Vocalic, Lexical and Prosodic Cues for the INTERSPEECH 2018 Self-Assessed Affect Challenge

Montacié, Claude; Caraty, Marie-José

doi:10.21437/interspeech.2018-1331

Cited by 13 publications

(11 citation statements)

References 37 publications

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“…Audio-based features provide prosodic cues related to the affective state of speakers [65]. Capturing the emotional valence of the CEO can alter the understanding of the underlying linguistic utterances in an earnings call [81].…”

Section: Harnessing Vocal Cuesmentioning

confidence: 99%

Multimodal Multi-Task Financial Risk Forecasting

Sawhney

Mathur

Mangal

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Stock price movement and volatility prediction aim to predict stocks' future trends to help investors make sound investment decisions and model financial risk. Companies' earnings calls are a rich, underexplored source of multimodal information for financial forecasting. However, existing fintech solutions are not optimized towards harnessing the interplay between the multimodal verbal and vocal cues in earnings calls. In this work, we present a multitask solution that utilizes domain specialized textual features and audio attentive alignment for predictive financial risk and price modeling. Our method advances existing solutions in two aspects: 1) tailoring a deep multimodal text-audio attention model, 2) optimizing volatility, and price movement prediction in a multi-task ensemble formulation. Through quantitative and qualitative analyses, we show the effectiveness of our deep multimodal approach. CCS CONCEPTS • Social and professional topics → Economic impact; • Computing methodologies → Natural language processing.

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Section: Harnessing Vocal Cuesmentioning

confidence: 99%

Multimodal Multi-Task Financial Risk Forecasting

Sawhney

Mathur

Mangal

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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“…Audio-based features provide prosodic cues related to the affective state of speakers (Montacié and Caraty, 2018). Capturing the emotional valence of the CEO can alter the understanding of the underlying linguistic utterances in an earnings call (Schröder et al, 2001).…”

Section: Vocal Cues: Audio Call Encodingmentioning

confidence: 99%

VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls

Sawhney

Khanna

Aggarwal

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Natural language processing has recently made stock movement forecasting and volatility forecasting advances, leading to improved financial forecasting. Transcripts of companies' earnings calls are well studied for risk modeling, offering unique investment insight into stock performance. However, vocal cues in the speech of company executives present an underexplored rich source of natural language data for estimating financial risk. Additionally, most existing approaches ignore the correlations between stocks. Building on existing work, we introduce a neural model for stock volatility prediction that accounts for stock interdependence via graph convolutions while fusing verbal, vocal, and financial features in a semi-supervised multi-task risk forecasting formulation. Our proposed model, VolTAGE, outperforms existing methods demonstrating the effectiveness of multimodal learning for volatility prediction.

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“…The captured speaker-traits are used in various tasks. E.g., assessing the self-affect of individuals [4], screening neurological diseases such as Mild Cognitive Impairment (MCI) [5], and psychological disorders like depression [6]. Also, in miscellaneous tasks, like estimating the conflict intensity [7] or determining personality traits of a speaker [8].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Embeddings for the Estimation of the Degree of Sleepiness

Egas-López

Gosztolya

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Estimating the degree of sleepiness from the human speech is an emerging research problem with straightforward applications. In this study, we employ the x-vector approach, currently the state-of-the-art in speaker recognition, as a neural network feature extractor to detect the level of sleepiness of a speaker. Besides using different corpora for fitting the xvector DNN, we also experiment with adding noise and reverberation to the training samples. According to our experimental results for the publicly available Dusseldorf Sleepy Language Corpus, utilizing x-vector embeddings as features for Support Vector Regression consistently leads to competitive performance scores in sleepiness detection. In particular, we present the highest Spearman's correlation coefficient on the public corpus that was achieved by a single method.

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Vocalic, Lexical and Prosodic Cues for the INTERSPEECH 2018 Self-Assessed Affect Challenge

Cited by 13 publications

References 37 publications

Multimodal Multi-Task Financial Risk Forecasting

Multimodal Multi-Task Financial Risk Forecasting

VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls

Deep Neural Network Embeddings for the Estimation of the Degree of Sleepiness

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