Vietnamese Capitalization and Punctuation Recovery Models

Uyen, Hoang Thi Thu; Tu, Nguyen Anh; Huy, Ta Duc

doi:10.21437/interspeech.2022-931

Cited by 2 publications

(1 citation statement)

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“…Researchers have studied a wide variety of methods, including n-gram models [1], recurrent neural networks [2,3,4], adversarial models [5], contrastive learning [6], and transformers [7,8]. Conditional random fields [9,10,11,12] had particularly notable success. Direct fine-tuning of BERT [13] has also proven effective, which we demonstrate in Section 4.1.…”

Section: Related Workmentioning

confidence: 99%

Efficient Ensemble Architecture for Multimodal Acoustic and Textual Embeddings in Punctuation Restoration using Time-Delay Neural Networks

Liu¹,

Beigi²

2023

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Punctuation restoration plays an essential role in the postprocessing procedure of automatic speech recognition, but model efficiency is a key requirement for this task. To that end, we present EfficientPunct, an ensemble method with a multimodal time-delay neural network that outperforms the current best model by 1.0 F1 points, using less than a tenth of its parameters to process embeddings. We streamline a speech recognizer to efficiently output hidden layer latent vectors as acoustic embeddings for punctuation restoration, as well as BERT to extract meaningful text embeddings. By using forced alignment and temporal convolutions, we eliminate the need for multihead attention-based fusion, greatly increasing computational efficiency but also raising performance. Efficient-Punct sets a new state of the art, in terms of both performance and efficiency, with an ensemble that weights BERT's purely language-based predictions slightly more than the multimodal network's predictions.

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