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检索条件"任意字段=2006 ISCA Tutorial and Research Workshop on Statistical and Perceptual Audio Processing, SAPA 2006"

共 21 条记录，以下是1-10 订阅

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Discriminative Word-Spotting Using Ordered Spectro-Temporal Patch Features

Discriminative Word-Spotting Using Ordered Spectro-Temporal ...

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2008 isca tutorial and research workshop on statistical and perceptual audio processing, sapa 2008

作者： Ezzat, Tony Poggio, Tomaso Center for Biological and Computational Learning McGovern Institute for Brain Research Massachusetts Institute of Technology CambridgeMA United States

We present a novel architecture for word-spotting which is trained from a small number of examples to classify an utterance as containing a target keyword or not. The word-spotting architecture relies on a novel feature set consisting of a set of ordered spectro-temporal patches which are extracted from the exemplar mel-spectra of target keywords. A local pooling operation across frequency and time is introduced which endows the extracted patch features with the flexibility to match novel unseen keywords. Finally, we describe how to train a support vector machine classifier to separate between keyword and nonkeyword patch feature responses. We present preliminary results indicating that our word-spotting architecture achieves a detection rate of 70-95% with false positive rates of about 0.25- 2 false positives per minute. © 2008 ITRW on statistical and perceptual audio processing, sapa 2008. All rights reserved.

关键词： Support vector machines

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ITRW on statistical and perceptual audio processing, sapa 2008

ITRW on Statistical and Perceptual Audio Processing, SAPA 20...

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2008 isca tutorial and research workshop on statistical and perceptual audio processing, sapa 2008

The proceedings contain 7 papers. The topics discussed include: computational auditory induction by missing-data non-negative matrix factorization;singing voice detection using modulation frequency feature;preliminary intelligibility tests of a monaural speech segregation system;combining pitch-based inference and non-negative spectrogram factorization in separating vocals from polyphonic music;explicit consistency constraints for STFT spectrograms and their application to phase reconstruction;data-driven articulatory inversion incorporating articulator priors;and discriminative word-spotting using ordered spectro-temporal patch features.

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Preliminary Intelligibility Tests of a Monaural Speech Segregation System

Preliminary Intelligibility Tests of a Monaural Speech Segre...

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2008 isca tutorial and research workshop on statistical and perceptual audio processing, sapa 2008

作者： Hu, Ke Divenyi, Pierre Ellis, Dan Jin, Zhaozhang Shinn-Cunningham, Barbara G. Wang, Deliang Department of Computer Science & Engineering Center for Cognitive Science The Ohio State University ColumbusOH43210 United States Speech and Hearing Research VA Medical Center MartinezCA94553 United States Department of Electrical Engineering Columbia University New YorkNY10027 United States Departments of Cognitive & Neural Systems and Biomedical Engineering Boston University BostonMA02215 United States

Human listeners are able to understand speech in the presence of a noisy background. How to simulate this perceptual ability remains a great challenge. This paper describes a preliminary evaluation of intelligibility of the output of a monaural speech segregation system. The system performs speech segregation in two stages. The first stage segregates voiced speech using supervised learning of harmonic features, and the second stage segregates unvoiced speech by subtracting noise energy that is estimated from voiced intervals and onset/offset based segmentation. Objective evaluation in terms of the match to ideal binary time-frequency masks shows substantial improvements. Tests with human subjects indicate that the system improves intelligibility for young listeners when the input SNR is very low, but does not aid elderly listeners. This preliminary evaluation identifies aspects of the system that should be improved in order to produce consistent improvement in intelligibility in noisy environments. © 2008 ITRW on statistical and perceptual audio processing, sapa 2008. All rights reserved.

关键词： Supervised learning

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A statistical model of timbre perception

A statistical model of timbre perception

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2006 isca tutorial and research workshop on statistical and perceptual audio processing, sapa 2006

作者： Terasawa, Hiroko Slaney, Malcolm Berger, Jonathan CCRMA Department of Music Stanford University Stanford CA United States Yahoo! Research SunnyvaleCA United States

We describe a perceptual space for timbre, define an objective metric that takes into account perceptual orthogonality and measure the quality of timbre interpolation. We discuss two timbre representations and measure perceptual judgments on an equivalent range of timbre variety. We determine that a timbre space based on Mel-frequency cepstral coefficients (MFCC) is a good model for a perceptual timbre space. © sapa 2006. All rights reserved.

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Improving the Noise-Robustness of Mel-Frequency Cepstral Coefficients for Speech processing

Improving the Noise-Robustness of Mel-Frequency Cepstral Coe...

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2006 isca tutorial and research workshop on statistical and perceptual audio processing, sapa 2006

作者： Ravindran, Sourabh Anderson, David V. Slaney, Malcolm School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Yahoo! Research 701 First Avenue SunnyvaleCA94089 United States

In this paper we study the noise-robustness of mel-frequency cepstral coefficients (MFCCs) and explore ways to improve their performance in noisy conditions. Improvements based on a more accurate model of the early auditory system are suggested to make the MFCC features more robust to noise while preserving their class discrimination ability. Speech versus non-speech classification and speech recognition are chosen to evaluate the performance gains afforded by the modifications. © sapa 2006. All rights reserved.

关键词： Speech processing

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The Iroquois Model: Using Temporal Dynamics to Separate Speakers

The Iroquois Model: Using Temporal Dynamics to Separate Spea...

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2006 isca tutorial and research workshop on statistical and perceptual audio processing, sapa 2006

作者： Rennie, Steven Olsen, Peder Hershey, John Kristjansson, Trausti IBM T. J. Watson Research Center Yorktown Heights NY United States

We describe a system that can separate and recognize the simultaneous speech of two speakers from a single channel recording and compare the performance of the system to that of human subjects. The system, which we call Iroquois, uses models of dynamics to achieve performance near that of human listeners. However the system exhibits a pattern of performance across conditions that is different from that of human subjects. In conditions where the amplitude of the speakers is similar, the Iroquois model surpasses human performance by over 50%. We hypothesize that the system accomplishes this remarkable feat by employing a different strategy to that of the human auditory system. © sapa 2006. All rights reserved.

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ITRW on statistical and perceptual audio processing, sapa 2006

ITRW on Statistical and Perceptual Audio Processing, SAPA 20...

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2006 isca tutorial and research workshop on statistical and perceptual audio processing, sapa 2006

The proceedings contain 12 papers. The topics discussed include: a probability model for interaural phase difference;sparseness and speech perception in noise;frequency component restoration for music sounds using local probabilistic models with maximum entropy learning;the Iroquois model: using temporal dynamics to separate speakers;estimating single-channel source separation masks: relevance vector machine classifiers vs. pitch-based masking;structuring time domain blind source separation algorithms for CASA integration;Leak energy based missing feature mask generation for ICA and GSS and its evaluation with simultaneous speech recognition;improving the noise-robustness of mel-frequency cepstral coefficients for speech processing;and speech recognition for a robot under its motor noises by selective application of missing feature theory and MLLR.

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Structuring Time Domain Blind Source Separation Algorithms for CASA Integration

Structuring Time Domain Blind Source Separation Algorithms f...

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2006 isca tutorial and research workshop on statistical and perceptual audio processing, sapa 2006

作者： Schölling, Björn Heckmann, Martin Joublin, Frank Goerick, Christian Honda Research Institute Europe GmbH Offenbach am MainD-63073 Germany Control Theory and Robotics Lab Darmstadt University of Technology DarmstadtD-64283 Germany

Most algorithms based on Computational Auditory Scene Analysis (CASA) for binaural speech separation do not have the ability to inhibit already localized and for a long time present sources in the auditory scene. This has the major drawback that the auditory cues of weaker and new sources are subject to interference from already localized and perceived signals and the separation performance is worse if the signals overlap in their processing domain. In this paper we outline how one can build intuitively a separation system that has this inhibition feature. The main block and starting point of our derivation is a simple cross correlation based localization system with two microphones. The inhibition is achieved by feeding back localization results to a filter and sum structure that cancels localized sounds. Interestingly, our intuitive approach leads to a special case of a well known time domain blind source separation algorithm which was derived from a statistical signal processing viewpoint and exhibits good convergence even in reverberant environments. Finally, we discuss how the insights gained from building a blind source separation this way can be used to integrate CASA techniques. © sapa 2006. All rights reserved.

关键词： Blind source separation

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Speech Recognition for a Robot under its Motor Noises by Selective Application of Missing Feature Theory and MLLR

Speech Recognition for a Robot under its Motor Noises by Sel...

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2006 isca tutorial and research workshop on statistical and perceptual audio processing, sapa 2006

作者： Nishimura, Yoshitaka Nakano, Mikio Nakadai, Kazuhiro Tsujino, Hiroshi Ishizuka, Mitsuru Graduate School of Information Science and Technology University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Honda Research Institute Japan Co. Ltd. 8-1 Honcho Wako-shi Saitama351-0114 Japan

Automatic speech recognition (ASR) is essential for a robot to communicate with people. One of the main problems with ASR for robots is that robots inevitably generate motor noises. The noise is captured with strong power by the robot's microphones, because the noise sources are closer to the microphones than the target speech source. The signal-to-noise ratio of input speech becomes quite low (less than 0 dB). However, it is possible to estimate the noise by using information on the robot's own motions and postures, because a type of motion/gesture produces almost the same pattern of noise every time it is performed. This paper proposes a method to improve ASR under motor noises by using the information on the robot's motion/gesture. The method selectively uses three techniques - multi-condition training, maximum-likelihood linear regression (MLLR), and missing feature theory (MFT). The former two techniques cope with the motor noises by selecting the noise-type-dependent acoustic model corresponding to a performing motion/gesture. The last technique extracts unreliable acoustic features in an input sound by matching the input with a pre-recorded noise of the current motion/gesture, and masks them in speech recognition to improve ASR performance. Because, in our method, ASR technique selection affects the systems performance, we evaluated the performance of three ASRs for each noise type of a robot's motion/gesture to obtain the best technique selection rule. The preliminary results of isolated word recognition showed the effectiveness of our method using the obtained technique selection rule. © sapa 2006. All rights reserved.

关键词： Robots

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Leak Energy Based Missing Feature Mask Generation for ICA and GSS and Its Evaluation with Simultaneous Speech Recognition

Leak Energy Based Missing Feature Mask Generation for ICA an...

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2006 isca tutorial and research workshop on statistical and perceptual audio processing, sapa 2006

作者： Yamamoto, Shun'ichi Takeda, Ryu Nakadai, Kazuhiro Nakano, Mikio Tsujino, Hiroshi Valin, Jean-Marc Komatani, Kazunori Ogata, Tetsuya Okuno, Hiroshi G. Graduate School of Informatics Kyoto University Kyoto Japan Honda Research Institute Japan Co. Ltd. Saitama Japan CSIRO ICT Center MarsfieldNSW Australia

This paper addresses automatic speech recognition (ASR) for robots integrated with sound source separation (SSS) by using leak noise based missing feature mask generation. The missing feature theory (MFT) is a promising approach to improve noise-robustness of ASR. An issue in MFT-based ASR is automatic generation of the missing feature mask. To improve robot audition, we applied this theory to interface ASR and SSS which extracts a sound source originated from a specific direction by multiple microphones. In a robot audition system, it is a promising approach to use SSS as a pre-processor for ASR to be able to deal with any kind of noises. However, ASR usually assumes clean speech input, while speech extracted by SSS never fails to be distorted. MFT can be applied to cope with distortion in the extracted speech. In this case, we can assume that the noises included in extracted sounds are mainly leakages from other channels. Thus, we introduced leak noise based missing feature mask generation, which can generate a missing feature mask automatically by using information on leak noise obtained from other channels. To assess the effectiveness of the leak noise based missing feature mask generation, we used two methods for SSS: geometric source separation (GSS) and independent component analysis (ICA), and Multiband Julian for MFT based ASR. The two constructed systems, that is, GSS-based and ICA-based robot audition systems, were evaluated through recognition of simultaneous speech uttered by two speakers. As a result, we showed that the proposed leak noise based missing feature mask generation worked well in both systems. © sapa 2006. All rights reserved.

关键词： Independent component analysis

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