The Influence of Video Sampling Rate on Lipreading Performance

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1 The Influence of Video Sampling Rate on Lipreading Performance Alin G. ChiŃu and Leon J.M. Rothkrantz Man-Machine Interaction Group Delft University of Technology Mekelweg 4, 2628CD, Delft, The Netherlands {A.G.Chitu, Abstract That adding visual information improves the recognition of speech is an established fact. However, since the video sampling is done at a much lower rate than speech sampling and the increase in performance, while promising, is still not at the expected level, the question of whether or not is it useful to use high speed recordings makes its appearance. We propose in this paper an analysis of the gain in information from the point of view of lipreading accuracy, based on Root Mean Square Deviation (RMSD) measure. We analyze both real data, recorded using high speed technology, and synthetic data obtained using the well known software tools CUAnimate and CSLU Toolkit. The analysis is performed on two different and highly used types of features the mouth width and height and optical flow. Taking into account the rate of speech our preliminary findings show two different situations. In the case of low speech rate, usually used during spellings or when uttering connected digits (e.g. telephone numbers, account numbers) or uttering separate words, the information gained by using high speed recordings is not significant enough to justify the tremendous amount of resources needed when working with high speed recordings. However, in the case of continuous speech, when the speech rate tends to increase, a recording rate in the range of 24 to 30 frames per second is definitely insufficient. Then interpolation is needed which decreases the recognition performance. 1. Introduction The work in the domain of speech recognition gains more and more importance in our times. Modules that are offering accurate and reliable speech recognition are required for almost any system deployed: from smart control rooms, public kiosks, mobile devices to smart electric devices in our houses. In general, we notice a novel and sustained trend towards more natural ways of communication with our smart devices. With the introduction of Hidden Markov Models (HMM), the performance of speech recognition systems deployed in controlled environments (e.g. offices) has reached very high levels. However, as soon as the environment where the system is deployed differs from the one present when the training database was built, the recognition performance degrades rapidly, due to the acoustic noise. To overcome these problems, we need to add complementary information coming from other channels that are not affected by acoustic noise. The visual information seems to be the most natural source of additional information since human beings use it in their daily lives [1]. The scientific literature gives promising results (e.g. an increase in performance with 10 to 20%) in the case of high noise to signal ratio. However, independently of the method used for extracting visual information, since the video signal is sampled at a frame rate of 25-30fps, while the audio data is sampled usually at 100fps, there is a big gap between the two. To solve this problem the obvious solution is to interpolate the slower sampled signal to match the faster one. The question that arises then, is how much reduction of the available information we induce by performing interpolation? What if we sample the video stream at a much higher sample rate? The advent of digital technology makes it possible to record video data at very high frame rates. However, the costs of the equipment and the resources necessary for high speed recordings are still extremely high. Therefore, before embarking to such an enormous effort we want to know what would be the benefits with respect to speech recognition performances.

2 This paper analyzes the loss of information at the signal level caused by using a low sample rate. The signals are composed of visual features that proved very successful in the literature. We present the results of the analysis in two situations: recordings using high speed camera and simulated speech using an artificial talking head. We also consider the two separate cases: low speech rate and high speech rate. The next section introduces the settings used for data acquisition in both cases of high speed recording and high speed simulation and gives a succinct description of the visual features analyzed. Section 3 presents the analyses together with the preliminary results, while stressing the issues in each case. Our conclusions are summarized in the last section of the paper. 2. Data acquisition We used for our analysis three data sources: our old dataset built using a regular camcorder at 25fps, DUTAVSC [12], new recordings using high speed camera and synthetic data obtained with the CUAnimate [11] software. 2.1 Real Data: High Speed Camera We used for the recordings a FASTCAM-APX RS 250K device. This camera is capable of capturing at a rate of fps grayscale images. To be able to achieve good image quality we limited our recordings to 250fps and 1MP resolution. Also to cope with the large storage needs for the recordings we decided to perform the analysis on very short utterances, namely the digits from 0 to 9. The utterances were produced in Dutch by a native male speaker. 2.2 Synthetic data: CUAnimate and CSLU Toolkit To ease the acquisition of high speed data we used the CUAnimate software in combination with the Center for Spoken Language Understanding (CSLUToolkit). CUAnimate is a set of software tools for researching full-bodied three-dimensional animated characters, and for controlling and rendering them in real time. It was build at the Center for Spoken Language Research, University of Colorado, with the declared goal of enabling animated computer characters to engage in natural face-to-face conversational interaction with users. The software gives the possibility to save the animation at different frame rates, namely 15, 24, 30, 60 and 999. Therefore, we recorded for the present research the VidTIMIT utterance She had your dark suit in greasy wash water all year. using all five different possible target frame rates. During animation we inhibited any other animation, such as random head movement, blinking, emotions, etc. The facial expression was set to neutral. 2.3 Visual features We want to investigate the influence of the frame rate at which the video clip was recorded on the amount of information lost. Hence, we will look at some of those visual features that proved to be most important for lipreading. The techniques developed for extracting visual information are variate: starting with appearance based methods [2, 3], to geometry based methods [4, 5], and vector flow based methods [6, 7, 8, 9, 10]. The mouth height and width and the mouth openness are some of the most used static visual features in the literature [13, 14]. The exact definitions of these features are shown in Figure 1a. We also analyzed the effect of the frame rate when using a different type of features, introduced in paper [10]. These features are computed using optical flow analysis and were reported to provide very good results. Figure 1b shows an instance of these features, however, a better description can be found in the paper.

3 Figure 1. a) Mouth width, heights and openness; b) Optical flow based features. 3. Analysis M 1 The measure used for analysis is defined as: RMSD= ( x xˆ ) M i = 1 i i 2. In each case we compared the feature signal obtained when the maximum available frame rate was used with the signals obtained by down-sampling the original signal and using linear interpolation to recover the signal. In the case of the artificial data we also compared the down-sampled clips with the corresponding recorded clips. For the optical flow based features, where the actual features used are the vertical and horizontal increments, interpolation is not permitted since it would yield erroneous results. In this case the analysis is performed on the cumulative sums of each feature vector. 3.1 Speech rate The first step we considered was to see how much data is recorded for each unit of visual speech, namely each viseme, when using regular frame rate. We analyzed the DUTAVSC dataset searching to approximate the mean number of frames per viseme. The overall result was that each viseme was covered by approximately 6 frames. However, when plotting the result per each utterance we discovered that two situations are emerging as seen in Figure 2. Some utterances are crowded in the range 2 to 5, while a second set is more or less uniformly spread from 5 to 20. Figure 2b and 2c, show in detail the two sets, respectively. We found that in the low speech rate set we have only spellings, phone and account numbers utterances, while in the high speech rate set we have continuous speech utterances. The mean numbers of frames per viseme in the two sets were, when considered separately, 3 frames per viseme for the high speech rate and 11 frames per viseme for the low speech rate. Hence, there is a significant difference between the two cases. Therefore, the analysis has to take into account this aspect. Figure 2. The histogram of the frames per visemes values in the DUTAVSC dataset. a) entire dataset; b) low speech rate utterances; c) high speech rate utterances. 3.2 Results based on the real recordings We will show the results obtained based on the utterances of the digit 0 and 1, recorded using high speed camera. The speech rate was in this case around 10 frames per viseme. Similar results were obtained for the other digits. Figure 3 shows the plots of the mouth width, mouth height and mouth openness is time, respectively for the digit 0.

4 Figure 3. Digit "0" the a) width, b) height, and c) openness in time. We can notice in these images that the interpolated signals are following very closely the finest signal, namely the 250fps signal. However the coarsest one, the 15fps signal, as expected is sometimes somewhat farther. The differences between the true signal and the ones obtained by interpolation are more visible in the case of the width, but even there, the spikes are most probable errors introduced by the detection algorithms used. Table 1 lists the RMSD values obtained in each situation. The units used are image pixels. We can also observe here that the RMSD decreases with respect to the frame rates, which is to be expected, however the very small numbers suggest again very little loss. Table 1. RMSD values for the case of real data recordings. Digit 0 15fps 24fps 30fps 60fps Width Height Openness Digit 1 15fps 24fps 30fps 60fps Width Height Openness The same analysis was performed for the cumulative sum of the optical flow based visual features. Figure 4 shows the plots of the different signals obtained for three of the features in the case of Digit 1. In Figure 4a is shown the feature that has the biggest variance, while the other features were randomly chosen. Figure 4. Optical flow based features. Cumulative sums in time. a) The most informative feature, b) and c) Some randomly chosen features. The same observations can be made in this case. However, we can notice in the case of the image b) that there is a bit bigger difference. Also we can see that the plot of 250fps signal is very irregular, which can send us to the conclusion that the optical flow analyzer has some accuracy problems when dealing with such a fine transformation. Table 2 gives the RMSD for this situation. Table 2. RMSD values for optical flow based features. The features are the ones shown in Figure 4. Digit 1 15fps 24fps 30fps 60fps Feature a) Feature b) Feature c)

5 Figures 5 and 6 show the plots obtained in the case of two recordings from the DUTAVSC dataset. In Figure 5 we have a slow speech rate utterance (i.e. 12 frames per viseme), while in Figure 6 a high speech rate utterance (i.e. 2 frames per viseme). Figure 5. Slow speech rate utterance from DUTAVSC. a) mouth openness; b) mouth width Figure 6. High speech rate utterance from DUTAVSC. a) mouth openness; b) mouth width The plots, in Figures 5 and 6, show for comparison the signals obtained by down-sampling the original signal by 2. The most important observation for the plot above is that there are much more spikes per time unit in the case of high speech rate than in the case of low speech rate. This implies that the sample rate necessary to preserve the signal should be much higher than in the other case. We can see already that by decimating the signal by two already introduces more errors in the case of high rate than the case of low rate speech. 3.2 Results based on the artificial recordings We performed the same analysis on the recordings made using the CUAnimate talking face. The results are given in Table 3. We also present here the RMSD computed using clips with lower frame rate obtained directly from the CUAnimate software. Table 3 Comparison based on CUAnimate data. The feature shown is mouth width. 15fps 24fps 30fps 60fps Comparison between 999fps clip and decimated clips Width Height Openness Comparison between decimated clips and clips obtain directly from CUAnimate Width Height Openness Comparison between 999fps clip and the interpolated data obtain directly from CUAnimate Width Height Openness We can again see a very small difference when the frame rate is lowered. Looking at the results in the central section of the Table 3 we can also note that the morphing algorithm used by CUAnimate is slightly different than linear interpolation.

6 4. Conclusions We introduced in this paper a method to analyze the loss of information due to the low sampling rate of the video information. The analysis was performed on both real and synthetic data. We observed the behavior of some visual features most used for lipreading because we are interested in the loss of information from the point of view of lipreading accuracy. Our preliminary results show that in the case of low speech rate the loss of information is not sufficiently large to justify the effort when working with high speed recordings. However, in the case of high speech rate the danger of losing the real signal is much higher. Thus, in this case it is required to use higher recording rates. This result may be explained by the fact that when speaking slowly our mouth makes more ample and complete movements compared to the case when the speech rate is high. It is also our belief that during high speech rate the shown visemes are poorer and contain less information. Hence, by using higher recording rate we could more efficiently capture that information. However, we may need to develop better error measures, and look at larger set of features, and more advanced features before taking a definitive decision. At this moment we can definitely argue that while 15fps is a too lower target, 25fps is too low for high speech rate, more than 250fps would be also a superfluous target. The visual features used for this research were carefully computed such that to include very little noise. However, in a real system the accuracy of the segmentation is far from perfect. The noise introduced in this way will generate additional concerns with respect to the recording rate. References [1] H. McGurk, J. MacDonald, Hearing lips and seeing voices, Nature, v. 264, pp , [2] N. Li, S. Dettmer, M. Shah, Lipreading using eigen sequences, Proc. Int. Conf. on Automatic Face- and Gesture-Recognition, (Zurich, Switzerland), pp.30 34, [3] , Visually recognizing speech using eigen sequences, Motion-based recognition, [4] L.J.M. Rothkrantz, J.C. Wojdeł, P. Wiggers, Comparison between different feature extraction techniques in lipreading applications, Specom 2006, SpIIRAS Petersburg, [5] I. A. Essa, A. Pentland, A Vision System for Observing and Extracting Facial Action Parameters, Proc. of IEEE Conf. on CVPR, pp , IEEE, June [6] K. Mase, A. Pentland., Automatic Lipreading by Optical-Flow Analysis, Systems and Computers in Japan, vol. 22, pp , [7] K. Iwano, S. Tamura, S. Furui, Bimodal Speech Recognition Using Lip Movement Measured by Optical-Flow analysis, in HSC2001, [8] D. J. Fleet, M. J. Black, Y. Yacoob, A. D. Jepson, Design and Use of Linear Models for Image Motion Analysis, Int. Journal of Computer Vision, vol. 36, no. 3, pp , [9] A. Martin, Lipreading by Optical Flow Correlation, tech. rep., Compute Science Department University of Central Florida, [10] A.G. ChiŃu, L.J.M. Rothkrantz, J.C. Wojdeł, P. Wiggers, "Comparison Between Different Feature Extraction Techniques for Audio-Visual Speech Recognition", JMUI, pp. 7-20, Springer, [11] J. Ma, J. Yan, R. Cole, CUAnimate Tools for Enabling Conversations with Animated Characters [12] J.C. Wojdeł, P. Wiggers, L.J.M. Rothkrantz, An audio-visual corpus for multimodal speech recognition in Dutch language, ICSLP2002, Denver, USA, September, pp , [13] M. Chan Hmm-based audio-visual speech recognition integrating geometric and appearancebased visual features IEEE 4 th Workshop on Multimedia Signal Processing, France, pp. 9-14, [14] X. Zhang, R.M. Mersereanu & M.A. Clements, Audio-visual speech recognition by speechreading, 14 th Int. Conf on Digital Signal Processing, Aegean Island of Thera, Greece, vol.2, pp , July 2002.

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