Pitch Determination of Speech Signals: Algorithms and Devices

Pitch (i.e., fundamental frequency FO and fundamental period TO) occupies a key position in the acoustic speech signal. The prosodic information of an utterance is predominantly determined by this parameter. The ear is more sensitive to changes of fundamental frequency than to changes of other speech signal parameters by an order of magnitude. The quality of vocoded speech is essentially influenced by the quality and faultlessness of the pitch measure­ ment. Hence the importance of this parameter necessitates using good and reliable measurement methods. At first glance the task looks simple: one just has to detect the funda­ mental frequency or period of a quasi-periodic signal. For a number of reasons, however, the task of pitch determination has to be counted among the most difficult problems in speech analysis. 1) In principle, speech is a nonstationary process; the momentary position of the vocal tract may change abruptly at any time. This leads to drastic variations in the temporal structure of the signal, even between subsequent pitch periods, and assuming a quasi-periodic signal is often far from realistic. 2) Due to the flexibility of the human vocal tract and the wide variety of voices, there exist a multitude of possible temporal structures. Narrow-band formants at low harmonics (especially at the second or third harmonic) are an additional source of difficulty. 3) For an arbitrary speech signal uttered by an unknown speaker, the fundamental frequency can vary over a range of almost four octaves (50 to 800 Hz).

Table of Contents:
1. Introduction.- 1.1 Voice Source Parameter Measurement and the Speech Signal.- 1.2 A Short Look at the Areas of Application.- 1.3 Organization of the Book.- 2. Basic Terminology. A Short Introduction to Digital Signal Processing.- 2.1 The Simplified Model of Speech Excitation.- 2.2 Digital Signal Processing 1: Signal Representation.- 2.3 Digital Signal Processing 2: Filters.- 2.4 Time-Variant Systems. The Principle of Short-Term Analysis.- 2.5 Definition of the Task. The Linear Model of Speech Production.- 2.6 A First Categorization of Pitch Determination Algorithms (PDAs).- 3. The Human Voice Source.- 3.1 Mechanism of Sound Generation at the Larynx.- 3.2 Operational Modes of the Larynx. Registers.- 3.3 The Glottal Source (Excitation) Signal.- 3.4 The Influence of the Vocal Tract Upon Voice Source Parameters.- 3.5 The Voiceless and the Transient Sources.- 4. Measuring Range, Accuracy, Pitch Perception.- 4.1 The Range of Fundamental Frequency.- 4.2 Pitch Perception. Toward a Redefinition of the Task.- 4.3 Measurement Accuracy.- 4.4 Representation of the Pitch Information in the Signal.- 4.5 Calibration and Performance Evaluation of a PDA.- 5. Manual and Instrumental Pitch Determination, Voicing Determination.- 5.1 Manual Pitch Determination.- 5.2 Pitch Determination Instruments (PDIs).- 5.3 Voicing Determination — Selected Examples.- 6. Time-Domain Pitch Determination.- 6.1 Pitch Determination by Fundamental-Harmonic Extraction.- 6.2 The Other Extreme — Temporal Structure Analysis.- 6.3 The Intermediate Device: Temporal Structure Transformation and Simplification.- 6.4 Parallel Processing in Fundamental Period Determination. Multichannel PDAs.- 6.5 Special-Purpose (High-Accuracy) Time-Domain PDAs.- 6.6 The Postprocessor.- 6.7 Final Comments.- 7. Design andImplementation of a Time-Domain PDA for Undistorted and Band-Limited Signals.- 7.1 The Linear Algorithm.- 7.2 Band-Limited Signals in Time-Domain PDAs.- 7.3 An Experimental Study Towards a Universal Time-Domain PDA Applying a Nonlinear Function and a Threshold Analysis Basic Extractor.- 7.4 Toward a Choice of Optimal Nonlinear Functions.- 7.5 Implementation of a Three-Channel PDA with Nonlinear Processing.- 8. Short-Term Analysis Pitch Determination.- 8.1 The Short-Term Transformation and Its Consequences.- 8.2 Autocorrelation Pitch Determination.- 8.3 “Anticorrelation” Pitch Determination: Average Magnitude Difference Function, Distance and Dissimilarity Measures, and Other Nonstationary Short-Term Analysis PDAs.- 8.4 Multiple Spectral Transform (“Cepstrum”) Pitch Determination.- 8.5 Frequency-Domain PDAs.- 8.6 Maximum-Likelihood (Least-Squares) Pitch Determination.- 8.7 Summary and Conclusions.- 9. General Discussion: Summary, Error Analysis, Applications.- 9.1 A Short Survey of the Principal Methods of Pitch Determination.- 9.2 Calibration, Search for Standards.- 9.3 Performance Evaluation of PDAs.- 9.4 A Closer Look at the Applications.- 9.5 Possible Paths Towards a General Solution.- Appendix A. Experimental Data on the Behavior of Nonlinear Functions in Time-Domain Pitch Determination Algorithms.- A.1 The Data Base of the Investigation.- A.2 Examples for the Behavior of the Nonlinear Functions.- A.3 Relative Amplitude RA1 and Enhancement RE1 of the First Harmonic.- A.4 Relative Amplitude RASM of Spurious Maximum and Autocorrelation Threshold.- A.5 Processing Sequence, Preemphasis, Phase, Band Limitation.- A.6 Optimal Performance of Nonlinear Functions.- A.7 Performance of the Comb Filters.- Appendix B. Original Text of the Quotations in Foreign LanguagesThroughout This Book.- List of Abbreviations.- Author and Subject Index.