Work

Unveiling Biases while Embracing Sustainability: Assessing the Dual Challenges of Automatic Speech Recognition Systems

Year: 2024

Type: conference-paper

Abstract: In this paper, we present a bias and sustainability focused investigation of Automatic Speech Recognition (ASR) systems, namely Whisper and Massively Multilingual Speech (MMS), which have achieved state-of-the-art (SOTA) performances. Despite their improved performance in controlled settings, there remains a critical gap in understanding their efficacy and equity in real-world scenarios. We analyze ASR biases w.r.t. gender, accent, and age group, as well as their effect on downstream tasks. In addition, we examine the environmental impact of ASR systems, scrutinizing the use of large acoustic models on carbon emission and energy consumption. We also provide insights into our empirical analyses, offering a valuable contribution to the claims surrounding bias and sustainability in ASR systems.

Source: Interspeech (USB)

DOI: 10.21437/interspeech.2024-2494

Authors: Ajinkya Kulkarni, Atharva Kulkarni, Miguel Couceiro, Isabel Trancoso

Institutions: Idiap Research Institute, Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento, INESC TEC

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: green

Funder: Agence Nationale de la Recherche