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Impact of Naturalistic Field Acoustic Environments on Forensic Text-independent Speaker Verification System ...
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Speech variability: A cross-language study on acoustic variations of speaking versus untrained singing
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In: J Acoust Soc Am (2020)
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A speech perturbation strategy based on “Lombard effect” for enhanced intelligibility for cochlear implant listeners
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In: J Acoust Soc Am (2020)
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Leveraging native language information for improved accented speech recognition ...
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Quantifying Cochlear Implant Users’ Ability for Speaker Identification using CI Auditory Stimuli
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In: Interspeech (2019)
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Advancing Multi-Accented LSTM-CTC Speech Recognition using a Domain Specific Student-Teacher Learning Paradigm ...
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UTD-CRSS Submission for MGB-3 Arabic Dialect Identification: Front-end and Back-end Advancements on Broadcast Speech ...
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Robust Harmonic Features for Classification-Based Pitch Estimation
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The Lombard effect observed in speech produced by cochlear implant users in noisy environments: A naturalistic study
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KU-ISPL Language Recognition System for NIST 2015 i-Vector Machine Learning Challenge ...
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Robust Speech Processing & Recognition: Speaker ID, Language ID, Speech Recognition/Keyword Spotting, Diarization/Co-Channel/Environmental Characterization, Speaker State Assessment
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In: DTIC (2015)
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Multivariate cepstral feature compensation on band-limited data for robust speech recognition
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Evaluation and analysis of whispered speech for cochlear implant users: Gender identification and intelligibility
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Evaluation of the importance of time-frequency contributions to speech intelligibility in noise
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Abstract:
Recent studies on binary masking techniques make the assumption that each time-frequency (T-F) unit contributes an equal amount to the overall intelligibility of speech. The present study demonstrated that the importance of each T-F unit to speech intelligibility varies in accordance with speech content. Specifically, T-F units are categorized into two classes, speech-present T-F units and speech-absent T-F units. Results indicate that the importance of each speech-present T-F unit to speech intelligibility is highly related to the loudness of its target component, while the importance of each speech-absent T-F unit varies according to the loudness of its masker component. Two types of mask errors are also considered, which include miss and false alarm errors. Consistent with previous work, false alarm errors are shown to be more harmful to speech intelligibility than miss errors when the mixture signal-to-noise ratio (SNR) is below 0 dB. However, the relative importance between the two types of error is conditioned on the SNR level of the input speech signal. Based on these observations, a mask-based objective measure, the loudness weighted hit-false, is proposed for predicting speech intelligibility. The proposed objective measure shows significantly higher correlation with intelligibility compared to two existing mask-based objective measures.
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Keyword:
Speech Perception [71]
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URL: https://doi.org/10.1121/1.4869088 http://www.ncbi.nlm.nih.gov/pubmed/24815280 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4032418/
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