NeuralMultiling: A Novel Neural Architecture Search for Smartphone based Multilingual Speaker Verification

TL;DR

This paper introduces NeuralMultiling, a neural architecture search method designed for multilingual speaker verification on smartphones, outperforming existing models with fewer parameters and lower error rates.

Contribution

The paper presents a novel neural architecture search approach tailored for multilingual speaker verification on mobile devices, optimizing neural cell configurations for improved performance.

Findings

Outperforms Autospeech by 5-6% EER reduction

Uses fewer model parameters than existing methods

Effective in language-agnostic and cross-device scenarios

Abstract

Multilingual speaker verification introduces the challenge of verifying a speaker in multiple languages. Existing systems were built using i-vector/x-vector approaches along with Bi-LSTMs, which were trained to discriminate speakers, irrespective of the language. Instead of exploring the design space manually, we propose a neural architecture search for multilingual speaker verification suitable for mobile devices, called \textbf{NeuralMultiling}. First, our algorithm searches for an optimal operational combination of neural cells with different architectures for normal cells and reduction cells and then derives a CNN model by stacking neural cells. Using the derived architecture, we performed two different studies:1) language agnostic condition and 2) interoperability between languages and devices on the publicly available Multilingual Audio-Visual Smartphone (MAVS) dataset. The experimental results suggest that the derived architecture significantly outperforms the existing Autospeech method by a 5-6\% reduction in the Equal Error Rate (EER) with fewer model parameters.