Attentive Temporal Pooling for Conformer-based Streaming Language Identification in Long-form Speech

Quan Wang; Yang Yu; Jason Pelecanos; Yiling Huang; Ignacio Lopez Moreno

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arXiv · 2202.12163

Attentive Temporal Pooling for Conformer-based Streaming Language Identification in Long-form Speech

Quan Wang·Yang Yu·Jason Pelecanos·Yiling Huang·Ignacio Lopez Moreno

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Abstract

In this paper, we introduce a novel language identification system based on conformer layers. We propose an attentive temporal pooling mechanism to allow the model to carry information in long-form audio via a recurrent form, such that the inference can be performed in a streaming fashion. Additionally, we investigate two domain adaptation approaches to allow adapting an existing language identification model without retraining the model parameters for a new domain. We perform a comparative study of different model topologies under different constraints of model size, and find that conformer-based models significantly outperform LSTM and transformer based models. Our experiments also show that attentive temporal pooling and domain adaptation improve model accuracy.

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BibTeX RIS

Quan Wang, Yang Yu, Jason Pelecanos, Yiling Huang, Ignacio Lopez Moreno. 2022-02-24. Attentive Temporal Pooling for Conformer-based Streaming Language Identification in Long-form Speech. https://arxiv.org/abs/2202.12163

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