Context-aware Fine-tuning of Self-supervised Speech Models

Self-supervised pre-trained transformers have improved the state of the art on a variety of speech tasks. Due to the quadratic time and space complexity of self-attention, they usually operate at the level of relatively short (e.g., utterance) segments. In this paper, we study the use of context, i.e., surrounding segments, during fine-tuning and propose a new approach called context-aware fine-tuning. We attach a context module on top of the last layer of a pre-trained model to encode the whole segment into a context embedding vector which is then used as an additional feature for the final prediction. During the fine-tuning stage, we introduce an auxiliary loss that encourages this context embedding vector to be similar to context vectors of surrounding segments. This allows the model to make predictions without access to these surrounding segments at inference time and requires only a tiny overhead compared to standard fine-tuned models. We evaluate the proposed approach using the SLUE and Libri-light benchmarks for several downstream tasks: Automatic speech recognition (ASR), named entity recognition (NER), and sentiment analysis (SA). The results show that context-aware fine-tuning not only outperforms a standard fine-tuning baseline but also rivals a strong context injection baseline that uses neighboring speech segments during inference

Coexistence of WiFi and WiMAX Systems Based on PS-Request Protocols†

We introduce both the coexistence zone within the WiMAX frame structure and a PS-Request protocol for the coexistence of WiFi and WiMAX systems sharing a frequency band. Because we know that the PS-Request protocol has drawbacks, we propose a revised PS-Request protocol to improve the performance. Two PS-Request protocols are based on the time division operation (TDO) of WiFi system and WiMAX system to avoid the mutual interference, and use the vestigial power management (PwrMgt) bit within the Frame Control field of the frames transmitted by a WiFi AP. The performance of the revised PS-Request protocol is evaluated by computer simulation, and compared to those of the cases without a coexistence protocol and to the original PS-Request protocol

DNN Transfer Learning based Non-linear Feature Extraction for Acoustic Event Classification

Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments

Restructuring field layouts for embedded memory systems

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