Sentence examples for speaker sample from inspiring English sources

Given our ethnically diverse speaker sample, we further ask how the coder's perception of a speaker's ethnicity interacts with their vocalization coding decisions.

Speaker samples also were analyzed acoustically.

Methods: Thirty adult dysphonic and six normal speaker samples were selected from a database.

The sentences are from ITU-T Recommendation P.501 [47] which contains two male and two female (British) English speakers sampled at 32 kHz.

The corpus is composed of 32 source signals, each consisting in three sentences pronounced by the same speaker (32 different speakers), sampled at 16 kHz, truncated to 5 s.

This suggests that the results are largely driven by the 'poor language speakers' sub-sample.

The speech data were spoken by three male and three female speakers and sampled at 8 kHz.

Twenty test phrases, spoken by seven speakers and sampled at 8 kHz, were used as the experimental data.

To help address this issue, although this likely deserves a more in-depth analysis, an additional experiment was run where the correlations between sub-topics and phone length were re-computed using only the speaker-session samples that had a Psychomotor Retardation score of 0 (i.e., no recorded psychomotor retardation).

In contrast, the majority of mtDNA sequences from Mozambique in the Southeast region match sequences from multiple regions, and only a small percentage of these sequences are unique to ethnic groups from Mozambique, perhaps reflecting the fact that only Bantu speakers were sampled [ 5, 6].

Table 5 Conditions for speaker recognition   Values Sampling frequency 16 kHz Frame length 25 ms Frame shift 10 ms Feature space 25 dimensions with CMN   (12 MFCCs + Δ + Δpower) Acoustic model GMMs with 128 diagonal   covariance matrices.