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ICA in [9] was applied to speech data in the time or time-frequency domain, and gave good performance in phoneme recognition tasks.
In [10], LDA that was applied to speech data in the time-frequency domain showed better performance than combined linear discriminants in the temporal and spectral domain in continuous digit recognition task.
Text to speech data conversion.
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The company is also using an even larger supercomputer cluster to analyse speech data to improve its voice recognition.
As perceptual reference the Dutch Intelligibility Assessment (DIA) was applied to the speech data to measure the phoneme intelligibility.
The questions and discussion topics were designed to elicit speech data which have their contents conformed to the domains of potential automatic speech recognition (ASR) applications such as transportation, tourism, and health care.
Our goal is to answer two questions: First, what is the best way to annotate speech data with multiple emotions – should we use the labels that the speaker intended to express, or labels based on listener perception of the resulting speech signals?
Eight types of additive noises are artificially added to clean speech data with SNR levels ranging from 20 to -5 dB.
The noisy speech signals were obtained by adding noise signals to clean speech data.
Case 5: Neutral speech recognition using neutral speech models adapted to whispered speech data (on dissimilar train and test data).
Case 6: Whispered speech recognition using neutral speech models adapted to whispered speech data (on whisper adaptation data).
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