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Preliminary Evaluation of Convolutional Neural Network Acoustic Model for Iban Language Using NVIDIA NeMo

Michael Steve Olsen, Juan Sarah Samson, Mit Edwin

Journal of Telecommunications and Information Technology

2022

nr 1

43--53

For the past few years, artificial neural networks (ANNs) have been one of the most common solutions relied upon while developing automated speech recognition (ASR) acoustic models. There are several variants of ANNs, such as deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs). A CNN model is widely used as a method for improving image processing performance. In recent years, CNNs have also been utilized in ASR techniques, and this paper investigates the preliminary result of an end-to-end CNN-based ASR using NVIDIA NeMo on the Iban corpus, an under-resourced language. Studies have shown that CNNs have also managed to produce excellent word error (WER) rates for the acoustic model on ASR for speech data. Conversely, results and studies concerned with under-resourced languages remain unsatisfactory. Hence, by using NVIDIA NeMo, a new ASR engine developed by NVIDIA, the viability and the potential of this alternative approach are evaluated in this paper. Two experiments were conducted: the number of resources used in the works of our ASR’s training was manipulated, as was the internal parameter of the engine used, namely the epochs. The results of those experiments are then analyzed and compared with the results shown in existing papers.

Identification of zones of increased vibroacoustic emission by means of the inverse method

Stryczniewicz L.

Archives of Acoustics

2004

Vol. 29, no. 4

563-576

The basic formulas concerning the inversion are presented in the paper. Examples of the author's studies on the possibility of the localization of zones of increased vibroactivity by means of the inverse method are given. The inverse method is very sensitive to the position of substitute sources. This feature can be used for the determination of the position of actual sources. The accuracy of the method depends also on the distribution of the observation points. The dependence of the position of substitute sources on their acoustic power level is discussed in the paper as well. The distribution of the acoustic fields around the air compressor is given as an example. The~measurements were performed at the points distributed on a semi-sphere. Both the amplitude and phase of the acoustic pressure were determined at each measuring point. On the basis of the results obtained, the zones of increased acoustic emission were searched for.