The standard approach to assess reliability of automatic speech transcriptions is through the use of confidence scores. If accurate, these scores provide a flexible mechanism to flag transcription errors for upstream and downstream applications. One challenging type of errors that recognisers make are deletions. These errors are not accounted for by the standard confidence estimation schemes and are hard to rectify in the upstream and downstream processing. High deletion rates are prominent in limited resource and highly mismatched training/testing conditions studied under IARPA Babel and Material programs. This paper looks at the use of bidirectional recurrent neural networks to yield confidence estimates in predicted as well as deleted words. Several simple schemes are examined for combination. To assess usefulness of this approach, the combined confidence score is examined for untranscribed data selection that favours transcriptions with lower deletion errors. Experiments are conducted using IARPA Babel/Material program languages.Index Termsconfidence score, deletion error, bidirectional recurrent neural network

Humans infer rich knowledge of objects from both auditory and visual cues. Building a machine of such competency, however, is very challenging, due to the great difficulty in capturing large-scale, clean data of objects with both their appearance and the sound they make. In this paper, we present a novel, open-source pipeline that generates audiovisual data, purely from 3D object shapes and their physical properties. Through comparison with audio recordings and human behavioral studies, we validate the accuracy of the sounds it generates. Using this generative model, we are able to construct a synthetic audio-visual dataset, namely Sound-20K, for object perception tasks. We demonstrate that auditory and visual information play complementary roles in object perception, and further, that the representation learned on synthetic audio-visual data can transfer to real-world scenarios.

Exogenous double-stranded RNA (dsRNA) can trigger gene silencing through the RNA interference (RNAi) pathway. Our previous research established that Bactrocera dorsalis can block RNAi after an initial priming of exposure to dsRNA. However, the mechanism underlying this phenomenon is not yet fully understood. Here, we demonstrate that fatty acid biosynthesis and metabolism pathways play important roles in the blockage of RNAi induced by dsRNA priming. The ratio of linoleic acid (LA) to arachidonic acid (AA) was significantly increased in the hemolymph of B. dorsalis following dsRNA priming, and further, the endocytosis of dsRNA into the midgut cells of B. dorsalis was inhibited in these samples. The expression levels of most genes involved in the fatty acid biosynthesis and metabolism pathways were altered following priming with dsRNA. Furthermore, altering the composition of fatty acids via the injection of AA can facilitate the uptake of ingested dsRNA into the midgut cells of Drosophila melanogaster and successfully induce an RNAi effect, which cannot be achieved via feeding in fruit flies. Our results suggest that polyunsaturated fatty acids are involved in the regulation of the dsRNA-endocytic ability in B. dorsalis.

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