Sentence examples for audio conditions from inspiring English sources
Audio-visual speech recognition systems significantly outperform their audio-only counterparts, especially under ideal visual and noisy audio conditions.
In this paper, we introduce probabilistic model based architecture for error handling in human robot spoken dialogue systems under adverse audio conditions.
The classifier utilizes a fixed decision boundary that has been specifically designed to account for the effects of noisy audio conditions.
In addition, the database conditions (spontaneous speech and challenging audio conditions) chosen for the experiments and the highly heterogeneous list of terms (single- and multi-word terms, in-vocabulary and out-of-vocabulary terms, and in-language and foreign terms) make the evaluation and the database attractive enough for future research.
Concerning the impact of the scenario on the video MOS, the AVSCT and BB scenarios were judged more severely than the SCT scenario in the case of strong video degradations (Lv1) with a difference of 0.47 MOS (computed over all audio conditions).
Due to the importance of audio segmentation and the need to develop robust systems capable of operating over a rich variety of audio conditions, the Albayzín-2014 Albayzín-2014proposed as an international evaluation to measure the performancampaigngmentation systems for different databases and different contexts.
At the conclusion of each audio condition, participants were asked to complete a cued recall task.
Employing a repeated measures design with two independent variables (hearing condition and audio condition) and one between groups independent variable (native language), the results revealed the beneficial effects on noise cancelling headphones on performance.
A paired samples t-test showed there was no significant difference between the proportion happy responses in the happy audio condition and the proportion sad responses in the sad audio condition (t(14) = .168, p < .869), indicating both expressions are equally well recognized.
In addition, Zwicker's loudness (N) was highly correlated with the annoyance from high-speed train noise in both the audio-only and audio-visual conditions.
The video scores displayed in Figure 1 were thus computed over all audio quality conditions.
