areas of large acoustic change in the audio signal, to aid in landmark detection. We also explore broad classes motivated along both acoustic and phonetic dimensions. Phonetic recognition experiments indicate that utilizing either phonetically or acoustically motivated broad classes offers significant recognition improvements compared to the baseline landmark method in both stationary and non-stationary noise conditions. Finally, this thesis investigates using broad class knowledge for island-driven search. Reliable regions of a speech signal, known as islands, carry most information in the signal compared to unreliable regions, known as gaps. Most speech recognizers do not differentiate between island and gap regions during search and as a result most of the search computation is spent in unreliable regions. Island-driven search addresses this problem by first identifying islands in the speech signal and directing the search outwards from these islands.(cont.) In this thesis, we develop a technique to identify islands from broad classes which have been confidently identified from the input signal. We explore a technique to prune the search space given island/gap knowledge. Finally, to further limit the amount of computation in unreliable regions, we investigate scoring less detailed broad class models in gap regions and more detailed phonetic models in island regions. Experiments on both small and large scale vocabulary tasks indicate that the island-driven search strategy results in an improvement in recognition accuracy and computation time.by Tara N. Sainath.Ph.D