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    "abbrevTitle": "SMPTE Motion Imaging Jour.",
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    "abstract": "Speech intelligibility and listening effort are receiving growing interest in broadcast. Since extensive expert assessments and formal listening tests are too costly in practice, instrumental measures to analyze intelligibility are required. Existing measures include energy-based methods (e.g., speech-to-background loudness differences) and auditory model-based methods, which mimic the auditory system and assess performance by comparing how well important speech features are preserved in a mix. A third class of measures makes use of advances in automatic speech recognition technologies. These approaches have recently been shown to generalize well to speech-impairing factors not included during training, suggesting they may be applicable across a variety of listening conditions. This may be an advantage in broadcast, where there are many different potential sources of poor intelligibility which are difficult to capture with traditional metrics. This contribution discusses potentials, limitations, and possible applications of current intelligibility measures.",
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    "authors": [
      {
        "name": "J. Rennies"
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      {
        "name": "H. Baumgartner"
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      {
        "name": "R. Huber"
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      {
        "name": "C. Rollwage"
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    "docLabel": "MIJ 2026, Volume 135, Number 5 (pp. 38 to 44)",
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    "docTitle": "Reference-Free Measurement of Dialog Intelligibility in (Post-) Production, Content Analyses, and Live Broadcast Applications",
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    "doi": "10.5594/JMI.2026/BCUK3128",
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    "issn": {
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    "keywords": [
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        "DIALOG INTELLIGIBILITY",
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