Daily Papers

The recently proposed Conformer model has become the de facto backbone model for various downstream speech tasks based on its hybrid attention-convolution architecture that captures both local and global features. However, through a series of systematic studies, we find that the Conformer architecture's design choices are not optimal. After re-examining the design choices for both the macro and micro-architecture of Conformer, we propose Squeezeformer which consistently outperforms the state-of-the-art ASR models under the same training schemes. In particular, for the macro-architecture, Squeezeformer incorporates (i) the Temporal U-Net structure which reduces the cost of the multi-head attention modules on long sequences, and (ii) a simpler block structure of multi-head attention or convolution modules followed up by feed-forward module instead of the Macaron structure proposed in Conformer. Furthermore, for the micro-architecture, Squeezeformer (i) simplifies the activations in the convolutional block, (ii) removes redundant Layer Normalization operations, and (iii) incorporates an efficient depthwise down-sampling layer to efficiently sub-sample the input signal. Squeezeformer achieves state-of-the-art results of 7.5%, 6.5%, and 6.0% word-error-rate (WER) on LibriSpeech test-other without external language models, which are 3.1%, 1.4%, and 0.6% better than Conformer-CTC with the same number of FLOPs. Our code is open-sourced and available online.

Accurate, high-resolution vertical profiles of optical turbulence (C_n^2), which reflect local meteorology and topography, are crucial for ground-based optical astronomy and free-space optical communication. However, measuring these profiles or generating them with numerical weather models requires substantial operational or computational effort. In this work, we present OTProf, a deep-learning method that estimates high-resolution C_n^2 profiles from widely available coarse-resolution ERA5 reanalysis data. OTProf is based on the Squeezeformer architecture, modified for super-resolution by introducing grid-agnostic interpolation in the latent space. We train and evaluate OTProf in the Netherlands using high-resolution C_n^2 data obtained from the Weather Research and Forecasting (WRF) model. The commonly used Hufnagel-Valley model serves as the baseline. Overall, OTProf reproduces the vertical structure of WRF-based C_n^2 more accurately than Hufnagel-Valley and yields more accurate estimates of the Fried parameter r_0 and the scintillation index σ_I^2. Out-of-domain performance is assessed using a smaller WRF-based C_n^2 dataset covering the Canary Islands. Initial results indicate a performance drop because the orography differs markedly from the training domain. We show that output bias correction based on a few local simulations can help OTProf to adapt, but more work for domain adaptation is needed. OTProf is therefore primarily recommended for in-domain applications, e.g., generating C_n^2 climatologies from long ERA5 records.