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This study investigates emotion detection from noise-corrupted telephone speech. A generic modulation filtering approach for audio pattern recognition is proposed that utilizes inherent long-term properties of acoustic features in different classes. When applied to binary classification along the activation and valence dimensions, filtering the baseline short-time timbral features in both the training...
This study focuses on acoustic variations in speech introduced by whispering, and proposes several strategies to improve robustness of automatic speech recognition of whispered speech with neutral-trained acoustic models. In the analysis part, differences in neutral and whispered speech captured in the UT-Vocal Effort II corpus are studied in terms of energy, spectral slope, and formant center frequency...
Many current speech models used in recognition involve thousands of parameters, whereas the mechanisms of speech production are conceptually very simple. We present and evaluate a new continuous state probabilistic model (CS-HMM) for recovering dwell-transition and phoneme sequences from dynamic speech production features. We show that with very few parameters, these features can be tracked, and phoneme...
Speech patterns are modulated by the emotional and neurophysiological state of the speaker. There exists a growing body of work that computationally examines this modulation in patients suffering from depression, autism, and post-traumatic stress disorder. However, the majority of the work in this area focuses on the analysis of structured speech collected in controlled environments. Here we expand...
Recent advances in speech analysis have shown that voiced speech can be very well represented using quasi-harmonic frequency tracks and local parameter adaptivity to the underlying signal. In this paper, we revisit the quasi-harmonicity approach through the extended adaptive Quasi-Harmonic Model — eaQHM, and we show that the application of a continuous f0 estimation method plus an adaptivity scheme...
Time-varying linear prediction has been studied in the context of speech signals, in which the auto-regressive (AR) coefficients of the system function are modeled as a linear combination of a set of known bases. Traditionally, least squares minimization is used for the estimation of model parameters of the system. Motivated by the sparse nature of the excitation signal for voiced sounds, we explore...
The paper presents a solution for singing voice processing that is used in a karaoke application with automated voice correction1. The intended purpose of the application is to automatically improve user's performance towards performance of a professional singer by implementation of voice effects such as pitch correction, artificial polyphony, time stretching and other. The proposed framework incorporates...
In this paper, a simple method for pitch-scale modifications of speech based on a recently suggested model for AM-FM decomposition of speech signals, is presented. This model is referred to as the adaptive Harmonic Model (aHM). The aHM models speech as a sum of harmonically related sinusoids that can adapt to the local characteristics of the signal. It was shown that this model provides high quality...
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