Fast and parallel decoding for transducer

The transducer architecture is becoming increasingly popular in the field of speech recognition, because it is naturally streaming as well as high in accuracy. One of the drawbacks of transducer is that it is difficult to decode in a fast and parallel way due to an unconstrained number of symbols that can be emitted per time step. In this work, we introduce a constrained version of transducer loss to learn strictly monotonic alignments between the sequences; we also improve the standard greedy search and beam search algorithms by limiting the number of symbols that can be emitted per time step in transducer decoding, making it more efficient to decode in parallel with batches. Furthermore, we propose an finite state automaton-based (FSA) parallel beam search algorithm that can run with graphs on GPU efficiently. The experiment results show that we achieve slight word error rate (WER) improvement as well as significant speedup in decoding. Our work is open-sourced and publicly available\footnote{https://github.com/k2-fsa/icefall}.Comment: Submitted to 2023 IEEE International Conference on Acoustics, Speech and Signal Processin

Predicting Multi-Codebook Vector Quantization Indexes for Knowledge Distillation

Knowledge distillation(KD) is a common approach to improve model performance in automatic speech recognition (ASR), where a student model is trained to imitate the output behaviour of a teacher model. However, traditional KD methods suffer from teacher label storage issue, especially when the training corpora are large. Although on-the-fly teacher label generation tackles this issue, the training speed is significantly slower as the teacher model has to be evaluated every batch. In this paper, we reformulate the generation of teacher label as a codec problem. We propose a novel Multi-codebook Vector Quantization (MVQ) approach that compresses teacher embeddings to codebook indexes (CI). Based on this, a KD training framework (MVQ-KD) is proposed where a student model predicts the CI generated from the embeddings of a self-supervised pre-trained teacher model. Experiments on the LibriSpeech clean-100 hour show that MVQ-KD framework achieves comparable performance as traditional KD methods (l1, l2), while requiring 256 times less storage. When the full LibriSpeech dataset is used, MVQ-KD framework results in 13.8% and 8.2% relative word error rate reductions (WERRs) for non -streaming transducer on test-clean and test-other and 4.0% and 4.9% for streaming transducer. The implementation of this work is already released as a part of the open-source project icefall.Comment: Submitted to ICASSP 202

Critical role of G3BP1 in bovine parainfluenza virus type 3 (BPIV3)-inhibition of stress granules formation and viral replication

BackgroundIt remains unclear whether BPIV3 infection leads to stress granules formation and whether G3BP1 plays a role in this process and in viral replication. This study aims to clarify the association between BPIV3 and stress granules, explore the effect of G3BP1 on BPIV3 replication, and provide significant insights into the mechanisms by which BPIV3 evades the host’s antiviral immunity to support its own survival.MethodsHere, we use Immunofluorescence staining to observe the effect of BPIV3 infection on the assembly of stress granules. Meanwhile, the expression changes of eIF2α and G3BP1 were determined. Overexpression or siRNA silencing of intracellular G3BP1 levels was examined for its regulatory control of BPIV3 replication.ResultsWe identify that the BPIV3 infection elicited phosphorylation of the eIF2α protein. However, it did not induce the assembly of stress granules; rather, it inhibited the formation of stress granules and downregulated the expression of G3BP1. G3BP1 overexpression facilitated the formation of stress granules within cells and hindered viral replication, while G3BP1 knockdown enhanced BPIV3 expression.ConclusionThis study suggest that G3BP1 plays a crucial role in BPIV3 suppressing stress granule formation and viral replication

Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G

Telco content delivery networks (CDNs) have envisioned building highly distributed and cloudified sites to provide a high-quality CDN service in the 5G era. However, there are still two open problems to be addressed. First, telco CDNs are operated upon the underlay network evolving towards information-centric networking (ICN). Different from CDNs that perform on the application layer, ICN enables information-centric forwarding to the network layer. Thus, it is challenging to take advantage of the benefits of both ICN and CDN to provide a high-quality content delivery service in the context of ICN-based telco CDNs. Second, bandwidth pricing and request mapping issues in ICN-based telco CDNs have not been thoroughly studied. In this paper, we first propose an ICN-based telco CDN framework that integrates the information-centric forwarding enabled by ICN and the powerful edge caching enabled by telco CDNs. Then, we propose a location-dependent pricing (LDP) strategy, taking into consideration the congestion level of different sites. Furthermore, on the basis of LDP, we formulate a price-aware request mapping (PARM) problem, which can be solved by existing linear programming solvers. Finally, we conduct extensive simulations to evaluate the effectiveness of our design

An Approach to Pre-Schedule Traffic in Time-Dependent Pricing Systems

Time-dependent pricing (TDP) sets different prices in different time slots in order to motivate users to shift their delay-tolerant flows from congested time slots to less congested ones, thus helping Internet service providers (ISPs) utilize their network capacity more efficiently. In existing TDP approaches, however, once a flow is delayed to a less congested time slot by a user, the user has to wait until that time slot to consume the flow, even if there is idle capacity in earlier time slot(s) to accommodate the flow. In addition, in case that the traffic usage shifted to some time slots is so aggressive that new congestion is caused, it is hard for the ISP to accommodate more bursty traffic. To address these issues, in this paper we propose an approach to pre-schedule the delayed flows before their deadlines. Our results from extensive simulations show that the proposed approach could benefit both users and ISPs. For example, an ISP can smooth its bandwidth usage, which in turn makes it possible to accommodate more bursty traffic