SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech

Progress in speech processing has been facilitated by shared datasets and benchmarks. Historically these have focused on automatic speech recognition (ASR), speaker identification, or other lower-level tasks. Interest has been growing in higher-level spoken language understanding tasks, including using end-to-end models, but there are fewer annotated datasets for such tasks. At the same time, recent work shows the possibility of pre-training generic representations and then fine-tuning for several tasks using relatively little labeled data. We propose to create a suite of benchmark tasks for Spoken Language Understanding Evaluation (SLUE) consisting of limited-size labeled training sets and corresponding evaluation sets. This resource would allow the research community to track progress, evaluate pre-trained representations for higher-level tasks, and study open questions such as the utility of pipeline versus end-to-end approaches. We present the first phase of the SLUE benchmark suite, consisting of named entity recognition, sentiment analysis, and ASR on the corresponding datasets. We focus on naturally produced (not read or synthesized) speech, and freely available datasets. We provide new transcriptions and annotations on subsets of the VoxCeleb and VoxPopuli datasets, evaluation metrics and results for baseline models, and an open-source toolkit to reproduce the baselines and evaluate new models.Comment: Updated preprint (Sentiment annotation on test set was updated). Toolkit link https://github.com/asappresearch/slue-toolki

Speaker Diarization with Lexical Information

This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition. We propose a speaker diarization system that can incorporate word-level speaker turn probabilities with speaker embeddings into a speaker clustering process to improve the overall diarization accuracy. To integrate lexical and acoustic information in a comprehensive way during clustering, we introduce an adjacency matrix integration for spectral clustering. Since words and word boundary information for word-level speaker turn probability estimation are provided by a speech recognition system, our proposed method works without any human intervention for manual transcriptions. We show that the proposed method improves diarization performance on various evaluation datasets compared to the baseline diarization system using acoustic information only in speaker embeddings

Limits of Binaries That Can Be Characterized by Gravitational Microlensing

Due to the high efficiency of planet detections, current microlensing planet searches focus on high-magnification events. High-magnification events are sensitive to remote binary companions as well and thus a sample of wide-separation binaries are expected to be collected as a byproduct. In this paper, we show that characterizing binaries for a portion of this sample will be difficult due to the degeneracy of the binary-lensing parameters. This degeneracy arises because the perturbation induced by the binary companion is well approximated by the Chang-Refsdal lensing for binaries with separations greater than a certain limit. For binaries composed of equal mass lenses, we find that the lens binarity can be noticed up to the separations of $\sim 60$ times of the Einstein radius corresponding to the mass of each lens. Among these binaries, however, we find that the lensing parameters can be determined only for a portion of binaries with separations less than $\sim 20$ times of the Einstein radius.Comment: 5 pages, 3 figures, 1 tabl

Characterization and Failure Analysis of Solid-State Diffusion Bonded Ceramic-to-Metal Transitions

Reliable, high-temperature, high-pressure transitions between ceramic heat exchangers and metal components enable higher efficiency in advanced power generation systems. Recent development of a novel cermet-filled seal ring design has shown potential to maintain a gas-tight seal through multiple thermal cycles up to 800­ ºC. Materials characterization and computational modeling provided insight to chemical behavior (i.e., solid-state diffusion) and mechanical integrity (i.e., stress) in the seal components. Results demonstrate a correlation between machining tolerance, assembly process, and diffusion behavior on the seal’s performance in ceramic-to-metal systems and helped guide the design efficacy of future seals