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

Autor:	Wang, Quan, Yu, Yang, Pelecanos, Jason, Huang, Yiling, Moreno, Ignacio Lopez
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Computation and Language Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.12163 Zobrazit plný text záznamu View this record from Arxiv