A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition

We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition. Measuring perplexity and frame-level classification accuracy, kernel-based acoustic models are as effective as their DNN counterparts. However, on token-error-rates DNN models can be significantly better. We have discovered that this might be attributed to DNN's unique strength in reducing both the perplexity and the entropy of the predicted posterior probabilities. Motivated by our findings, we propose a new technique, entropy regularized perplexity, for model selection. This technique can noticeably improve the recognition performance of both types of models, and reduces the gap between them. While effective on Broadcast News, this technique could be also applicable to other tasks.Comment: arXiv admin note: text overlap with arXiv:1411.400

Tailoring the Spectra of White Organic Light-Emitting Devices by Trap Effect of a Concentration-Insensitive Dopant

Highly efficient phosphorescent organic light-emitting devices (PhOLEDs) had been fabricated by using a novel iridium complex, bis[2-(3′,5′-di-tert-butylbiphenyl-4-yl)benzothiazolato-N,C2′]iridium(III) (acetylacetonate) [(tbpbt)2Ir(acac)], as the emitter. With a wide doping ratio ranging from 15 wt% to 25 wt%, the PhOLEDs maintained a comparable high performance, indicating concentration-insensitive property of the (tbpbt)2Ir(acac). On the basis of the unique characteristic of concentration insensitivity, the application of this phosphor was explored by fabricating white organic light-emitting devices (WOLEDs) with altered doping ratio, indicating that trap effect of (tbpbt)2Ir(acac) could effectively tailor WOLEDs spectra. Typically, a high-power efficiency, current efficiency, and external quantum efficiency of 30.0 lm/W, 38.8 cd/A, 18.1%, were achieved by 20 wt% doped WOLEDs

Language-specific Acoustic Boundary Learning for Mandarin-English Code-switching Speech Recognition

Code-switching speech recognition (CSSR) transcribes speech that switches between multiple languages or dialects within a single sentence. The main challenge in this task is that different languages often have similar pronunciations, making it difficult for models to distinguish between them. In this paper, we propose a method for solving the CSSR task from the perspective of language-specific acoustic boundary learning. We introduce language-specific weight estimators (LSWE) to model acoustic boundary learning in different languages separately. Additionally, a non-autoregressive (NAR) decoder and a language change detection (LCD) module are employed to assist in training. Evaluated on the SEAME corpus, our method achieves a state-of-the-art mixed error rate (MER) of 16.29% and 22.81% on the test_man and test_sge sets. We also demonstrate the effectiveness of our method on a 9000-hour in-house meeting code-switching dataset, where our method achieves a relatively 7.9% MER reduction

An In Vitro Comparison of the Digestibility and Gastrointestinal Fate of Scallops and Plant-Based Scallop Analogs

Concerns exist regarding the negative environmental impact and health risks associated with ocean fishing and aquaculture, such as stock depletion, pollution, biodiversity loss, and toxin presence. To address these concerns, plant-based seafood analogs are being developed. Our previous study successfully created plant-based scallop analogs using pea proteins and citrus pectin, resembling real scallops in appearance and texture. This study focuses on comparing the digestive fate of these analogs to real scallops, as it can impact their nutritional properties. Using an in vitro digestion model (INFOGEST), we simulated oral, gastric, and small intestinal conditions. The analysis revealed differences in the microstructure, physicochemical properties, and protein digestibility between the plant-based scallops and real scallops. The particle size and charge followed the following similar trends for both types of scallops: the particle size decreased from the mouth to the stomach to the small intestine; the particles were negative in the mouth, positive in the stomach, and negative in the small intestine. The protein digestibility of the plant-based scallops was considerably lower than that of real scallops. For instance, around 18.8% and 61.4% of protein was digested in the stomach and small intestine phases for the real scallop (80.2% total digestion), whereas around 8.7% and 47.7% of the protein was digested for the plant-based scallop (56.4% total digestion). The lower digestibility of the plant-based scallops may have been due to differences in the protein structure, the presence of dietary fibers (pectin), or antinutritional factors in the plant proteins. These findings are crucial for developing more sustainable next-generation plant-based seafood analogs