Spoken Language Identification System for English-Mandarin Code-Switching Child-Directed Speech

TL;DR

This paper presents a two-stage Encoder-Decoder E2E model for robust language identification in challenging speech conditions, achieving significant error rate improvements on Singaporean child-directed code-switched speech.

Contribution

Introduces a lightweight Encoder-Decoder model tailored for non-standard, accented, and spontaneous code-switched speech, with curated datasets for public use.

Findings

Achieved 15.6% EER in closed track

Achieved 11.1% EER in open track

Curated and released additional Singaporean speech data

Abstract

This work focuses on improving the Spoken Language Identification (LangId) system for a challenge that focuses on developing robust language identification systems that are reliable for non-standard, accented (Singaporean accent), spontaneous code-switched, and child-directed speech collected via Zoom. We propose a two-stage Encoder-Decoder-based E2E model. The encoder module consists of 1D depth-wise separable convolutions with Squeeze-and-Excitation (SE) layers with a global context. The decoder module uses an attentive temporal pooling mechanism to get fixed length time-independent feature representation. The total number of parameters in the model is around 22.1 M, which is relatively light compared to using some large-scale pre-trained speech models. We achieved an EER of 15.6% in the closed track and 11.1% in the open track (baseline system 22.1%). We also curated additional LangId data from YouTube videos (having Singaporean speakers), which will be released for public use.