An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds. It is based on the well-known autocorrelation method with a number of modifications that combine to prevent errors. The algorithm has several desirable features. Error rates are about three times lower than the best competing methods, as evaluated over a database of speech recorded together with a laryngograph signal. There is no upper limit on the frequency search range, so the algorithm is suited for high-pitched voices and music. The algorithm is relatively simple and may be implemented efficiently and with low latency, and it involves few parameters that must be tuned. It is based on a signal model (periodic signal) that may be extended in several ways to handle various forms of aperiodicity that occur in particular applications. Finally, interesting parallels may be drawn with models of auditory processing.

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YIN, a fundamental frequency estimator for speech and music

In the development process of noise-reduction algorithms, an objective machine-driven intelligibility measure which shows high correlation with speech intelligibility is of great interest. Besides reducing time and costs compared to real listening experiments, an objective intelligibility measure could also help provide answers on how to improve the intelligibility of noisy unprocessed speech. In this paper, a short-time objective intelligibility measure (STOI) is presented, which shows high correlation with the intelligibility of noisy and time-frequency weighted noisy speech (e.g., resulting from noise reduction) of three different listening experiments. In general, STOI showed better correlation with speech intelligibility compared to five other reference objective intelligibility models. In contrast to other conventional intelligibility models which tend to rely on global statistics across entire sentences, STOI is based on shorter time segments (386 ms). Experiments indeed show that it is beneficial to take segment lengths of this order into account. In addition, a free Matlab implementation is provided.

An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech

福祉関連職におけるMaslach Burnout Inventoryの因子構造の比較

(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

Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex.

The goal of the study was to investigate whether the temporal resolution of the auditory system is influenced by the variability of the stimulus envelope. To do so, the ability to detect an increment in the duration of a temporal gap (the test gap) was measured with an adaptive 3‐IFC procedure. The stimulus consisted of a series of 10‐ms broadband noise pulses. The pulses were separated by a 10‐ms silent period, or temporal gap. In the main experiments, the test gap was either the first or the last gap in a series of 21 pulses. The variability in the stimulus’ envelope was controlled directly by applying a jitter to the onset of the individual pulses in the pulse trains. Additionally, the stimuli were presented with different fine structure variabilities which also induced differences in the variability of the envelope. The gap‐discrimination thresholds for the jittered noise pulse trains showed strong dependence on the amount of jitter as long as the jitter was applied randomly leading to a different pattern for every stimulus. When the jitter was applied as a frozen jitter resulting in a constant pattern of pulses, the thresholds did not increase significantly. A similar result was obtained for the different fine structure variabilities. A frozen fine structure led to thresholds about 1 ms lower than those obtained with random noise stimuli. A measure for the envelope variability was provided by calculating the variances of the envelope spectrum of the gammatone‐filtered stimuli. The results of the calculations show a qualitative correspondence to the experimental results.

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Influence of fine structure and envelope variability on gap‐duration discrimination thresholds

Description of complex sounds

An explanation for the apparantly wider critical bandwidth in binaural experiments

Whereas the human auditory system has remarkable capabilities to focus on a particular target source in complex multi-source scenarios, it has remained a challenging task to develop algorithms that are able to retrieve information about sound sources in a complex acoustic scene (e.g. to localize and identify active speech sources). A robust binaural scene recognizer will be presented that is able to simultaneously localize and classify a predefined number of target speech sources in the presence of reverberation and interfering noise. The model consists of three stages: localization stage, detection of speech sources, and recognition of speaker identities. First, a binaural front-end is used to localize relevant sound source activity. Based on this localization information, a binary mask is determined which identifies the activity of individual sound sources on a time-frequency (T-F) basis. The localization is based on the supervised learning of azimuth-dependent binaural features, namely interaural time and level differences (ITDs and ILDs). Secondly, a speech detection module determines whether the corresponding source type is speech or noise for all sound sources that have been found. For this purpose the estimated binary mask and the corresponding spectral features are passed to a missing data classifier for each sound source candidate. Finally, the speaker identity of all detected speech sources is recognized. The proposed system is analyzed in simulated, adverse conditions including interfering noise, reverberation and the presence of multiple target sources. Compared to a state-of-the art MFCC recognizer, the proposed model achieves significant speaker recognition accuracy improvements.

Simultaneous localization and identification of speakers in noisy and reverberant environments

In recent years, auxiliary sensors have been employed to improve the robustness of emerging hands-free speech communication systems based on air-conduction microphones, especially in low signal-to-noise-ratio environments. One such sensor, based on ultrasound, captures articulatory movement information during speech production and has been used in a voice activity detector and also shown to improve the performance of speech recognizers. However, studies thus far have tested such sensors in ideal scenarios where only relevant ar-ticulatory information was assumed to be present. Therefore, in this paper the robustness of such sensors in realistic scenarios is investigated. Challenges arising from non-articulatory movements and other environmental influences captured by ultrasound sensors are discussed and strategies for their detection presented. Finally, the proposed strategies are evaluated in an ultrasound-based voice activity detector.

AG Armin Kohlrausch

Papers

Influence of fine structure and envelope variability on gap‐duration discrimination thresholds

Description of complex sounds

An explanation for the apparantly wider critical bandwidth in binaural experiments

Simultaneous localization and identification of speakers in noisy and reverberant environments

Robustness improvement of ultrasound-based sensor systems for speech communication