However, Yu et al. (2015) found that English-speaking individuals are not likely to differ in their coarticulatory phonetic realizations across time, showing high within-speaker consistency.
On the other hand, Yu et al. (2015) showed that English-speaking individuals are not likely to differ in their coarticulatory phonetic realizations across time, showing high within-speaker consistency.
On the other hand, if the first language acquisition process can somehow make the native speakers become more resistant to the influence of speaker proficiency on within-speaker variability, then one would find the overall intra-speaker variability to be fairly comparable across speakers of different proficiency levels.
On the other hand, if Min speakers treat the realization of /dz/ as more like coarticulatory effects found in English, then one would expect that much of the variability in /dz/ realization comes from speakers' various sociolinguistic backgrounds, rather than within-speaker idiosyncratic variations.
Therefore, this study followed the design of Chuang and Fon (2017) and utilized extended paragraphs to allow talkers to reveal a fuller spectrum of their realization variability so that interaction among speaker gender, Min proficiency, and within-speaker consistency could be more clearly analyzed.
One possible reason for this result is that the performance of EFR normalization is highly dependent on the scale of development dataset, and in our experiment, we did not offer that much corpus to construct full-scale between- and within-speaker covariance matrices.
Frequency data measured in Hz were normalised to account for anatomical differences between male and female speakers, and for within-speaker variability [45].
A fourth condition, intended as a low-emotion distractor set, was constructed by manually combining parts of all 3 emotion conditions, within-speaker, to create 21 "mixed" stimuli (Speaker A: 8, Speaker B: 6, Speaker C: 7; mean duration 2.96 s).
Within speaker, our data suggests that articulation of either /r/ or /s/ does not predict articulation of the other.
We show that a simple loss function which maximizes the dissimilarity between near frames and long distance frames helps to construct a speech embedding that improves phoneme discriminability, both within and across speakers, even though the loss function only uses within speaker information.
According to [4], feature extraction within speaker identification should be less influenced by noise or the person's health.
