Acoustic Word Embeddings for Untranscribed Target Languages with Continued Pretraining and Learned Pooling

• URL: http://arxiv.org/abs/2306.02153v1
• Date: Sat, 3 Jun 2023 16:44:21 GMT
• Title: Acoustic Word Embeddings for Untranscribed Target Languages with Continued Pretraining and Learned Pooling
• Authors: Ramon Sanabria, Ondrej Klejch, Hao Tang, Sharon Goldwater
• Abstract summary: Acoustic word embeddings are created by training a pooling function using pairs of word-like units. Mean-pooled representations from a self-supervised English model were suggested as a promising alternative, but their performance on target languages was not fully competitive. We show that both approaches outperform a recent approach on word discrimination.
• Score: 28.758396218435635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Acoustic word embeddings are typically created by training a pooling function using pairs of word-like units. For unsupervised systems, these are mined using k-nearest neighbor (KNN) search, which is slow. Recently, mean-pooled representations from a pre-trained self-supervised English model were suggested as a promising alternative, but their performance on target languages was not fully competitive. Here, we explore improvements to both approaches: we use continued pre-training to adapt the self-supervised model to the target language, and we use a multilingual phone recognizer (MPR) to mine phone n-gram pairs for training the pooling function. Evaluating on four languages, we show that both methods outperform a recent approach on word discrimination. Moreover, the MPR method is orders of magnitude faster than KNN, and is highly data efficient. We also show a small improvement from performing learned pooling on top of the continued pre-trained representations.

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