DC-Informative Joint Color-Frequency Modulation for Visible Light Communications

In this paper, we consider the problem of constellation design for a visible light communication (VLC) system using red/green/blue light-emitting diodes (RGB LED), and propose a method termed DC-informative joint color-frequency modulation (DCI-JCFM). This method jointly utilizes available diversity resources including different optical wavelengths, multiple baseband subcarriers, and adaptive DC-bias. Constellation is designed in a high dimensional space, where the compact sphere packing advantage over lower dimensional counterparts is utilized. Taking into account multiple practical illumination constraints, a non-convex optimization problem is formulated, seeking the least error rate with a fixed spectral efficiency. The proposed scheme is compared with a decoupled scheme, where constellation is designed separately for each LED. Notable gains for DCI-JCFM are observed through simulations where balanced, unbalanced and very unbalanced color illuminations are considered.Comment: submitted to Journal of Lightwave Technology, Aug. 5th 201

Weak Alignment Supervision from Hybrid Model Improves End-to-end ASR

In this paper, we aim to create weak alignment supervision from an existing hybrid system to aid the end-to-end modeling of automatic speech recognition. Towards this end, we use the existing hybrid ASR system to produce triphone alignments of the training audios. We then create a cross-entropy loss at a certain layer of the encoder using the derived alignments. In contrast to the general one-hot cross-entropy losses, here we use a cross-entropy loss with a label smoothing parameter to regularize the supervision. As a comparison, we also conduct the experiments with one-hot cross-entropy losses and CTC losses with loss weighting. The results show that placing the weak alignment supervision with the label smoothing parameter of 0.5 at the third encoder layer outperforms the other two approaches and leads to about 5\% relative WER reduction on the TED-LIUM 2 dataset over the baseline. We see similar improvements when applying the method out-of-the-box on a Tagalog end-to-end ASR system.Comment: 7 pages, 7 figures, and 5 table

Improving Language Model Integration for Neural Machine Translation

The integration of language models for neural machine translation has been extensively studied in the past. It has been shown that an external language model, trained on additional target-side monolingual data, can help improve translation quality. However, there has always been the assumption that the translation model also learns an implicit target-side language model during training, which interferes with the external language model at decoding time. Recently, some works on automatic speech recognition have demonstrated that, if the implicit language model is neutralized in decoding, further improvements can be gained when integrating an external language model. In this work, we transfer this concept to the task of machine translation and compare with the most prominent way of including additional monolingual data - namely back-translation. We find that accounting for the implicit language model significantly boosts the performance of language model fusion, although this approach is still outperformed by back-translation.Comment: accepted at ACL2023 (Findings

Self-Normalized Importance Sampling for Neural Language Modeling

To mitigate the problem of having to traverse over the full vocabulary in the softmax normalization of a neural language model, sampling-based training criteria are proposed and investigated in the context of large vocabulary word-based neural language models. These training criteria typically enjoy the benefit of faster training and testing, at a cost of slightly degraded performance in terms of perplexity and almost no visible drop in word error rate. While noise contrastive estimation is one of the most popular choices, recently we show that other sampling-based criteria can also perform well, as long as an extra correction step is done, where the intended class posterior probability is recovered from the raw model outputs. In this work, we propose self-normalized importance sampling. Compared to our previous work, the criteria considered in this work are self-normalized and there is no need to further conduct a correction step. Through self-normalized language model training as well as lattice rescoring experiments, we show that our proposed self-normalized importance sampling is competitive in both research-oriented and production-oriented automatic speech recognition tasks.Comment: Accepted at INTERSPEECH 202