Comparing spectrum estimators in speaker verification under additive noise degradation

Bu çalışma, 25-30 Mart 2012 tarihleri arasında Kyoto[Japonya]’da düzenlenen IEEE International Conference on Acoustics, Speech and Signal Processing’da bildiri olarak sunulmuştur.Different short-term spectrum estimators for speaker verification under additive noise are considered. Conventionally, mel-frequency cepstral coefficients (MFCCs) are computed from discrete Fourier transform (DFT) spectra of windowed speech frames. Recently, linear prediction (LP) and its temporally weighted variants have been substituted as the spectrum analysis method in speech and speaker recognition. In this paper, 12 different short-term spectrum estimation methods are compared for speaker verification under additive noise contamination. Experimental results conducted on NIST 2002 SRE show that the spectrum estimation method has a large effect on recognition performance and stabilized weighted LP (SWLP) and minimum variance distortionless response (MVDR) methods yield approximately 7 % and 8 % relative improvements over the standard DFT method at -10 dB SNR level of factory and babble noises, respectively in terms of equal error rate (EER).Inst Elect & Elect Engineers, Signal Processing SocIEE

Using group delay functions from all-pole models for speaker recognition

Bu çalışma, 25-29 Ağustos 2013 tarihlerinde Lyon[Fransa]'da düzenlenen 14. Annual Conference of the International Speech Communication Association [Interspeech 2013]'da bildiri olarak sunulmuştur.Popular features for speech processing, such as mel-frequency cepstral coefficients (MFCCs), are derived from the short-term magnitude spectrum, whereas the phase spectrum remains unused. While the common argument to use only the magnitude spectrum is that the human ear is phase-deaf, phase-based features have remained less explored due to additional signal processing difficulties they introduce. A useful representation of the phase is the group delay function, but its robust computation remains difficult. This paper advocates the use of group delay functions derived from parametric all-pole models instead of their direct computation from the discrete Fourier transform. Using a subset of the vocal effort data in the NIST 2010 speaker recognition evaluation (SRE) corpus, we show that group delay features derived via parametric all-pole models improve recognition accuracy, especially under high vocal effort. Additionally, the group delay features provide comparable or improved accuracy over conventional magnitude-based MFCC features. Thus, the use of group delay functions derived from all-pole models provide an effective way to utilize information from the phase spectrum of speech signals.Academy of Finland (253120)Int Speech Commun AssociationAmazonMicrosoftGoogleTcL SYTRALEuropean Language Resources AssociationOuaeroImaginoveVOCAPIA ResearchAcapelaSpeech OceanALDEBARANOrangeVecsysIBM ResearchRaytheon BBN TechnologyVoxyge

Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition

Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays, the major challenge is to achieve good robustness in adverse noisy conditions so that automatic speech recognizers can be used in real situations. Spectral subtraction (SS) is a well-known and effective approach; it was originally designed for improving the quality of speech signal judged by human listeners. SS techniques usually improve the quality and intelligibility of speech signal while speech recognition systems need compensation techniques to reduce mismatch between noisy speech features and clean trained acoustic model. Nevertheless, correlation can be expected between speech quality improvement and the increase in recognition accuracy. This paper proposes a novel approach for solving this problem by considering SS and the speech recognizer not as two independent entities cascaded together, but rather as two interconnected components of a single system, sharing the common goal of improved speech recognition accuracy. This will incorporate important information of the statistical models of the recognition engine as a feedback for tuning SS parameters. By using this architecture, we overcome the drawbacks of previously proposed methods and achieve better recognition accuracy. Experimental evaluations show that the proposed method can achieve significant improvement of recognition rates across a wide range of signal to noise ratios

Single-Microphone Speech Enhancement and Separation Using Deep Learning

The cocktail party problem comprises the challenging task of understanding a speech signal in a complex acoustic environment, where multiple speakers and background noise signals simultaneously interfere with the speech signal of interest. A signal processing algorithm that can effectively increase the speech intelligibility and quality of speech signals in such complicated acoustic situations is highly desirable. Especially for applications involving mobile communication devices and hearing assistive devices. Due to the re-emergence of machine learning techniques, today, known as deep learning, the challenges involved with such algorithms might be overcome. In this PhD thesis, we study and develop deep learning-based techniques for two sub-disciplines of the cocktail party problem: single-microphone speech enhancement and single-microphone multi-talker speech separation. Specifically, we conduct in-depth empirical analysis of the generalizability capability of modern deep learning-based single-microphone speech enhancement algorithms. We show that performance of such algorithms is closely linked to the training data, and good generalizability can be achieved with carefully designed training data. Furthermore, we propose uPIT, a deep learning-based algorithm for single-microphone speech separation and we report state-of-the-art results on a speaker-independent multi-talker speech separation task. Additionally, we show that uPIT works well for joint speech separation and enhancement without explicit prior knowledge about the noise type or number of speakers. Finally, we show that deep learning-based speech enhancement algorithms designed to minimize the classical short-time spectral amplitude mean squared error leads to enhanced speech signals which are essentially optimal in terms of STOI, a state-of-the-art speech intelligibility estimator.Comment: PhD Thesis. 233 page

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

Applications of missing feature theory to speaker recognition

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 100-101).An important problem in speaker recognition is the degradation that occurs when speaker models trained with speech from one type of channel are used to score speech from another type of channel, known as channel mismatch. This thesis investigates various channel compensation techniques and approaches from missing feature theory for improving Gaussian mixture model (GMM)-based speaker verification under this mismatch condition. Experiments are performed using a speech corpus consisting of "clean" training speech and "dirty" test speech equal to the clean speech corrupted by additive Gaussian noise. Channel compensation methods studied are cepstral mean subtraction, RASTA, and spectral subtraction. Approaches to missing feature theory include missing feature compensation, which removes corrupted features, and missing feature restoration which predicts such features from neighboring features in both frequency and time. These methods are investigated both individually and in combination. In particular, missing feature compensation combined with spectral subtraction in the discrete Fourier transform domain significantly improves GMM speaker verification accuracy and outperforms all other methods examined in this thesis, reducing the equal error rate by about 10% more than other methods over a SNR range of 5-25 dB. Moreover, this considerably outperforms a state-of-the-art GMM recognizer for the mismatch application that combines missing feature theory with spectral subtraction developed in a mel-filter energy domain. Finally, the concept of missing restoration is explored. A novel linear minimum mean-squared-error missing feature estimator is derived and applied to pure vowels as well as a clean/dirty verification trial. While it does not improve performance in the verification trial, a large SNR improvement for features estimated for the pure vowel case indicate promise in the application of this method.by Michael Thomas Padilla.S.M