Computational modelling of speech data integration to assess interactions in B2B sales calls

Vered Silber-Varod; Anat Lerner; Nehoray Carmi; Daphna Amit; Yonathan Guttel; Chris Orlob; Omri Allouche

doi:10.1109/DataCom.2019.00031

Computational modelling of speech data integration to assess interactions in B2B sales calls

Vered Silber-Varod, Anat Lerner, Nehoray Carmi, Daphna Amit, Yonathan Guttel, Chris Orlob, Omri Allouche

Computer Science discipline

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The business sector now recognizes the value of Conversation Intelligence in understanding patterns, structures and insights of authentic conversation. Using machine learning methods, companies process massive amount of data about conversation content, vocal features and even speaker body gestures of spoken conversations. This study is a Work-in-Progress (WIP), aimed to modeling the dynamics between sales representatives and customers in business-to-business (B2B) sales calls, by relying solely on the acoustic signal. To this end, we analyze 358 sales calls at the Discovery phase. To model the conversations, we compute a basic set of acoustic features: Talk proportions, F0, intensity, harmonics-to-noise ratio (HNR), jitter, and shimmer. The plots of each acoustic feature reveal the interactions and common behavior across calls, on one hand, and within calls, on the other. The study demonstrates that using delta metrics to assess the interactions leads to new insights.

Original language	English
Title of host publication	Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	152-157
Number of pages	6
ISBN (Electronic)	9781728141176
DOIs	https://doi.org/10.1109/DataCom.2019.00031
State	Published - 2019
Event	5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019 - Kaohsiung, Taiwan, Province of China Duration: 18 Nov 2019 → 21 Nov 2019

Publication series

Name	Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019

Conference

Conference	5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019
Country/Territory	Taiwan, Province of China
City	Kaohsiung
Period	18/11/19 → 21/11/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

acoustic features
Conversation Intelligence
conversation modelling
sales calls
speech data

Access to Document

10.1109/DataCom.2019.00031

https://conferences.computer.org/datacom/2019/pdfs/DataCom2019-3MYdIPKpqxiurNWZaDmspf/5uhKofOkh2v4GoePJuW12F/7tX4H9Y6tr43Wqc2mb7TKM.pdf

Cite this

Silber-Varod, V., Lerner, A., Carmi, N., Amit, D., Guttel, Y., Orlob, C., & Allouche, O. (2019). Computational modelling of speech data integration to assess interactions in B2B sales calls. In Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019 (pp. 152-157). (Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DataCom.2019.00031

Silber-Varod, Vered ; Lerner, Anat ; Carmi, Nehoray et al. / Computational modelling of speech data integration to assess interactions in B2B sales calls. Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 152-157 (Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019).

@inproceedings{8a3aff1f724b40ae9286c9696c00b5fc,

title = "Computational modelling of speech data integration to assess interactions in B2B sales calls",

abstract = "The business sector now recognizes the value of Conversation Intelligence in understanding patterns, structures and insights of authentic conversation. Using machine learning methods, companies process massive amount of data about conversation content, vocal features and even speaker body gestures of spoken conversations. This study is a Work-in-Progress (WIP), aimed to modeling the dynamics between sales representatives and customers in business-to-business (B2B) sales calls, by relying solely on the acoustic signal. To this end, we analyze 358 sales calls at the Discovery phase. To model the conversations, we compute a basic set of acoustic features: Talk proportions, F0, intensity, harmonics-to-noise ratio (HNR), jitter, and shimmer. The plots of each acoustic feature reveal the interactions and common behavior across calls, on one hand, and within calls, on the other. The study demonstrates that using delta metrics to assess the interactions leads to new insights.",

keywords = "acoustic features, Conversation Intelligence, conversation modelling, sales calls, speech data",

author = "Vered Silber-Varod and Anat Lerner and Nehoray Carmi and Daphna Amit and Yonathan Guttel and Chris Orlob and Omri Allouche",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

year = "2019",

doi = "10.1109/DataCom.2019.00031",

language = "אנגלית",

series = "Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "152--157",

booktitle = "Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019",

address = "ארצות הברית",

}

Silber-Varod, V, Lerner, A, Carmi, N, Amit, D, Guttel, Y, Orlob, C & Allouche, O 2019, Computational modelling of speech data integration to assess interactions in B2B sales calls. in Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019. Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019, Institute of Electrical and Electronics Engineers Inc., pp. 152-157, 5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019, Kaohsiung, Taiwan, Province of China, 18/11/19. https://doi.org/10.1109/DataCom.2019.00031

Computational modelling of speech data integration to assess interactions in B2B sales calls. / Silber-Varod, Vered; Lerner, Anat; Carmi, Nehoray et al.
Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 152-157 (Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Computational modelling of speech data integration to assess interactions in B2B sales calls

AU - Silber-Varod, Vered

AU - Lerner, Anat

AU - Carmi, Nehoray

AU - Amit, Daphna

AU - Guttel, Yonathan

AU - Orlob, Chris

AU - Allouche, Omri

PY - 2019

Y1 - 2019

N2 - The business sector now recognizes the value of Conversation Intelligence in understanding patterns, structures and insights of authentic conversation. Using machine learning methods, companies process massive amount of data about conversation content, vocal features and even speaker body gestures of spoken conversations. This study is a Work-in-Progress (WIP), aimed to modeling the dynamics between sales representatives and customers in business-to-business (B2B) sales calls, by relying solely on the acoustic signal. To this end, we analyze 358 sales calls at the Discovery phase. To model the conversations, we compute a basic set of acoustic features: Talk proportions, F0, intensity, harmonics-to-noise ratio (HNR), jitter, and shimmer. The plots of each acoustic feature reveal the interactions and common behavior across calls, on one hand, and within calls, on the other. The study demonstrates that using delta metrics to assess the interactions leads to new insights.

AB - The business sector now recognizes the value of Conversation Intelligence in understanding patterns, structures and insights of authentic conversation. Using machine learning methods, companies process massive amount of data about conversation content, vocal features and even speaker body gestures of spoken conversations. This study is a Work-in-Progress (WIP), aimed to modeling the dynamics between sales representatives and customers in business-to-business (B2B) sales calls, by relying solely on the acoustic signal. To this end, we analyze 358 sales calls at the Discovery phase. To model the conversations, we compute a basic set of acoustic features: Talk proportions, F0, intensity, harmonics-to-noise ratio (HNR), jitter, and shimmer. The plots of each acoustic feature reveal the interactions and common behavior across calls, on one hand, and within calls, on the other. The study demonstrates that using delta metrics to assess the interactions leads to new insights.

KW - acoustic features

KW - Conversation Intelligence

KW - conversation modelling

KW - sales calls

KW - speech data

UR - http://www.scopus.com/inward/record.url?scp=85093885001&partnerID=8YFLogxK

U2 - 10.1109/DataCom.2019.00031

DO - 10.1109/DataCom.2019.00031

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

T3 - Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019

SP - 152

EP - 157

BT - Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019

Y2 - 18 November 2019 through 21 November 2019

ER -

Silber-Varod V, Lerner A, Carmi N, Amit D, Guttel Y, Orlob C et al. Computational modelling of speech data integration to assess interactions in B2B sales calls. In Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 152-157. (Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019). doi: 10.1109/DataCom.2019.00031

Computational modelling of speech data integration to assess interactions in B2B sales calls

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this