A new technique for the analysis of speech, the perceptual linear predictive (PLP) technique, is presented and examined. This technique uses three concepts from the psychophysics of hearing to derive an estimate of the auditory spectrum: (1) the critical-band spectral resolution, (2) the equal-loudness curve, and (3) the intensity-loudness power law. The auditory spectrum is then approximated by an autoregressive all-pole model. A 5th-order all-pole model is effective in suppressing speaker-dependent details of the auditory spectrum. In comparison with conventional linear predictive (LP) analysis, PLP analysis is more consistent with human hearing. The effective second formant F2' and the 3.5-Bark spectral-peak integration theories of vowel perception are well accounted for. PLP analysis is computationally efficient and yields a low-dimensional representation of speech. These properties are found to be useful in speaker-independent automatic-speech recognition.

Perceptual linear predictive (PLP) analysis of speech

(NOTE: Each chapter begins with an introduction and concludes with a Summary, Exercises and Bibliography.) 1. Introduction. Discrete-Time Speech Signal Processing. The Speech Communication Pathway. Analysis/Synthesis Based on Speech Production and Perception. Applications. Outline of Book. 2. A Discrete-Time Signal Processing Framework. Discrete-Time Signals. Discrete-Time Systems. Discrete-Time Fourier Transform. Uncertainty Principle. z-Transform. LTI Systems in the Frequency Domain. Properties of LTI Systems. Time-Varying Systems. Discrete-Fourier Transform. Conversion of Continuous Signals and Systems to Discrete Time. 3. Production and Classification of Speech Sounds. Anatomy and Physiology of Speech Production. Spectrographic Analysis of Speech. Categorization of Speech Sounds. Prosody: The Melody of Speech. Speech Perception. 4. Acoustics of Speech Production. Physics of Sound. Uniform Tube Model. A Discrete-Time Model Based on Tube Concatenation. Vocal Fold/Vocal Tract Interaction. 5. Analysis and Synthesis of Pole-Zero Speech Models. Time-Dependent Processing. All-Pole Modeling of Deterministic Signals. Linear Prediction Analysis of Stochastic Speech Sounds. Criterion of "Goodness". Synthesis Based on All-Pole Modeling. Pole-Zero Estimation. Decomposition of the Glottal Flow Derivative. Appendix 5.A: Properties of Stochastic Processes. Random Processes. Ensemble Averages. Stationary Random Process. Time Averages. Power Density Spectrum. Appendix 5.B: Derivation of the Lattice Filter in Linear Prediction Analysis. 6. Homomorphic Signal Processing. Concept. Homomorphic Systems for Convolution. Complex Cepstrum of Speech-Like Sequences. Spectral Root Homomorphic Filtering. Short-Time Homomorphic Analysis of Periodic Sequences. Short-Time Speech Analysis. Analysis/Synthesis Structures. Contrasting Linear Prediction and Homomorphic Filtering. 7. Short-Time Fourier Transform Analysis and Synthesis. Short-Time Analysis. Short-Time Synthesis. Short-Time Fourier Transform Magnitude. Signal Estimation from the Modified STFT or STFTM. Time-Scale Modification and Enhancement of Speech. Appendix 7.A: FBS Method with Multiplicative Modification. 8. Filter-Bank Analysis/Synthesis. Revisiting the FBS Method. Phase Vocoder. Phase Coherence in the Phase Vocoder. Constant-Q Analysis/Synthesis. Auditory Modeling. 9. Sinusoidal Analysis/Synthesis. Sinusoidal Speech Model. Estimation of Sinewave Parameters. Synthesis. Source/Filter Phase Model. Additive Deterministic-Stochastic Model. Appendix 9.A: Derivation of the Sinewave Model. Appendix 9.B: Derivation of Optimal Cubic Phase Parameters. 10. Frequency-Domain Pitch Estimation. A Correlation-Based Pitch Estimator. Pitch Estimation Based on a "Comb Filter<170. Pitch Estimation Based on a Harmonic Sinewave Model. Glottal Pulse Onset Estimation. Multi-Band Pitch and Voicing Estimation. 11. Nonlinear Measurement and Modeling Techniques. The STFT and Wavelet Transform Revisited. Bilinear Time-Frequency Distributions. Aeroacoustic Flow in the Vocal Tract. Instantaneous Teager Energy Operator. 12. Speech Coding. Statistical Models of Speech. Scaler Quantization. Vector Quantization (VQ). Frequency-Domain Coding. Model-Based Coding. LPC Residual Coding. 13. Speech Enhancement. Introduction. Preliminaries. Wiener Filtering. Model-Based Processing. Enhancement Based on Auditory Masking. Appendix 13.A: Stochastic-Theoretic parameter Estimation. 14. Speaker Recognition. Introduction. Spectral Features for Speaker Recognition. Speaker Recognition Algorithms. Non-Spectral Features in Speaker Recognition. Signal Enhancement for the Mismatched Condition. Speaker Recognition from Coded Speech. Appendix 14.A: Expectation-Maximization (EM) Estimation. Glossary.Speech Signal Processing.Units.Databases.Index.About the Author.

Discrete-Time Speech Signal Processing: Principles and Practice

Sensory segmentation is an outstanding unsolved problem of theoretical, practical and technical importance. The basic idea of a solution is described in the form of a model. The response of “neurons” within the sensory field is temporally unstable. Segmentation is expressed by synchronization within segments and desynchronization between segments. Correlations are generated by an autonomous pattern formation process. Neuronal coupling is the result both of peripheral evidence (similarity of local quality) and of central evidence (common membership in a stored pattern). The model is consistent with known anatomy and physiology. However, a new physiological function, synaptic modulation, has to be postulated. The present paper restricts explicit treatment to the peripheral evidence represented by amplitude modulations globally present in all components of a sound spectrum. Generalization to arbitrary sensory qualities will be the subject of a later paper. The model is an application and illustration of the Correlation Theory of brain function.

A neural cocktail-party processor

During the last decade, CD-quality digital audio has essentially replaced analog audio. Emerging digital audio applications for network, wireless, and multimedia computing systems face a series of constraints such as reduced channel bandwidth, limited storage capacity, and low cost. These new applications have created a demand for high-quality digital audio delivery at low bit rates. In response to this need, considerable research has been devoted to the development of algorithms for perceptually transparent coding of high-fidelity (CD-quality) digital audio. As a result, many algorithms have been proposed, and several have now become international and/or commercial product standards. This paper reviews algorithms for perceptually transparent coding of CD-quality digital audio, including both research and standardization activities. This paper is organized as follows. First, psychoacoustic principles are described, with the MPEG psychoacoustic signal analysis model 1 discussed in some detail. Next, filter bank design issues and algorithms are addressed, with a particular emphasis placed on the modified discrete cosine transform, a perfect reconstruction cosine-modulated filter bank that has become of central importance in perceptual audio coding. Then, we review methodologies that achieve perceptually transparent coding of FM- and CD-quality audio signals, including algorithms that manipulate transform components, subband signal decompositions, sinusoidal signal components, and linear prediction parameters, as well as hybrid algorithms that make use of more than one signal model. These discussions concentrate on architectures and applications of those techniques that utilize psychoacoustic models to exploit efficiently masking characteristics of the human receiver. Several algorithms that have become international and/or commercial standards receive in-depth treatment, including the ISO/IEC MPEG family (-1, -2, -4), the Lucent Technologies PAC/EPAC/MPAC, the Dolby AC-2/AC-3, and the Sony ATRAC/SDDS algorithms. Then, we describe subjective evaluation methodologies in some detail, including the ITU-R BS.1116 recommendation on subjective measurements of small impairments. This paper concludes with a discussion of future research directions.

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Perceptual coding of digital audio

Otoacoustic emissions (OAEs) of all types are widely assumed to arise by a common mechanism: nonlinear electromechanical distortion within the cochlea. In this view, both stimulus-frequency (SFOAEs) and distortion-product emissions (DPOAEs) arise because nonlinearities in the mechanics act as "sources" of backward-traveling waves. This unified picture is tested by analyzing measurements of emission phase using a simple phenomenological description of the nonlinear re-emission process. The analysis framework is independent of the detailed form of the emission sources and the nonlinearities that produce them. The analysis demonstrates that the common assumption that SFOAEs originate by nonlinear distortion requires that SFOAE phase be essentially independent of frequency, in striking contradiction with experiment. This contradiction implies that evoked otoacoustic emissions arise by two fundamentally different mechanisms within the cochlea. These two mechanisms (linear reflection versus nonlinear distortion) are described and two broad classes of emissions--reflection-source and distortion-source emissions--are distinguished based on the mechanisms of their generation. The implications of this OAE taxonomy for the measurement, interpretation, and clinical use of otoacoustic emissions as noninvasive probes of cochlear function are discussed.

http://web.mit.edu/apg/papers/shera-guinan-taxonomy-JASA99.pdf

Evoked otoacoustic emissions arise by two fundamentally different mechanisms: A taxonomy for mammalian OAEs

Linear prediction is considered with respect to a nonlinear frequency scale obtained by a first‐order all‐pass transformation. The predictor can be computed from a frequency‐warped autocorrelation function obtained from the power spectrum or by a direct linear transformation of the original acf. Three numerical procedures are compared. Alternatively, the predictor can be determined from a covariance matrix or (adaptively) from continuously formed correlations, suitably defined according to the all‐pass transformation. Prediction‐error minimization and spectral flattening are no longer equivalent criteria. In the synthesis part of a vocoder or APC system, no inverse transformation is required, since the direct form of the analysis and synthesis filters can be modified so as to immediately realize the warped transfer function. Single‐word intelligibility is compared for a predictive vocoder on a ’’Bark’’ scale and a linear frequency scale. The Bark scale yields results around 90% even at predictor orders of 5 to 7. More possible applications have been given previously by other authors.

Linear prediction on a warped frequency scale

Summary In this article a new acoustic parameter for the objective description of voice quality is introduced. It is based on the correlation coefficientfor Hilbert envelopes of different frequency bands. The parameter indicates whether a given voice signal originates from vibrations of the vocal folds or from turbulent noise generated in the vocal tract and is thus related to (but not a direct measure of) breathiness. Therefore it is named Glottal-to-Noise Excitation Ratio (GNE Ratio). GNE is compared to HNR (Harmonics-to-Noise Ratio) and NNE (Normalized Noise Energy), existing measures also sensitive to additive noise (turbulence). Experiments with artificialsignals show that only the GNE is almost independent of frequency modulation noise (jitter) and amplitude modulation noise (shimmer).

Glottal-to-Noise Excitation Ratio - a New Measure for Describing Pathological Voices

The glottal to noise excitation ratio (GNE) is an acoustic measure designed to assess the amount of noise in a pulse train generated by the oscillation of the vocal folds. So far its properties have only been studied for synthesized signals, where it was found to be independent of variations of fundamental frequency (jitter) and amplitude (shimmer). On the other hand, other features designed for the same purpose like NNE (normalized noise energy) or CHNR (cepstrum based harmonics-to-noise ratio) did not show this independence. This advantage of the GNE over NNE and CHNR, as well as its general applicability in voice quality assessment, is now tested for real speech using a large group of pathologic voices (n=447). A set of four acoustic features is extracted from a total of 22 mostly well-known acoustic voice quality measures by correlation analysis, mutual information analysis, and principal components analysis. Three of these measures are chosen to assess primarily different aspects of signal aperiodici...

Selection and combination of acoustic features for the description of pathologic voices

For vowels excited by vigorous glottal vibrations, the instant of glottal closure is tentatively identified with the moment of strongest excitation (at not too low frequencies) and worst linear predictability. Some predictor methods for its determination are reviewed, which do not always yield reliable and unequivocal results. Then Sobakin's method using the determinant of the autocovariance matrix is examined critically and reinterpreted such that the determinant is maximum if the beginning of the interval on which the autocovariance matrix is calculated coincides with the glottal closure. This hypothesis is tested by comparison with the predictor methods and by looking at the inversely filtered waveforms and the formants obtained from predictors determined on a shifted interval. The determinant method seems to be very reliable even for otherwise difficult cases, such as the vowel /u/.

Determination of the instant of glottal closure from the speech wave

The hoarseness diagram (Michaelis, Frohlich, & Strube, 1998a) has been proposed as a new approach to describe different acoustic properties of voices. To test its performance in the analysis of pat...

Hans Werner Strube

Papers

Linear prediction on a warped frequency scale

Glottal-to-Noise Excitation Ratio - a New Measure for Describing Pathological Voices

Selection and combination of acoustic features for the description of pathologic voices

Determination of the instant of glottal closure from the speech wave

Acoustic voice analysis by means of the hoarseness diagram.