Improving Speaker Gender Detection by Combining Pitch and SDC

Title

Improving Speaker Gender Detection by Combining Pitch and SDC

Creator

Mohanty A.; Cherukuri R.C.

Description

Gender detection is helpful in various applications, such as speaker and emotion recognition, which helps with online learning, telecom caller identification, etc. This process is also used in speech analysis and initiating human-machine interaction. Gender detection is a complex process but an essential part of the digital world dealing with voice. The proposed approach is to detect gender from a speech by combining acoustic features like shifted delta cepstral (SDC) and pitch. The first step is preprocessing the speech sample to retrieve valid speech data. The second step is to calculate the pitch and SDC for each frame. The multifeature fusion method combines the speech features, and the XGBoost model is applied to detect gender. This approach results in accuracy rates of 99.44 and 99.37% with the help of RAVDESS and TIMIT datasets compared to the pre-defined methods. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Networks and Systems, Vol-818, pp. 451-462.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Datasets; Gender detection; MFCC; Pitch; SDC; XGBoost

Coverage

Mohanty A., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Cherukuri R.C., CHRIST (Deemed to be University), Karnataka, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981997861-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-99-7862-5_34

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187721881&doi=10.1007%2f978-981-99-7862-5_34&partnerID=40&md5=b92ed4600ed3204cbbff7df0f854fa38