Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-Labeling

Abstract

Speech Emotion Recognition (SER) application is frequently associated with privacy concerns as it often acquires and transmits speech data at the client-side to remote cloud platforms for further processing. These speech data can reveal not only speech content and affective information but the speaker's identity, demographic traits, and health status. Federated learning (FL) is a distributed machine learning algorithm that coordinates clients to train a model collaboratively without sharing local data. This algorithm shows enormous potential for SER applications as sharing raw speech or speech features from a user's device is vulnerable to privacy attacks. However, a major challenge in FL is limited availability of high-quality labeled data samples. In this work, we propose a semi-supervised federated learning framework, Semi-FedSER, that utilizes both labeled and unlabeled data samples to address the challenge of limited labeled data samples in FL. We show that our Semi-FedSER can generate desired SER performance even when the local label rate l = 20% using two SER benchmark datasets: IEMOCAP and MSP-Improv.

Federated Representation Learning for Automatic Speech Recognition

“…There are a handful of attempts in literature for applying FL in speech-related tasks. Some of these applications are: ASR [10,11,12,13,14], Keyword Spotting [15,16], Emotion Recognition [17,18,16], and Speaker Verification [19]. Notably, for combining FL with SSL, the only available works include Federated self-supervised learning (FSSL) [20] for acoustic event detection and [21], where the challenges involved in combining FL & SSL due to hardware limitations on the client are highlighted and a wav2vec 2.0 [4] model is trained with FL on Common-Voice Italian data [22] and fine-tuned for ASR.…”