## 4 Evaluation  
### Experimental Setup  
Two sensors were used to detect the ultrasonic Doppler shifts. One was attached to the wire frame of the headset microphone, and was used to detect Doppler shifts in the mouth and cheek area. The other was placed at the front of the throat for detection of Doppler sonar in the jaw and the neck area. Each sensor was composed of an ultrasonic transmitter emitting a continuous ultrasonic tone at 40 kHz and a wide band receiver that acquired signals that ranged from 10 to 65 kHz. An audio microphone was also used to simultaneously record audio-frequency range signals. The effective radiation area was 19.9 cm\({}^{2}\), which was experimentally computed. Such an area proved to be sufficient to detect the subject's articulatory movements. A photograph of the developed sensor mounted on a subject appears shown in Figure 4.  
Five subjects participated in the recording, one female speaker and four male speakers whose ages ranged from 22 to 50. All subjects had no known articulatory problems. Newspaper articles were used as speaking materials. The properties of the dataset are summarized in Table 3. The features were extracted from the windowed speech and from the windowed UDS. A 32 ms length for the Hanning window is commonly used to compute and extract the feature parameters at 16 ms intervals. The log-magnitude of the Fourier transform (FT) coefficients was used, and the length of the fast Fourier transform (FFT) was set at 256. Accordingly, the dimension of the log-power spectral feature vector was 128. The same FFT length was adopted to compute the UDS feature. Although the frequency resolution of the speech signal seemed relatively lower than other speech-related applications such as ASR, TTS, and speech coding, the experimental result showed that the performance of the DNN-based estimation was not improved with an increase in the length of the FFT. The DNN was trained using 60% of the total features in the training stage, and the remaining features were used for validation (20%) and evaluation (20%). According to the results obtained by the validation dataset, the best performance was obtained when the DNN contained three hidden layers, and the number of the nodes in each hidden layer was set to \([1.5\times N_{i}]\). (where \([x]\) was the nearest integer value of \(x\)) \(N_{i}\) was the number of input nodes, which was 160 (=16 features/channel \(\times\) 2 channel \(\times\) 5 frames). With the exception of the top layer, the sigmoid activation function was adopted. The momentum constant, \(\alpha\), of the sigmoid active function was set to 0.7. A linear function was used in the top layer. The performance of the DNN was expected to improve with dropout regularization [44]. The experimental results also showed clear differences between when a dropout was adopted and when it was not, particularly for the test data. Hence, dropout regularization was adopted in the present study where a keep probability of 0.75 was employed. Three objective measures were applied in the experiments, a Perceptual evaluation of the speech quality (PESQ) [37], the log-spectral distortion (LSD, in dB), and a short-time objective intelligibility measure (STOI) [45]. PESQ was calculated by comparing the reproduced speech with a sample of original reference speech, and it ranged from \(-0.5\) to 4.5. The LSD of a speech signal is defined as  
\[d_{LSD}=\frac{1}{N}\sum_{n=0}^{N-1}\sqrt{\frac{1}{P}\sum_{p=0}^{P-1}\left[10 \cdot\log_{10}\frac{|\hat{Y}_{p,n}|^{2}}{|Y_{p,n}|^{2}}\right]^{2}} \tag{21}\]  
where \(Y_{p,n}\) and \(\hat{Y}_{p,n}\) are the short-time Fourier transform (STFT) of the original speech and the reproduced speech, respectively, \(p\) is the frequency bin, \(n\) is an index of the time frame and \(N\) is the set of frames with speech presence. The STOI [45] was proposed as a correlation-based method to evaluate the speech intelligibility degradation caused by  
Figure 4: Photograph of the ultrasonic sensors. (**Left**): Mounted on the right cheek. (**Right**): Magnified sensor portion.