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Massaro et al. [7], for example, evaluated visual-only intelligibility of a speaker dependent speech driven system on isolated words.
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The proposed model resulted in letter error rates (LER's) of approximately 28% for a speaker independent system and 20% for a speaker dependent system.
The subspace bases are shared among all speakers, and a speaker dependent coordinate vector is estimated for each unknown speaker.
Each row in (14) says that each observation ϕ ij = μ + Fh i + Gw ij + ε ij consists of a speaker-dependent component μ + Fh i and session-dependent component Gw ij + ε ij, where ε ij ~ N 0, Σ is responsible for the residual variation in (10).
The purpose of designing a speaker-dependent creation set is that we expect that any given voice-tag will be created by a single user in real applications and not by multiple users.
The advantage of the adaptation approach is that the amount of user-specific speech material composes only a fraction of the data required for building a speaker-dependent state-of-the-art speech recognizer.
The degree of this linear warping is in fact a speaker-dependent factor and must be estimated for each speaker.
We assess the influence of prior knowledge of the speakers in a depression estimation experiment, using an iVector-based state-of-the-art approach to depression level estimation to perform a speaker-dependent experiment and a speaker-independent experiment.
Also, since we only have speech data for a small number of speakers, we evaluate our proposed method as a speaker-dependent system.
As with any examination of the effects of an outside influence on the behavior of the speech production system, we must approach our analysis from a speaker-dependent perspective.
During speaker adaptation, a speaker-dependent eigenphone matrix representing the main phone variation patterns for a specific speaker is estimated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com