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191,040
One microphone blind dereverberation based on quasi-periodicity of speech signals
- Advances in Neural Information Processing Systems 16 (NIPS 16
, 2004
"... of speech signals ..."
Correlates of Linguistic Rhythm in the Speech Signal
, 1999
"... This paper presents instrumental measurements based on a consonant/vowel segmentation for eight languages. The measurements suggest that intuitive rhythm types reflect specific phonological properties, which in turn are signaled by the acoustic/phonetic properties of speech. The data support the not ..."
Abstract
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Cited by 199 (10 self)
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This paper presents instrumental measurements based on a consonant/vowel segmentation for eight languages. The measurements suggest that intuitive rhythm types reflect specific phonological properties, which in turn are signaled by the acoustic/phonetic properties of speech. The data support
Speech Signal Representations
"... Source-Filter model • Source Filter model: decomposition of speech signals-– A source passed through a linear time-varying filter • But assume that the filter is short-time time-invariant ..."
Abstract
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Source-Filter model • Source Filter model: decomposition of speech signals-– A source passed through a linear time-varying filter • But assume that the filter is short-time time-invariant
THE BAVARIAN ARCHIVE FOR SPEECH SIGNALS
"... The Bavarian Archive for Speech Signals Til was foun ded as a nonprot institution in January and is hosted by the Institut f ur Phonetik und Sprachliche Kom ..."
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The Bavarian Archive for Speech Signals Til was foun ded as a nonprot institution in January and is hosted by the Institut f ur Phonetik und Sprachliche Kom
Fractal Modeling of Speech Signals
, 1994
"... this paper, we present a method for speech signal analysis and synthesis based on IFS theory. We consider a speech signal as the graph of a continuous function whose irregularity, measured in terms of its local Holder exponents, is arbitrary. We extract a few remarkable points in the signal and perf ..."
Abstract
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Cited by 5 (2 self)
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this paper, we present a method for speech signal analysis and synthesis based on IFS theory. We consider a speech signal as the graph of a continuous function whose irregularity, measured in terms of its local Holder exponents, is arbitrary. We extract a few remarkable points in the signal
BE 514. Speech Signal Processing
"... The goal of this course is to provide the basic concepts and theories of speech production, speech perception and speech signal processing, and their applications to contemporary speech technology. The course is organized in a manner that builds a strong foundation of basics first, and then concentr ..."
Abstract
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The goal of this course is to provide the basic concepts and theories of speech production, speech perception and speech signal processing, and their applications to contemporary speech technology. The course is organized in a manner that builds a strong foundation of basics first
On Preprocessing of Speech Signals
"... Abstract—Preprocessing of speech signals is considered a crucial step in the development of a robust and efficient speech or speaker recognition system. In this paper, we present some popular statistical outlier-detection based strategies to segregate the silence/unvoiced part of the speech signal f ..."
Abstract
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Cited by 2 (0 self)
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Abstract—Preprocessing of speech signals is considered a crucial step in the development of a robust and efficient speech or speaker recognition system. In this paper, we present some popular statistical outlier-detection based strategies to segregate the silence/unvoiced part of the speech signal
On chaotic nature of speech signals
, 812
"... Various phonemes are considered in terms of nonlinear dynamics. Phase portraits of the signals in the embedded space, correlation dimension estimate and the largest Lyapunov exponent are analyzed. It is shown that the speech signals have comparatively small dimension and the positive largest Lyapuno ..."
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Various phonemes are considered in terms of nonlinear dynamics. Phase portraits of the signals in the embedded space, correlation dimension estimate and the largest Lyapunov exponent are analyzed. It is shown that the speech signals have comparatively small dimension and the positive largest
LPC ANALYSIS OF SPEECH SIGNAL
"... Linear Predictive Coding is most powerful signal analysis technique. LPC is used to compress the spectral information for its efficient storage and transmission. In this paper the LPC is implemented using simulink model in matlab. LPC model consist of two parts analysis and synthesis. In analysis se ..."
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section, the reflection coefficient from input speech signal is extracted and use it to compute residual coefficient. In synthesis section, the original signal is reconstructed using reflection and residual coefficient. The input speech signal of 8 kHz is taken in ‘wav ’ format.
Statistical Modeling of the Speech Signal
"... Abstract—The Gaussian distribution is the most commonly used statistical model of the speech signal. In this paper we propose more general statistical model for the distributions of the real and imaginary parts of the speech signal DFT coefficients and their magnitudes. Based on experimental measure ..."
Abstract
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Cited by 4 (2 self)
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Abstract—The Gaussian distribution is the most commonly used statistical model of the speech signal. In this paper we propose more general statistical model for the distributions of the real and imaginary parts of the speech signal DFT coefficients and their magnitudes. Based on experimental
Results 1 - 10
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191,040
