Below is an abstract from a computer science research paper. Given this abstract, your task is to extract all unique relationships between entities of the following types:

- "USED-FOR": For instance, "Our method models user proficiency" -> ["method", "user proficiency", "USED-FOR"].
- "EVALUATE-FOR": For instance, "We evaluate our method using F1-measure" -> ["F1-measure", "method", "EVALUATE-FOR"].
- "FEATURE-OF": For instance, "prior knowledge of the model" -> ["prior knowledge", "model", "FEATURE-OF"].
- "HYPONYM-OF": For instance, "TUIT is a software library" -> ["TUIT", "software library", "HYPONYM-OF"].
- "PART-OF": For instance, "We incorporate NLU module into the system." -> ["NLU module", "system", "PART-OF"].
- "COMPARE": For instance, "Unlike the quantitative prior, the qualitative prior is often ignored" -> ["quantitative prior", "qualitative prior", "COMPARE"].
- "CONJUNCTION": For instance: "obtained from human expert or knowledge base" -> ["human expert", "knowledge base", "CONJUNCTION"].

Format your output as a json array. Each entry in the array should express a single relation, formatted as ["<Entity_A>", "<Entity_B>", "<RELATION_A_B>"]. An example output might look like:

[["neural networks", "classification", "USED-FOR"], ["neuron", "neural network", "PART-OF"]].

If no relations are found, please return an empty array [].

Abstract:

In this paper we evaluate four objective measures of speech with regards to intelligibility prediction of synthesized speech in diverse noisy situations. We evaluated three intel-ligibility measures, the Dau measure, the glimpse proportion and the Speech Intelligibility Index (SII) and a quality measure, the Perceptual Evaluation of Speech Quality (PESQ). For the generation of synthesized speech we used a state of the art HMM-based speech synthesis system. The noisy conditions comprised four additive noises. The measures were compared with subjective intelligibility scores obtained in listening tests. The results show the Dau and the glimpse measures to be the best predictors of intelligibility, with correlations of around 0.83 to subjective scores. All measures gave less accurate predictions of intelligibility for synthetic speech than have previously been found for natural speech; in particular the SII measure. In additional experiments, we processed the synthesized speech by an ideal binary mask before adding noise. The Glimpse measure gave the most accurate intelligibility predictions in this situation.
[["HMM-based speech synthesis system", "generation of synthesized speech", "USED-FOR"], ["Dau measure", "glimpse proportion", "CONJUNCTION"], ["Dau measure", "intel-ligibility measures", "HYPONYM-OF"], ["glimpse proportion", "intel-ligibility measures", "HYPONYM-OF"], ["correlations", "Dau measure", "EVALUATE-FOR"], ["Speech Intelligibility Index (SII)", "intel-ligibility measures", "HYPONYM-OF"], ["Dau measure", "predictors of intelligibility", "HYPONYM-OF"], ["synthetic speech", "natural speech", "COMPARE"], ["synthetic speech", "intelligibility prediction", "USED-FOR"], ["glimpse proportion", "intelligibility prediction", "USED-FOR"], ["glimpse proportion", "predictors of intelligibility", "HYPONYM-OF"], ["additive noises", "noisy conditions", "PART-OF"], ["diverse noisy situations", "synthesized speech", "FEATURE-OF"], ["intel-ligibility measures", "quality measure", "CONJUNCTION"], ["SII measure", "measures of speech", "HYPONYM-OF"], ["Perceptual Evaluation of Speech Quality (PESQ)", "quality measure", "HYPONYM-OF"], ["measures of speech", "intelligibility prediction", "EVALUATE-FOR"], ["synthesized speech", "intelligibility prediction", "USED-FOR"], ["glimpse proportion", "Speech Intelligibility Index (SII)", "CONJUNCTION"], ["Dau measure", "subjective scores", "COMPARE"], ["glimpse proportion", "subjective scores", "COMPARE"], ["measures of speech", "subjective intelligibility scores", "COMPARE"], ["correlations", "glimpse proportion", "EVALUATE-FOR"], ["ideal binary mask", "synthesized speech", "USED-FOR"]]