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  • An improved speech transmission index for intelligibility prediction

    Author(s)
    Schwerin, Belinda
    Paliwal, Kuldip
    Griffith University Author(s)
    Schwerin, Belinda M.
    Paliwal, Kuldip K.
    Year published
    2014
    Metadata
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    Abstract
    The speech transmission index (STI) is a well known measure of intelligibility, most suited to the evaluation of speech intelligibility in rooms, with stimuli subjected to additive noise and reverberance. However, STI and its many variations do not effectively represent the intelligibility of stimuli containing non-linear distortions such as those resulting from processing by enhancement algorithms. In this paper, we revisit the STI approach and propose a variation which processes the modulation envelope in short-time segments, requiring only an assumption of quasi-stationarity (rather than the stationarity assumption of ...
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    The speech transmission index (STI) is a well known measure of intelligibility, most suited to the evaluation of speech intelligibility in rooms, with stimuli subjected to additive noise and reverberance. However, STI and its many variations do not effectively represent the intelligibility of stimuli containing non-linear distortions such as those resulting from processing by enhancement algorithms. In this paper, we revisit the STI approach and propose a variation which processes the modulation envelope in short-time segments, requiring only an assumption of quasi-stationarity (rather than the stationarity assumption of STI) of the modulation signal. Results presented in this work show that the proposed approach improves the measures correlation to subjective intelligibility scores compared to traditional STI for a range of noise types and subjected to different enhancement approaches. The approach is also shown to have higher correlation than other coherence, correlation and distance measures tested, but is unsuited to the evaluation of stimuli heavily distorted with (for example) masking based processing, where an alternative approach such as STOI is recommended.
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    Journal Title
    Speech Communication
    Volume
    65
    DOI
    https://doi.org/10.1016/j.specom.2014.05.003
    Subject
    Signal Processing
    Artificial Intelligence and Image Processing
    Cognitive Sciences
    Linguistics
    Publication URI
    http://hdl.handle.net/10072/64207
    Collection
    • Journal articles

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