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

Speech Enhancement Exploiting the Source-Filter Model

Imagining everyday life without mobile telephony is nowadays hardly possible. Calls are being made in every thinkable situation and environment. Hence, the microphone will not only pick up the user’s speech but also sound from the surroundings which is likely to impede the understanding of the conversational partner. Modern speech enhancement systems are able to mitigate such effects and most users are not even aware of their existence. In this thesis the development of a modern single-channel speech enhancement approach is presented, which uses the divide and conquer principle to combat environmental noise in microphone signals. Though initially motivated by mobile telephony applications, this approach can be applied whenever speech is to be retrieved from a corrupted signal. The approach uses the so-called source-filter model to divide the problem into two subproblems which are then subsequently conquered by enhancing the source (the excitation signal) and the filter (the spectral envelope) separately. Both enhanced signals are then used to denoise the corrupted signal. The estimation of spectral envelopes has quite some history and some approaches already exist for speech enhancement. However, they typically neglect the excitation signal which leads to the inability of enhancing the fine structure properly. Both individual enhancement approaches exploit benefits of the cepstral domain which offers, e.g., advantageous mathematical properties and straightforward synthesis of excitation-like signals. We investigate traditional model-based schemes like Gaussian mixture models (GMMs), classical signal processing-based, as well as modern deep neural network (DNN)-based approaches in this thesis. The enhanced signals are not used directly to enhance the corrupted signal (e.g., to synthesize a clean speech signal) but as so-called a priori signal-to-noise ratio (SNR) estimate in a traditional statistical speech enhancement system. Such a traditional system consists of a noise power estimator, an a priori SNR estimator, and a spectral weighting rule that is usually driven by the results of the aforementioned estimators and subsequently employed to retrieve the clean speech estimate from the noisy observation. As a result the new approach obtains significantly higher noise attenuation compared to current state-of-the-art systems while maintaining a quite comparable speech component quality and speech intelligibility. In consequence, the overall quality of the enhanced speech signal turns out to be superior as compared to state-of-the-art speech ehnahcement approaches.Mobiltelefonie ist aus dem heutigen Leben nicht mehr wegzudenken. Telefonate werden in beliebigen Situationen an beliebigen Orten geführt und dabei nimmt das Mikrofon nicht nur die Sprache des Nutzers auf, sondern auch die Umgebungsgeräusche, welche das Verständnis des Gesprächspartners stark beeinflussen können. Moderne Systeme können durch Sprachverbesserungsalgorithmen solchen Effekten entgegenwirken, dabei ist vielen Nutzern nicht einmal bewusst, dass diese Algorithmen existieren. In dieser Arbeit wird die Entwicklung eines einkanaligen Sprachverbesserungssystems vorgestellt. Der Ansatz setzt auf das Teile-und-herrsche-Verfahren, um störende Umgebungsgeräusche aus Mikrofonsignalen herauszufiltern. Dieses Verfahren kann für sämtliche Fälle angewendet werden, in denen Sprache aus verrauschten Signalen extrahiert werden soll. Der Ansatz nutzt das Quelle-Filter-Modell, um das ursprüngliche Problem in zwei Unterprobleme aufzuteilen, die anschließend gelöst werden, indem die Quelle (das Anregungssignal) und das Filter (die spektrale Einhüllende) separat verbessert werden. Die verbesserten Signale werden gemeinsam genutzt, um das gestörte Mikrofonsignal zu entrauschen. Die Schätzung von spektralen Einhüllenden wurde bereits in der Vergangenheit erforscht und zum Teil auch für die Sprachverbesserung angewandt. Typischerweise wird dabei jedoch das Anregungssignal vernachlässigt, so dass die spektrale Feinstruktur des Mikrofonsignals nicht verbessert werden kann. Beide Ansätze nutzen jeweils die Eigenschaften der cepstralen Domäne, die unter anderem vorteilhafte mathematische Eigenschaften mit sich bringen, sowie die Möglichkeit, Prototypen eines Anregungssignals zu erzeugen. Wir untersuchen modellbasierte Ansätze, wie z.B. Gaußsche Mischmodelle, klassische signalverarbeitungsbasierte Lösungen und auch moderne tiefe neuronale Netzwerke in dieser Arbeit. Die so verbesserten Signale werden nicht direkt zur Sprachsignalverbesserung genutzt (z.B. Sprachsynthese), sondern als sogenannter A-priori-Signal-zu-Rauschleistungs-Schätzwert in einem traditionellen statistischen Sprachverbesserungssystem. Dieses besteht aus einem Störleistungs-Schätzer, einem A-priori-Signal-zu-Rauschleistungs-Schätzer und einer spektralen Gewichtungsregel, die üblicherweise mit Hilfe der Ergebnisse der beiden Schätzer berechnet wird. Schließlich wird eine Schätzung des sauberen Sprachsignals aus der Mikrofonaufnahme gewonnen. Der neue Ansatz bietet eine signifikant höhere Dämpfung des Störgeräuschs als der bisherige Stand der Technik. Dabei wird eine vergleichbare Qualität der Sprachkomponente und der Sprachverständlichkeit gewährleistet. Somit konnte die Gesamtqualität des verbesserten Sprachsignals gegenüber dem Stand der Technik erhöht werden

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

Reverberation: models, estimation and application

The use of reverberation models is required in many applications such as acoustic measurements, speech dereverberation and robust automatic speech recognition. The aim of this thesis is to investigate different models and propose a perceptually-relevant reverberation model with suitable parameter estimation techniques for different applications. Reverberation can be modelled in both the time and frequency domain. The model parameters give direct information of both physical and perceptual characteristics. These characteristics create a multidimensional parameter space of reverberation, which can be to a large extent captured by a time-frequency domain model. In this thesis, the relationship between physical and perceptual model parameters will be discussed. In the first application, an intrusive technique is proposed to measure the reverberation or reverberance, perception of reverberation and the colouration. The room decay rate parameter is of particular interest. In practical applications, a blind estimate of the decay rate of acoustic energy in a room is required. A statistical model for the distribution of the decay rate of the reverberant signal named the eagleMax distribution is proposed. The eagleMax distribution describes the reverberant speech decay rates as a random variable that is the maximum of the room decay rates and anechoic speech decay rates. Three methods were developed to estimate the mean room decay rate from the eagleMax distributions alone. The estimated room decay rates form a reverberation model that will be discussed in the context of room acoustic measurements, speech dereverberation and robust automatic speech recognition individually

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Sistema de reconhecimento de escalas musicais utilizando transformação rápida de Fourier e Arduino

Trabalho de Conclusão de Curso, apresentado para obtenção do grau de Bacharel no Curso de Ciência da Computação da Universidade do Extremo Sul Catarinense, UNESC.A música é uma manifestação artística comum, seja como hobby ou como atividade profissional, ela é uma constante no dia a dia de um grande número de pessoas. Visando ampliar a disponibilidade de opções do uso da tecnologia na música, esta pesquisa tem como objetivo aplicar o algoritmo da transformação rápida de Fourier no desenvolvimento de um sistema capaz de identificar escalas músicas oriundas de um instrumento elétrico. A aplicação pode ler e classificar musicalmente frequências entre 134 Hz e 1040 Hz

OBJECTIVE AND SUBJECTIVE EVALUATION OF DEREVERBERATION ALGORITHMS

Reverberation significantly impacts the quality and intelligibility of speech. Several dereverberation algorithms have been proposed in the literature to combat this problem. A majority of these algorithms utilize a single channel and are developed for monaural applications, and as such do not preserve the cues necessary for sound localization. This thesis describes a blind two-channel dereverberation technique that improves the quality of speech corrupted by reverberation while preserving cues that affect localization. The method is based by combining a short term (2ms) and long term (20ms) weighting function of the linear prediction (LP) residual of the input signal. The developed and other dereverberation algorithms are evaluated objectively and subjectively in terms of sound quality and localization accuracy. The binaural adaptation provides a significant increase in sound quality while removing the loss in localization ability found in the bilateral implementation

Prediction-driven computational auditory scene analysis

The sound of a busy environment, such as a city street, gives rise to a perception of numerous distinct events in a human listener--the 'auditory scene analysis' of the acoustic information. Recent advances in the understanding of this process from experimental psychoacoustics have led to several efforts to build a computer model capable of the same function. This work is known as 'computational auditory scene analysis'. The dominant approach to this problem has been as a sequence of modules, the output of one forming the input to the next. Sound is converted to its spectrum, cues are picked out, and representations of the cues are grouped into an abstract description of the initial input. This 'data-driven' approach has some specific weaknesses in comparison to the auditory system: it will interpret a given sound in the same way regardless of its context, and it cannot 'infer' the presence of a sound for which direct evidence is hidden by other components. The 'prediction-driven' approach is presented as an alternative, in which analysis is a process of reconciliation between the observed acoustic features and the predictions of an internal model of the sound-producing entities in the environment. In this way, predicted sound events will form part of the scene interpretation as long as they are consistent with the input sound, regardless of whether direct evidence is found. A blackboard-based implementation of this approach is described which analyzes dense, ambient sound examples into a vocabulary of noise clouds, transient clicks, and a correlogram-based representation of wide-band periodic energy called the weft. The system is assessed through experiments that firstly investigate subjects' perception of distinct events in ambient sound examples, and secondly collect quality judgments for sound events resynthesized by the system. Although rated as far from perfect, there was good agreement between the events detected by the model and by the listeners. In addition, the experimental procedure does not depend on special aspects of the algorithm (other than the generation of resyntheses), and is applicable to the assessment and comparison of other models of human auditory organization

Aspiration noise during phonation : synthesis, analysis, and pitch-scale modification

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 139-145).The current study investigates the synthesis and analysis of aspiration noise in synthesized and spoken vowels. Based on the linear source-filter model of speech production, we implement a vowel synthesizer in which the aspiration noise source is temporally modulated by the periodic source waveform. Modulations in the noise source waveform and their synchrony with the periodic source are shown to be salient for natural-sounding vowel synthesis. After developing the synthesis framework, we research past approaches to separate the two additive components of the model. A challenge for analysis based on this model is the accurate estimation of the aspiration noise component that contains energy across the frequency spectrum and temporal characteristics due to modulations in the noise source. Spectral harmonic/noise component analysis of spoken vowels shows evidence of noise modulations with peaks in the estimated noise source component synchronous with both the open phase of the periodic source and with time instants of glottal closure. Inspired by this observation of natural modulations in the aspiration noise source, we develop an alternate approach to the speech signal processing aim of accurate pitch-scale modification. The proposed strategy takes a dual processing approach, in which the periodic and noise components of the speech signal are separately analyzed, modified, and re-synthesized. The periodic component is modified using our implementation of time-domain pitch-synchronous overlap-add, and the noise component is handled by modifying characteristics of its source waveform.(cont.) Since we have modeled an inherent coupling between the original periodic and aspiration noise sources, the modification algorithm is designed to preserve the synchrony between temporal modulations of the two sources. The reconstructed modified signal is perceived to be natural-sounding and generally reduces artifacts that are typically heard in current modification techniques.by Daryush Mehta.S.M

Features of hearing: applications of machine learning to uncover the building blocks of hearing

Recent advances in machine learning have instigated a renewed interest in using machine learning approaches to better understand human sensory processing. This line of research is particularly interesting for speech research since speech comprehension is uniquely human, which complicates obtaining detailed neural recordings. In this thesis, I explore how machine learning can be used to uncover new knowledge about the auditory system, with a focus on discovering robust auditory features. The resulting increased understanding of the noise robustness of human hearing may help to better assist those with hearing loss and improve Automatic Speech Recognition (ASR) systems. First, I show how computational neuroscience and machine learning can be combined to generate hypotheses about auditory features. I introduce a neural feature detection model with a modest number of parameters that is compatible with auditory physiology. By testing feature detector variants in a speech classification task, I confirm the importance of both well-studied and lesser-known auditory features. Second, I investigate whether ASR software is a good candidate model of the human auditory system. By comparing several state-of-the-art ASR systems to the results from humans on a range of psychometric experiments, I show that these ASR systems diverge markedly from humans in at least some psychometric tests. This implies that none of these systems act as a strong proxy for human speech recognition, although some may be useful when asking more narrowly defined questions. For neuroscientists, this thesis exemplifies how machine learning can be used to generate new hypotheses about human hearing, while also highlighting the caveats of investigating systems that may work fundamentally differently from the human brain. For machine learning engineers, I point to tangible directions for improving ASR systems. To motivate the continued cross-fertilization between these fields, a toolbox that allows researchers to assess new ASR systems has been released.Open Acces