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- W2896033767 abstract "Speech, speaker, and language systems have traditionally relied on carefully collected speech material for training acoustic models. There is an overwhelming abundance of publicly accessible audio material available for training. A major challenge, however, is that such found data is not professionally recorded, and therefore may contain a wide diversity of background noise, nonlinear distortions, or other unknown environmental or technology-based contamination or mismatch. There is a critical need for automatic analysis to screen such unknown data sets before acoustic model development training, or to perform input audio purity screening prior to classification. In this study, we propose a waveform based clipping detection algorithm for naturalistic audio streams and analyze the impact of clipping at different severities on speech quality measures and automatic speaker identification systems. We use the TIMIT and NIST-SRE08 speech corpora as case studies. The results show, as expected, that clipping introduces a nonlinear distortion into clean speech data, which reduces speech quality and performance for speaker recognition. We also investigate what degree of clipping can be present to sustain effective speech system performance. The proposed detection system, which will be released, could potentially contribute to new audio collections for speech and language technology development." @default.
- W2896033767 created "2018-10-26" @default.
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- W2896033767 date "2018-09-01" @default.
- W2896033767 modified "2023-09-24" @default.
- W2896033767 title "Nonlinear waveform distortion: Assessment and detection of clipping on speech data and systems" @default.
- W2896033767 doi "https://doi.org/10.1121/1.5068230" @default.
- W2896033767 hasPublicationYear "2018" @default.
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