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28th National Conference on Communications (NCC)

作者： Yajnanarayana, Vijaya Ericsson Res Lund Sweden

ISBN: (纸本)9781665451369

Beyond 5G networks will operate at high frequencies with wide bandwidths. This brings both opportunities and challenges. Opportunities include high throughput connectivity with low latency. However, one of the main challenges in these networks is due to the high path loss at these operating frequencies, which requires network to be deployed densely to provide coverage. Since these cells have small inter-site-distance (ISD), the dwell-time of the UEs in these cells are small, thus supporting mobility in these types of dense networks is a challenge and require frequent beam or cell reassignments. A pro-active mobility management scheme which exploits the historical trajectories can provide better prediction of cells and beams as UEs move in the coverage area. We propose an AI based method using sequence-to-sequence modeling for the estimation of handover cells/beams along with dwell-time using the trajectory information of the UE. Results indicate that for a dense deployment, an accuracy of more than 90 percent can be achieved for handover cell estimation and very low mean absolute error (MAE) for dwell-time.

关键词： Handover (HO) Mobility Machine-Learning sequence-to-sequence modeling Recurrent Neural Network (RNN) Beamforming Beam Prediction

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Any-to-Many Voice Conversion With Location-Relative sequence-to-sequence modeling

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2021年 29卷 1717-1728页

作者： Liu, Songxiang Cao, Yuewen Wang, Disong Wu, Xixin Liu, Xunying Meng, Helen Chinese Univ Hong Kong Dept Syst Engn & Engn Management Human Comp Commun Lab HCCL Hong Kong Peoples R China Univ Cambridge Engn Dept Cambridge CB2 1TN England

This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq), non-parallel voice conversion approach, which utilizes text supervision during training. In this approach, we combine a bottle-neck feature extractor (BNE) with a seq2seq synthesis module. During the training stage, an encoder-decoder-based hybrid connectionist-temporal-classification-attention (CTC-attention) phoneme recognizer is trained, whose encoder has a bottle-neck layer. A BNE is obtained from the phoneme recognizer and is utilized to extract speaker-independent, dense and rich spoken content representations from spectral features. Then a multi-speaker location-relative attention based seq2seq synthesis model is trained to reconstruct spectral features from the bottle-neck features, conditioning on speaker representations for speaker identity control in the generated speech. To mitigate the difficulties of using seq2seq models to align long sequences, we down-sample the input spectral feature along the temporal dimension and equip the synthesis model with a discretized mixture of logistic (MoL) attention mechanism. Since the phoneme recognizer is trained with large speech recognition data corpus, the proposed approach can conduct any-to-many voice conversion. Objective and subjective evaluations show that the proposed any-to-many approach has superior voice conversion performance in terms of both naturalness and speaker similarity. Ablation studies are conducted to confirm the effectiveness of feature selection and model design strategies in the proposed approach. The proposed VC approach can readily be extended to support any-to-any VC (also known as one/few-shot VC), and achieve high performance according to objective and subjective evaluations.

关键词： Hidden Markov models Feature extraction Training Acoustics Decoding Pipelines Computational modeling Any-to-many voice conversion location relative attention sequence-to-sequence modeling

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Seq2SeqPy: A Lightweight and Customizable Toolkit for Neural sequence-to-sequence modeling 12

Seq2SeqPy: A Lightweight and Customizable Toolkit for Neural...

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12th International Conference on Language Resources and Evaluation (LREC)

作者： Qader, Raheel Portet, Francois Labbe, Cyril Univ Grenoble Alpes LIG Grenoble France

ISBN: (纸本)9791095546344

We present Seq2SeqPy a lightweight toolkit for sequence-to-sequence modeling that prioritizes simplicity and ability to customize the standard architectures easily. The toolkit supports several known models such as Recurrent Neural Networks, Pointer Generator Networks, and transformer model. We evaluate the toolkit on two datasets and we show that the toolkit performs similarly or even better than a very widely used sequence-to-sequence toolkit.

关键词： sequence-to-sequence modeling deep learning toolkit

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SoURA: a user-reliability-aware social recommendation system based on graph neural network

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NEURAL COMPUTING & APPLICATIONS 2023年第25期35卷 18533-18551页

作者： Dawn, Sucheta Das, Monidipa Bandyopadhyay, Sanghamitra Indian Stat Inst Machine Intelligence Unit Kolkata India

Exploiting user trust information for developing a recommendation system has gained increasing research interest in recent years. Due to the exchange of opinions about items over the social network, trust plays a crucial role for a user to like or dislike an item. Graph Neural Networks (GNNs), which have the intrinsic power of integrating node information and topological structure, have a high potential to advance the field of trust-aware social recommendation. However, as of now, this area is little explored, with most of the existing GNN-based models ignoring the trust propagation and trust composition properties. To address this issue, in this paper, we propose a novel GNN-based framework that can capture such trust propagation and trust composition aspects by incorporating the concept of 'user-reliability.' Our proposed user-reliability-aware social recommendation framework, termed as SoURA, generates the user-embedding and item-embedding with consideration to the user-reliability values, which, in turn, helps in better evaluation of the user trust. Experimental evaluations on the benchmark Ciao and Epinion datasets demonstrate the effectiveness of incorporating user-reliability for finding user-embedding and item embedding in a social recommendation system. The proposed SoURA is found to show a minimum of 25% improvement over the state-of-the-art GNN-based recommendation algorithms.

关键词： Graph neural networks Recommender system User-reliability sequence-to-sequence modeling Trust-aware recommendation

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Sequential modeling by Leveraging Non-Uniform Distribution of Speech Emotion

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2023年 31卷 1087-1099页

作者： Lin, Wei-Cheng Busso, Carlos Univ Texas Dallas Erik Jonsson Sch Engn & Comp Sci Richardson TX 75080 USA

The expression and perception of human emotions are not uniformly distributed over time. Therefore, tracking local changes of emotion within a segment can lead to better models for speech emotion recognition (SER), even when the task is to provide a sentence-level prediction of the emotional content. A challenge to exploring local emotional changes within a sentence is that most existing emotional corpora only provide sentence-level annotations (i.e., one label per sentence). This labeling approach is not appropriate for leveraging the dynamic emotional trends within a sentence. We propose a framework that splits a sentence into a fixed number of chunks, generating chunk-level emotional patterns. The approach relies on emotion rankers to unveil the emotional pattern within a sentence, creating continuous emotional curves. Our approach trains the sentence-level SER model with a sequence-to-sequence formulation by leveraging the retrieved emotional curves. The proposed method achieves the best concordance correlation coefficient (CCC) prediction performance for arousal (0.7120), valence (0.3125), and dominance (0.6324) on the MSP-Podcast corpus. In addition, we validate the approach with experiments on the IEMOCAP and MSP-IMPROV databases. We further compare the retrieved curves with time-continuous emotional traces. The evaluation demonstrates that these retrieved chunk-label curves can effectively capture emotional trends within a sentence, displaying a time-consistency property that is similar to time-continuous traces annotated by human listeners. The proposed SER model learns meaningful, complementary, local information that contributes to the improvement of sentence-level predictions of emotional attributes.

关键词： Hidden Markov models Task analysis Emotion recognition Feature extraction Annotations Speech processing Databases Emotion rankers speech emotion recognition chunk-level segmentation sequence-to-sequence modeling

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ML-assisted Optimization of Securities Lending 23

ML-assisted Optimization of Securities Lending

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4th ACM International Conference on AI in Finance (ICAIF)

作者： Prasad, Abhinav Arunachalam, Prakash Motamedi, Ali Bhattacharya, Ranjeeta Liu, Beibei McCormick, Hays Skip Xu, Shengzhe Muralidhar, Nikhil Ramakrishnan, Naren Bank New York Mellon New York NY 10166 USA Virginia Tech Comp Sci Arlington VA USA Stevens Inst Technol Comp Sci Hoboken NJ USA

ISBN: (纸本)9798400702402

This paper presents an integrated methodology to forecast the direction and magnitude of movements of lending rates in security markets. We develop a sequence-to-sequence (seq2seq) modeling framework that integrates feature engineering, motif mining, and temporal prediction in a unified manner to perform forecasting at scale in real-time or near real-time. We have deployed this approach in a large custodial setting demonstrating scalability to a large number of equities as well as newly introduced IPO-based securities in highly volatile environments.

关键词： Securities Lending sequence-to-sequence modeling Motif Mining Deep Learning

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NetTraj: A Network-Based Vehicle Trajectory Prediction Model With Directional Representation and Spatiotemporal Attention Mechanisms

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 14470-14481页

作者： Liang, Yuebing Zhao, Zhan Univ Hong Kong Dept Urban Planning & Design Hong Kong Peoples R China

Trajectory prediction of vehicles in city-scale road networks is of great importance to various location-based applications such as vehicle navigation, traffic management, and location-based recommendations. Existing methods typically represent a trajectory as a sequence of grid cells, road segments or intention sets. None of them is ideal, as the cell-based representation ignores the road network structures and the other two are less efficient in analyzing city-scale road networks. Moreover, previous models barely leverage spatial dependencies or only consider them at the grid cell level, ignoring the non-Euclidean spatial structure shaped by irregular road networks. To address these problems, we propose a network-based vehicle trajectory prediction model named NetTraj, which represents each trajectory as a sequence of intersections and associated movement directions, and then feeds them into a LSTM encoder-decoder network for future trajectory generation. Furthermore, we introduce a local graph attention mechanism to capture network-level spatial dependencies of trajectories, and a temporal attention mechanism with a sliding context window to capture both short- and long-term temporal dependencies in trajectory data. Extensive experiments based on two real-world large-scale taxi trajectory datasets show that NetTraj outperforms the existing state-of-the-art methods for vehicle trajectory prediction, validating the effectiveness of the proposed trajectory representation method and spatiotemporal attention mechanisms.

关键词： Trajectory Roads Predictive models Hidden Markov models Data models Spatiotemporal phenomena Public transportation Trajectory prediction trajectory representation road networks sequence-to-sequence modeling spatiotemporal attention

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ANY-TO-ONE sequence-TO-sequence VOICE CONVERSION USING SELF-SUPERVISED DISCRETE SPEECH REPRESENTATIONS

ANY-TO-ONE SEQUENCE-TO-SEQUENCE VOICE CONVERSION USING SELF-...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Huang, Wen-Chin Wu, Yi-Chiao Hayashi, Tomoki Nagoya Univ Nagoya Aichi Japan

ISBN: (纸本)9781728176055

We present a novel approach to any-to-one (A2O) voice conversion (VC) in a sequence-to-sequence (seq2seq) framework. A2O VC aims to convert any speaker, including those unseen during training, to a fixed target speaker. We utilize vq-wav2vec (VQW2V), a discretized self-supervised speech representation that was learned from massive unlabeled data, which is assumed to be speaker-independent and well corresponds to underlying linguistic contents. Given a training dataset of the target speaker, we extract VQW2V and acoustic features to estimate a seq2seq mapping function from the former to the latter. With the help of a pretraining method and a newly designed postprocessing technique, our model can be generalized to only 5 min of data, even outperforming the same model trained with parallel data.

关键词： voice conversion any-to-one voice conversion self-supervised speech representation vq-wav2vec sequence-to-sequence modeling

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The IVI Lab entry to the GENEA Challenge 2022-A Tacotron2 Based Method for Co-Speech Gesture Generation With Locality-Constraint Atention Mechanism 22

The IVI Lab entry to the GENEA Challenge 2022-A Tacotron2 Ba...

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24th ACM International Conference on Multimodal Interaction (ICMI)

作者： Chang, Che-Jui Zhang, Sen Kapadia, Mubbasir Rutgers State Univ New Brunswick NJ 08901 USA

ISBN: (纸本)9781450393904

This paper describes the IVI Lab entry to the GENEA Challenge 2022. We formulate the gesture generation problem as a sequence-to-sequence conversion task with text, audio, and speaker identity as inputs and the body motion as the output. We use the Tacotron2 architecture as our backbone with the locality-constraint attention mechanism that guides the decoder to learn the dependencies from the neighboring latent features. The collective evaluation released by GENEA Challenge 2022 indicates that our two entries (FSH and USK) for the full body and upper body tracks statistically outperform the audio-driven and text-driven baselines on both two subjective metrics. Remarkably, our full-body entry receives the highest speech appropriateness (60.5% matched) among all submitted entries. We also conduct an objective evaluation to compare our motion acceleration and jerk with two autoregressive baselines. The result indicates that the motion distribution of our generated gestures is much closer to the distribution of natural gestures.

关键词： co-speech gesture generation sequence-to-sequence modeling locality constraint attention

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TOWARDS IDENTITY PRESERVING NORMAL TO DYSARTHRIC VOICE CONVERSION 47

TOWARDS IDENTITY PRESERVING NORMAL TO DYSARTHRIC VOICE CONVE...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Huang, Wen-Chin Halpern, Bence Mark Violeta, Lester Phillip Scharenborg, Odette Toda, Tomoki Nagoya Univ Nagoya Aichi Japan Delft Univ Technol Multimedia Comp Grp Delft Netherlands Univ Amsterdam Amsterdam Netherlands Netherlands Canc Inst Amsterdam Netherlands

ISBN: (纸本)9781665405409

We present a voice conversion framework that converts normal speech into dysarthric speech while preserving the speaker identity. Such a framework is essential for (1) clinical decision making processes and alleviation of patient stress, (2) data augmentation for dysarthric speech recognition. This is an especially challenging task since the converted samples should capture the severity of dysarthric speech while being highly natural and possessing the speaker identity of the normal speaker. To this end, we adopted a two-stage framework, which consists of a sequence-to-sequence model and a nonparallel frame-wise model. Objective and subjective evaluations were conducted on the UASpeech dataset, and results showed that the method was able to yield reasonable naturalness and capture severity aspects of the pathological speech. On the other hand, the similarity to the normal source speaker's voice was limited and requires further improvements.

关键词： voice conversion pathological speech dysarthric speech sequence-to-sequence modeling autoencoder

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