290 research outputs found
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
DESIGN AND EVALUATION OF HARMONIC SPEECH ENHANCEMENT AND BANDWIDTH EXTENSION
Improving the quality and intelligibility of speech signals continues to be an important topic in mobile communications and hearing aid applications. This thesis explored the possibilities of improving the quality of corrupted speech by cascading a log Minimum Mean Square Error (logMMSE) noise reduction system with a Harmonic Speech Enhancement (HSE) system. In HSE, an adaptive comb filter is deployed to harmonically filter the useful speech signal and suppress the noisy components to noise floor. A Bandwidth Extension (BWE) algorithm was applied to the enhanced speech for further improvements in speech quality. Performance of this algorithm combination was evaluated using objective speech quality metrics across a variety of noisy and reverberant environments. Results showed that the logMMSE and HSE combination enhanced the speech quality in any reverberant environment and in the presence of multi-talker babble. The objective improvements associated with the BWE were found to be minima
Robust speaker recognition using both vocal source and vocal tract features estimated from noisy input utterances.
Wang, Ning.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 106-115).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Introduction to Speech and Speaker Recognition --- p.1Chapter 1.2 --- Difficulties and Challenges of Speaker Authentication --- p.6Chapter 1.3 --- Objectives and Thesis Outline --- p.7Chapter 2 --- Speaker Recognition System --- p.10Chapter 2.1 --- Baseline Speaker Recognition System Overview --- p.10Chapter 2.1.1 --- Feature Extraction --- p.12Chapter 2.1.2 --- Pattern Generation and Classification --- p.24Chapter 2.2 --- Performance Evaluation Metric for Different Speaker Recognition Tasks --- p.30Chapter 2.3 --- Robustness of Speaker Recognition System --- p.30Chapter 2.3.1 --- Speech Corpus: CU2C --- p.30Chapter 2.3.2 --- Noise Database: NOISEX-92 --- p.34Chapter 2.3.3 --- Mismatched Training and Testing Conditions --- p.35Chapter 2.4 --- Summary --- p.37Chapter 3 --- Speaker Recognition System using both Vocal Tract and Vocal Source Features --- p.38Chapter 3.1 --- Speech Production Mechanism --- p.39Chapter 3.1.1 --- Speech Production: An Overview --- p.39Chapter 3.1.2 --- Acoustic Properties of Human Speech --- p.40Chapter 3.2 --- Source-filter Model and Linear Predictive Analysis --- p.44Chapter 3.2.1 --- Source-filter Speech Model --- p.44Chapter 3.2.2 --- Linear Predictive Analysis for Speech Signal --- p.46Chapter 3.3 --- Vocal Tract Features --- p.51Chapter 3.4 --- Vocal Source Features --- p.52Chapter 3.4.1 --- Source Related Features: An Overview --- p.52Chapter 3.4.2 --- Source Related Features: Technical Viewpoints --- p.54Chapter 3.5 --- Effects of Noises on Speech Properties --- p.55Chapter 3.6 --- Summary --- p.61Chapter 4 --- Estimation of Robust Acoustic Features for Speaker Discrimination --- p.62Chapter 4.1 --- Robust Speech Techniques --- p.63Chapter 4.1.1 --- Noise Resilience --- p.64Chapter 4.1.2 --- Speech Enhancement --- p.64Chapter 4.2 --- Spectral Subtractive-Type Preprocessing --- p.65Chapter 4.2.1 --- Noise Estimation --- p.66Chapter 4.2.2 --- Spectral Subtraction Algorithm --- p.66Chapter 4.3 --- LP Analysis of Noisy Speech --- p.67Chapter 4.3.1 --- LP Inverse Filtering: Whitening Process --- p.68Chapter 4.3.2 --- Magnitude Response of All-pole Filter in Noisy Condition --- p.70Chapter 4.3.3 --- Noise Spectral Reshaping --- p.72Chapter 4.4 --- Distinctive Vocal Tract and Vocal Source Feature Extraction . . --- p.73Chapter 4.4.1 --- Vocal Tract Feature Extraction --- p.73Chapter 4.4.2 --- Source Feature Generation Procedure --- p.75Chapter 4.4.3 --- Subband-specific Parameterization Method --- p.79Chapter 4.5 --- Summary --- p.87Chapter 5 --- Speaker Recognition Tasks & Performance Evaluation --- p.88Chapter 5.1 --- Speaker Recognition Experimental Setup --- p.89Chapter 5.1.1 --- Task Description --- p.89Chapter 5.1.2 --- Baseline Experiments --- p.90Chapter 5.1.3 --- Identification and Verification Results --- p.91Chapter 5.2 --- Speaker Recognition using Source-tract Features --- p.92Chapter 5.2.1 --- Source Feature Selection --- p.92Chapter 5.2.2 --- Source-tract Feature Fusion --- p.94Chapter 5.2.3 --- Identification and Verification Results --- p.95Chapter 5.3 --- Performance Analysis --- p.98Chapter 6 --- Conclusion --- p.102Chapter 6.1 --- Discussion and Conclusion --- p.102Chapter 6.2 --- Suggestion of Future Work --- p.10
Analysis of very low quality speech for mask-based enhancement
The complexity of the speech enhancement problem has motivated many different solutions. However, most techniques address situations in which the target speech is fully intelligible and the background noise energy is low in comparison with that of the speech. Thus while current enhancement algorithms can improve the perceived quality, the intelligibility of the speech is not increased significantly and may even be reduced.
Recent research shows that intelligibility of very noisy speech can be improved by the use of a binary mask, in which a binary weight is applied to each time-frequency bin of the input spectrogram. There are several alternative goals for the binary mask estimator, based either on the Signal-to-Noise Ratio (SNR) of each time-frequency bin or on the speech signal characteristics alone. Our approach to the binary mask estimation problem aims to preserve the important speech cues independently of the noise present by identifying time-frequency regions that contain significant speech energy.
The speech power spectrum varies greatly for different types of speech sound. The energy of voiced speech sounds is concentrated in the harmonics of the fundamental frequency while that of unvoiced sounds is, in contrast, distributed across a broad range of frequencies. To identify the presence of speech energy in a noisy speech signal we have therefore developed two detection algorithms. The first is a robust algorithm that identifies voiced speech segments and estimates their fundamental frequency. The second detects the presence of sibilants and estimates their energy distribution. In addition, we have developed a robust algorithm to estimate the active level of the speech. The outputs of these algorithms are combined with other features estimated from the noisy speech to form the input to a classifier which estimates a mask that accurately reflects the time-frequency distribution of speech energy even at low SNR levels. We evaluate a mask-based speech enhancer on a range of speech and noise signals and demonstrate a consistent increase in an objective intelligibility measure with respect to noisy speech.Open Acces
Studies on noise robust automatic speech recognition
Noise in everyday acoustic environments such as cars, traffic environments, and cafeterias remains one of the main challenges in automatic speech recognition (ASR). As a research theme, it has received wide attention in conferences and scientific journals focused on speech technology. This article collection reviews both the classic and novel approaches suggested for noise robust ASR. The articles are literature reviews written for the spring 2009 seminar course on noise robust automatic speech recognition (course code T-61.6060) held at TKK
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
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