GCI DETECTION FROM RAW SPEECH USING A FULLY-CONVOLUTIONAL NETWORK

Glottal Closure Instants (GCI) detection consists in automatically detecting temporal locations of most significant excitation of the vocal tract from the speech signal. It is used in many speech analysis and processing applications, and various algorithms have been proposed for this purpose. Recently, new approaches using convo-lutional neural networks have emerged , with encouraging results. Following this trend, we propose a simple approach that performs a regression from the speech waveform to a target signal from which the GCI are easily obtained by peak-picking. However, the ground truth GCI used for training and evaluation are usually extracted from EGG signals, which are not reliable and often not available. To overcome this problem, we propose to train our network on high-quality synthetic speech with perfect ground truth. The performances of the proposed algorithm are compared with three other state-of-the-art approaches using publicly available datasets, and the impact of using controlled synthetic or real speech signals in the training stage is investigated. The experimental results demonstrate that the proposed method obtains similar or better results than other state-of-the-art algorithms and that using large synthetic datasets with many speaker offers better generalization ability than using a smaller database of real speech and EGG signals

Speaker Recognition using Supra-segmental Level Excitation Information

Speaker specific information present in the excitation signal is mostly viewed from sub-segmental, segmental and supra-segmental levels. In this work, the supra-segmental level information is explored for recognizing speakers. Earlier study has shown that, combined use of pitch and epoch strength vectors provides useful supra-segmental information. However, the speaker recognition accuracy achieved by supra-segmental level feature is relatively poor than other levels source information. May be the modulation information present at the supra-segmental level of the excitation signal is not manifested properly in pith and epoch strength vectors. We propose a method to model the supra-segmental level modulation information from residual mel frequency cepstral coefficient (R-MFCC) trajectories. The evidences from R-MFCC trajectories combined with pitch and epoch strength vectors are proposed to represent supra-segmental information. Experimental results show that compared to pitch and epoch strength vectors, the proposed approach provides relatively improved performance. Further, the proposed supra-segmental level information is relatively more complimentary to other levels information

Event-based instantaneous fundamental frequency estimation from speech signals

Exploiting the impulse-like nature of excitation in the sequence of glottal cycles, a method is proposed to derive the instantaneous fundamental frequency from speech signals. The method involves passing the speech signal through two ideal resonators located at zero frequency. A filtered signal is derived from the output of the resonators by subtracting the local mean computed over an interval corresponding to the average pitch period. The positive zero crossings in the filtered signal correspond to the locations of the strong impulses in each glottal cycle. Then the instantaneous fundamental frequency is obtained by taking the reciprocal of the interval between successive positive zero crossings. Due to filtering by zero-frequency resonator, the effects of noise and vocal-tract variations are practically eliminated. For the same reason, the method is also robust to degradation in speech due to additive noise. The accuracy of the fundamental frequency estimation by the proposed method is comparable or even better than many existing methods. Moreover, the proposed method is also robust against rapid variation of the pitch period or vocal-tract changes. The method works well even when the glottal cycles are not periodic or when the speech signals are not correlated in successive glottal cycles