Semantic Query-by-example Speech Search Using Visual Grounding

By Herman Kamper, Aristotelis Anastassiou, Karen Livescu. 2019

31 citations recorded by the source corpus.

A number of recent studies have started to investigate how speech systems can be trained on untranscribed speech by leveraging accompanying images at training time. Examples of tasks include keyword prediction and within- and across-mode retrieval. Here we consider how such models can be used for query-by-example (QbE) search, the task of retrieving utterances relevant to a given spoken query. We are particularly interested in semantic QbE, where the task is not only to retrieve utterances containing exact instances of the query, but also utterances whose meaning is relevant to the query. We follow a segmental QbE approach where variable-duration speech segments (queries, search utterances) are mapped to fixed-dimensional embedding vectors. We show that a QbE system using an embedding function trained on visually grounded speech data outperforms a purely acoustic QbE system in terms of both exact and semantic retrieval performance.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Multimodal Learning, Speech & Audio, Signal Processing

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Citations in the corpus

Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech (2019, background): “…algorithms (Leidal et al., 2017; Kamper et al., 2017c; Havard et al., 2019a; Merkx et al., 2019; Chrupała et al., 2017; Scharenborg et al., 2018; Kamper et al., 2019b;a; Surı́s et al., 2019; Ilharco et al., 2019; Eloff et al., 2019a), application to multilingual settings (Harwath et al.,…”
On the Contributions of Visual and Textual Supervision in Low-resource Semantic Speech Retrieval (2019, unknown): “Such work has developed approaches for training models for tasks such as cross-modal retrieval [2,3]; unsupervised learning of word-like and phone-like units [7–10]; and retrieval tasks like keyword search, query-by-example search, and semantic search [6,11,12].”