Figure 6 Spectrogram of the microphone input (mic. 1 at car speed of 140 km/h, short speaker).
The first value is the output SNR for a short speaker while the second number represents the result for the tall speaker.
The first values in each field are with respect to a short speaker while the second ones are according to a tall person.
The spectrogram of one input signal and the result of the voice activity detection are shown in Figure 6 for the worst case scenario (short speaker at car speed of 140 km/h).
Good generalization properties of this classifier enahle to achieve low error rates even for short training speaker utterances.
Death rates are notably higher in eastern Finland than in western Finland, and life expectancy of Finnish speakers shorter than that of Swedish speakers.
In short, the speakers are about as clean as you can expect from their kind.
In[4], Conkie et al. used short-frame speaker-normalized pitch and energy as acoustic representations modeled by prosodic context-dependent HMMs.
Two of the most distinctive features of overall mortality in Finland are that death rates increase in the southwest to northeast direction and that life expectancy of Finnish speakers is shorter than that of Swedish speakers [ 1].
It's only when something fails — a microphone shorts out, a speaker whines with feedback — that audiences are guaranteed to notice.
