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Search in speech recordings based on semantic vectors
Boboš, Dominik ; Karafiát, Martin (oponent) ; Schwarz, Petr (vedoucí práce)
In the current era of information overload, efficient methods for information retrieval are crucial. This thesis summarises methods for obtaining vector representations for text and audio, also known as semantic vectors. We took a deeper look at joint-representation models such as SpeechT5 and SeamlessM4T, which transform these various forms of input into one shared vector space. Based on these models, we built a system which allows us to search in data regardless of the modality. In order to evaluate the proposed solution on semantic search tasks, apart from standard keyword spotting tasks, we labelled a dataset to capture similar semantic meanings of the keywords or phrases. Finally, we conducted several experiments, where we explored the possibilities of the models used by limiting the context seen during finetuning or involving text-to-speech (TTS) systems to improve overall performance.
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Similarity Search in Document Collections
Jordanov, Dimitar Dimitrov ; Plchot, Oldřich (oponent) ; Smrž, Pavel (vedoucí práce)
The main objective of this work is to estimate the efficiency of the available software for similarity search in document collections and on two in particular, Semantic Vectors and Lecene's class MoreLikeThis. The paper provides a comparison of those two approaches and introduces methods that can lead to improving the quality of the results generated by a search.
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Similarity Search in Document Collections
Jordanov, Dimitar Dimitrov ; Plchot, Oldřich (oponent) ; Smrž, Pavel (vedoucí práce)
The main objective of this work is to estimate the efficiency of the available software for similarity search in document collections and on two in particular, Semantic Vectors and Lecene's class MoreLikeThis. The paper provides a comparison of those two approaches and introduces methods that can lead to improving the quality of the results generated by a search.
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