2020 International Conference on Signal Processing and Communications (SPCOM)最新文献_第4页

Jointly learning to align and transcribe using attention-based alignment and uncertainty-to-weigh losses 使用基于注意力的对齐和不确定性来权衡损失，共同学习对齐和转录

2020 International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2020-07-01 DOI: 10.1109/SPCOM50965.2020.9179519

Shreekantha Nadig, S. Chakraborty, Anuj K. Shah, Chaitanaya Sharma, V. Ramasubramanian, Sachit Rao

{"title":"Jointly learning to align and transcribe using attention-based alignment and uncertainty-to-weigh losses","authors":"Shreekantha Nadig, S. Chakraborty, Anuj K. Shah, Chaitanaya Sharma, V. Ramasubramanian, Sachit Rao","doi":"10.1109/SPCOM50965.2020.9179519","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179519","url":null,"abstract":"End-to-end Automatic Speech Recognition (ASR) models with attention, especially the Joint Connectionist Temporal Classification (CTC) and Attention in Encoder-Decoder models have shown promising results. In this joint CTC and Attention framework, misalignment of attention with the ground truth is not penalised, as the focus is on optimising only the CTC and Attention cost functions. In this paper, a function that additionally minimizes alignment errors is introduced. This function is expected to enable the ASR system to attend to the right part of the input sequence, and in turn, minimize alignment and transcription errors. We also implement a dynamic weighting of losses corresponding with the tasks of CTC, attention, and alignment. We demonstrate that in many cases, the proposed design framework results in better performance and faster convergence. We show results on two datasets - TIMIT and Librispeech 100 hours for the phone recognition task by taking the alignments from a previously trained monophone Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) model.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129054568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Speech rate estimation using representations learned from speech with convolutional neural network 基于卷积神经网络语音学习表征的语音速率估计

2020 International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2020-07-01 DOI: 10.1109/SPCOM50965.2020.9179502

Renuka Mannem, H. Jyothi, Aravind Illa, P. Ghosh

{"title":"Speech rate estimation using representations learned from speech with convolutional neural network","authors":"Renuka Mannem, H. Jyothi, Aravind Illa, P. Ghosh","doi":"10.1109/SPCOM50965.2020.9179502","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179502","url":null,"abstract":"With advancement in machine learning techniques, several speech related applications deploy end-to-end models to learn relevant features from the raw speech signal. In this work, we focus on the speech rate estimation task using an end-to-end model to learn representation from raw speech in a data driven manner. We propose an end-to-end model that comprises of 1-d convolutional layer to extract representations from raw speech and a convolutional dense neural network (CDNN) to predict speech rate from these representations. The primary aim of the work is to understand the nature of representations learned by end-to-end model for the speech rate estimation task. Experiments are performed using TIMIT corpus, in seen and unseen subject conditions. Experimental results reveal that, the frequency response of the learned 1-d CNN filters are low-pass in nature, and center frequencies of majority of the filters lie below 1000Hz. While comparing the performance of the proposed end-to-end system with the baseline MFCC based approach, we find that the performance of the learned features with CNN are on par with MFCC.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115575764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Tool for image annotation based on gaze 基于注视的图像注释工具

2020 International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2020-07-01 DOI: 10.1109/SPCOM50965.2020.9179496

Mallampalli Kapardi, Satya Patel, Raghu Sesha Iyengar, K. S. Sridharan, M. Raghavan

引用次数: 0

Neurochaos Inspired Hybrid Machine Learning Architecture for Classification 基于神经混沌的混合机器学习分类体系结构

2020 International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2020-07-01 DOI: 10.1109/SPCOM50965.2020.9179632

H. N. Harikrishnan, N. Nagaraj

引用次数: 9

Design of Puncturing for Length-Compatible Polar Codes Using Differential Evolution 基于差分进化的长度兼容极化码穿刺设计

2020 International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2020-07-01 DOI: 10.1109/SPCOM50965.2020.9179612

K. Deka, S. Sharma

引用次数: 0

SPCOM 2020 Front Matter SPCOM 2020前沿问题

2020 International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2020-07-01 DOI: 10.1109/spcom50965.2020.9179615

引用次数: 0

Impact of User and Relay Hardware Impairments on Spectral Efficiency of HD Massive MIMO Relay 用户和中继硬件缺陷对高清大规模MIMO中继频谱效率的影响

2020 International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2020-07-01 DOI: 10.1109/SPCOM50965.2020.9179574

S. Dey, E. Sharma, Rohit Budhiraja

引用次数: 0

On the Improved Memory Utilization in HARQ Pooling 提高HARQ池中内存利用率的研究

2020 International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2020-07-01 DOI: 10.1109/SPCOM50965.2020.9179580

S. K. Vankayala, S. AshokKrishnanK., RaviTeja Gundeti, Konchady Gautam Shenoy

引用次数: 0

Mixed FSO/RF SIMO SWIPT Decode-and-Forward Relaying Systems 混合FSO/RF SIMO SWIPT译码转发中继系统

2020 International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2020-07-01 DOI: 10.1109/SPCOM50965.2020.9179563

Rupender Singh, M. Rawat, Anshul Jaiswal

{"title":"Mixed FSO/RF SIMO SWIPT Decode-and-Forward Relaying Systems","authors":"Rupender Singh, M. Rawat, Anshul Jaiswal","doi":"10.1109/SPCOM50965.2020.9179563","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179563","url":null,"abstract":"This article focuses on a dual-hop simultaneous wireless information and power transfer (SWIPT) single-input-multiple-output (SIMO) system having one free-space optical (FSO) link followed by one radio frequency (RF) link. We assume that RF source conveys secure information to the FSO receiver through an intermediate relay equipped with multiple antenna, which exploits the decode-and-forward relaying technique. The FSO link endures pointing error and atmospheric turbulence, which is modeled as Gamma-Gamma distribution, and RF link suffers from Fisher-Snedecor F fading. We investigate the consequences of number of antennas, pointing error, atmospheric turbulence, detection technology, electrical signal-to-noise ratio of FSO link, and energy harvesting on the performance of proposed SIMO mixed FSO/RF SWIPT framework. More specifically, we derive the unified analytical expressions for statistical channel characteristics such as probability density function, moments, amount of fading, outage probability, and ergodic capacity. Based on these results, physical layer security analysis is carried out, and the analytical expressions for secure outage probability and strictly positive secrecy capacity are derived.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125864482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Small Segment Emphasized Performance Evaluation Metric for Medical Images 医学图像小片段强调性能评价指标

2020 International Conference on Signal Processing and Communications (SPCOM) Pub Date : 2020-07-01 DOI: 10.1109/SPCOM50965.2020.9179617

R. Ammu, N. Sinha

{"title":"Small Segment Emphasized Performance Evaluation Metric for Medical Images","authors":"R. Ammu, N. Sinha","doi":"10.1109/SPCOM50965.2020.9179617","DOIUrl":"https://doi.org/10.1109/SPCOM50965.2020.9179617","url":null,"abstract":"Automatic image segmentation and quantification are critical steps in medical image analysis. The main challenges in medical image segmentation are due to the imbalance in data distribution and spatial variations of ROI. The ideal segmentation should extract all kinds of segments irrespective of size, shape and position. Commonly used metrics such as accuracy, IOU, Dice similarity coefficient consider all the detected pixels in a similar way. However, the detection of smaller segments is critical in medical analysis since it helps in early treatment of the disease and are also easier to miss. Hence, segmentation evaluation must accord larger weighting to pixels in smaller segments compared to the bigger ones. We propose a novel evaluation metric for segmentation performance, emphasizing smaller segments, by assigning a higher weightage to those pixels. Weighted false positives are also considered in deriving the new metric named, “SSEGEP” (Smatt SEGment Emphasized Performance evaluation metric), (range: 0 (Bad) to 1 (Good)). The proposed approach has been applied to two different publicly available real medical data sets of CT modality consisting of scans of the liver and pancreas of 131 and 107 subjects respectively and the results have been compared with existing evaluation metrics. Statistical significance testing is performed to quantity the relevance of the proposed approach. In comparison to Dice similarity coefficient, SSEGEP resulted in a promising p-value of the order 10-18 for hepatic tumor. The proposed metric is found to perform better for the images having multiple segments for a single label and where the regions of interest are not localized.","PeriodicalId":208527,"journal":{"name":"2020 International Conference on Signal Processing and Communications (SPCOM)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127166380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2