Learning Acoustic Word Embeddings with Temporal Context for Query-by-Example Speech Search
This addresses the need for efficient and accurate speech search systems, but it is incremental as it builds on existing embedding methods with added temporal context.
The paper tackles the problem of query-by-example speech search by learning acoustic word embeddings with temporal context, resulting in improved performance over state-of-the-art frame-level features and reduced run-time computation by eliminating dynamic time warping.
We propose to learn acoustic word embeddings with temporal context for query-by-example (QbE) speech search. The temporal context includes the leading and trailing word sequences of a word. We assume that there exist spoken word pairs in the training database. We pad the word pairs with their original temporal context to form fixed-length speech segment pairs. We obtain the acoustic word embeddings through a deep convolutional neural network (CNN) which is trained on the speech segment pairs with a triplet loss. Shifting a fixed-length analysis window through the search content, we obtain a running sequence of embeddings. In this way, searching for the spoken query is equivalent to the matching of acoustic word embeddings. The experiments show that our proposed acoustic word embeddings learned with temporal context are effective in QbE speech search. They outperform the state-of-the-art frame-level feature representations and reduce run-time computation since no dynamic time warping is required in QbE speech search. We also find that it is important to have sufficient speech segment pairs to train the deep CNN for effective acoustic word embeddings.