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Introduction to Speech Processing
  • Introduction to Speech Processing
  • 1. Preface
    • 1.4. Using this book
    • 1.5. List of authors
    • 1.6. Instructions for Developers
    • 1.7. Contributor Covenant Code of Conduct
  • 2. Introduction
    • 2.1. Why speech processing?
    • 2.2. Speech production and acoustic properties
    • Speech perception (Wikipedia)
    • 2.3. Linguistic structure of speech
    • Speech-language pathology (Wikipedia)
    • 2.4. Applications and systems structures
    • Social and cognitive processes in human communication (external)
  • 3. Basic Representations
    • 3.1. Short-time analysis of speech and audio signals
    • 3.2. Short-time processing of speech signals
    • 3.3. Waveform
    • 3.4. Windowing
    • 3.5. Signal energy, loudness and decibel
    • 3.6. Spectrogram and the STFT
    • 3.7. Autocorrelation and autocovariance
    • 3.8. The cepstrum, mel-cepstrum and mel-frequency cepstral coefficients (MFCCs)
    • 3.9. Linear prediction
    • 3.10. Fundamental frequency (F0)
    • 3.11. Zero-crossing rate
    • 3.12. Deltas and Delta-deltas
    • 3.13. Pitch-Synchoronous Overlap-Add (PSOLA)
    • 3.14. Jitter and shimmer
    • https://en.wikipedia.org/wiki/Crest_factor
  • 4. Pre-processing
    • 4.1. Pre-emphasis
    • Noise gate (Wikipedia)
    • Dynamic Range Compression (Wikipedia)
  • 5. Modelling tools in speech processing
    • 5.1. Source modelling and perceptual modelling
    • 5.2. Linear regression
    • 5.3. Sub-space models
    • 5.4. Vector quantization (VQ)
    • 5.5. Gaussian mixture model (GMM)
    • 5.6. Neural networks
    • 5.7. Non-negative Matrix and Tensor Factorization
    • 5.8. Vocoder
  • 6. Evaluation of speech processing methods
    • 6.1. Subjective quality evaluation
    • 6.2. Objective quality evaluation
    • 6.3. Other performance measures
    • 6.4. Analysis of evaluation results
  • 7. Speech analysis
    • 7.1. Fundamental frequency estimation
    • Voice and speech analysis (Wikipedia)
    • 7.2. Measurements for medical applications
      • Electroglottography (Wikipedia)
      • Videokymography (Wikipedia)
      • 7.2.1. Inverse filtering for glottal activity estimation
    • 7.3. Forensic Speaker Recognition
  • 8. Recognition tasks in speech processing
    • 8.1. Voice Activity Detection (VAD)
    • 8.2. Wake-word and keyword spotting
    • 8.3. Speech Recognition
    • 8.4. Speaker Recognition and Verification
    • 8.5. Speaker Diarization
    • 8.6. Paralinguistic speech processing
  • 9. Speech Synthesis
    • 9.1. Concatenative speech synthesis
    • 9.2. Statistical parametric speech synthesis
  • 10. Transmission, storage and telecommunication
    • 10.1. Design goals
    • 10.2. Code-excited linear prediction (CELP)
    • 10.3. Frequency-domain coding
    • 10.4. Modified discrete cosine transform (MDCT)
    • 10.5. Entropy coding
    • 10.6. Perceptual modelling in speech and audio coding
  • 11. Speech enhancement
    • 11.1. Noise attenuation
    • 11.2. Echo cancellation
    • 11.3. Bandwidth extension (BWE)
    • 11.8. Multi-channel speech enhancement and beamforming
      • 11.8.3. Time-Delay of Arrival (TDoA) and Direction of Arrival (DoA) Estimation
  • 12. Computational models of human language processing
  • 13. Security and privacy in speech technology
  • 14. Glossary
  • 15. References
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Speech analysis

7. Speech analysis#

  • Fundamental frequency estimation

  • Formant estimation and tracking

  • Measurements for medical applications

  • Inverse filtering for glottal activity estimation

  • Forensic speaker recognition

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6.4. Analysis of evaluation results

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7.1. Fundamental frequency estimation

By Tom Bäckström, Okko Räsänen, Abraham Zewoudie, Pablo Pérez Zarazaga, Liisa Koivusalo, Sneha Das, Esteban Gómez Mellado, Mariem Bouafif Mansali, Daniel Ramos

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