Speech Communication - Special issue on speech under stress
Speech Communication - Special issue on speech under stress
A Speech Parameter Generation Algorithm Considering Global Variance for HMM-Based Speech Synthesis
IEICE - Transactions on Information and Systems
Review: Statistical parametric speech synthesis
Speech Communication
An adaptive algorithm for mel-cepstral analysis of speech
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
IEEE Transactions on Audio, Speech, and Language Processing
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This paper describes speech intelligibility enhancement for Hidden Markov Model (HMM) generated synthetic speech in noise. We present a method for modifying the Mel cepstral coefficients generated by statistical parametric models that have been trained on plain speech. We update these coefficients such that the glimpse proportion - an objective measure of the intelligibility of speech in noise - increases, while keeping the speech energy fixed. An acoustic analysis reveals that the modified speech is boosted in the region 1-4kHz, particularly for vowels, nasals and approximants. Results from listening tests employing speech-shaped noise show that the modified speech is as intelligible as a synthetic voice trained on plain speech whose duration, Mel cepstral coefficients and excitation signal parameters have been adapted to Lombard speech from the same speaker. Our proposed method does not require these additional recordings of Lombard speech. In the presence of a competing talker, both modification and adaptation of spectral coefficients give more modest gains.