This paper presents a self-supervised learning (SSL) approach for estimating the angular offset of freehand ultrasound (US) probe movement relative to an ideal viewing angle. Proper US probe angle is essential for acquiring high-quality images for diagnostics. This can be challenging, however, due to the high variability in anatomy and the subjectivity in interpretation. The ability to guide a user to an optimal probe angle can greatly improve the quality of image acquired which in turn will facilitate an accurate assessment. Probe angle is particularly important for musculoskeletal US since the probe often needs to be perpendicularly aligned with the target anatomy (e.g., bone, tendon, or ligament). In this paper, we used plantar fascia (PF) as the use case to develop a probe angle guidance pipeline. It has an immediate application for PF thickness measurement, which is used for assessing plantar fasciitis. In this SSL pipeline, circularized versions of over 3,600 US images of PF were each rotated by a varied amount, and an encoder-support vector machine pair was trained to predict this known angle of rotation. In a cross-validation from 13 subjects, the model predicted rotation angle with an average error of 2.98 degrees (SD: 2.62), and the SSL-trained model accurately predicted the relative rotation angles of images acquired while rotating the US probe. The key contribution of this work is the SSL approach on synthetically generated image and probe angle pairs to emulate angular motion relative to a standard view. This approach for estimating angular offset without labelled data has the potential to be applied to future work guiding users to an optimal view for acquiring US images.

We consider the task of dimensional emotion recognition on video data using deep learning. While several previous methods have shown the benefits of training temporal neural network models such as recurrent neural networks (RNNs) on hand-crafted features, few works have considered combining convolutional neural networks (CNNs) with RNNs. In this work, we present a system that performs emotion recognition on video data using both CNNs and RNNs, and we also analyze how much each neural network component contributes to the system's overall performance. We present our findings on videos from the Audio/Visual+Emotion Challenge (AV+EC2015). In our experiments, we analyze the effects of several hyperparameters on overall performance while also achieving superior performance to the baseline and other competing methods.

We present two approaches to noise robust very low bit rate speech coding using wideband MELP analysis/synthesis. Both methods exploit multiple acoustic and non-acoustic input sensors, using our previously-presented dynamic waveform fusion algorithm to simultaneously perform waveform fusion, noise suppression, and cross-channel noise cancellation. One coder uses a 600 bps scalable phonetic vocoder, with a phonetic speech recognizer followed by joint predictive vector quantization of the error in wideband MELP parameters. The second coder operates at 300 bps with fixed 80 ms segments, using novel variable-rate multistage matrix quantization techniques. Formal test results show that both coders achieve equivalent intelligibility to the 2.4 kbps NATO standard MELPe coder in harsh acoustic noise environments, at much lower bit rates, with only modest quality loss.

A multimodal person recognition architecture has been developed for the purpose of improving overall recognition performance and for addressing channel-specific performance shortfalls. This multimodal architecture includes the fusion of a face recognition system with the MIT/LL GMM/UBM speaker recognition architecture. This architecture exploits the complementary and redundant nature of the face and speech modalities. The resulting multimodal architecture has been evaluated on the XM2VTS corpus using the Lausanne open set verification protocols, and demonstrates excellent recognition performance. The multimodal architecture also exhibits strong recognition performance gains over the performance of the individual modalities.

Speech communication is significantly more difficult in severe acoustic background noise environments, especially when low-rate speech coders are used. Non-acoustic sensors, such as radar sensors, vibrometers, and bone-conduction microphones, offer significant potential in these situations. We extend previous work on fixed waveform fusion from multiple sensors to an optimal dynamic waveform fusion algorithm that minimizes both additive noise and signal distortion in the estimated speech signal. We show that a minimum mean squared error (MMSE) waveform matching criterion results in a generalized multichannel Wiener filter, and that this filter will simultaneously perform waveform fusion, noise suppression, and crosschannel noise cancellation. Formal intelligibility and quality testing demonstrate significant improvement from this approach.

The intelligibility of speech transmitted through low-rate coders is severely degraded when high levels of acoustic noise are present in the acoustic environment. Recent advances in nonacoustic sensors, including microwave radar, skin vibration, and bone conduction sensors, provide the exciting possibility of both glottal excitation and, more generally, vocal tract measurements that are relatively immune to acoustic disturbances and can supplement the acoustic speech waveform. We are currently investigating methods of combining the output of these sensors for use in low-rate encoding according to their capability in representing specific speech characteristics in different frequency bands. Nonacoustic sensors have the ability to reveal certain speech attributes lost in the noisy acoustic signal; for example, low-energy consonant voice bars, nasality, and glottalized excitation. By fusing nonacoustic low-frequency and pitch content with acoustic-microphone content, we have achieved significant intelligibility performance gains using the DRT across a variety of environments over the government standard 2400-bps MELPe coder. By fusing quantized high-band 4-to-8-kHz speech, requiring only an additional 116 bps, we obtain further DRT performance gains by exploiting the ear's insensitivity to fine spectral detail in this frequency region.

Estimating and evaluating confidence has become a key aspect of the speaker recognition problem because of the increased use of this technology in forensic applications. We discuss evaluation measures for speaker recognition and some of their properties. We then propose a framework for confidence estimation based upon scores and meta-information, such as utterance duration, channel type, and SNR. The framework uses regression techniques with multilayer perceptrons to estimate confidence with a data-driven methodology. As an application, we show the use of the framework in a speaker comparison task drawn from the NIST 2000 evaluation. A relative comparison of different types of meta-information is given. We demonstrate that the new framework can give substantial improvements over standard distribution methods of estimating confidence.

The estimation of speech parameters and the intelligibility of speech transmitted through low-rate coders, such as MELP (mixed excitation linear prediction), are severely degraded when there are high levels of acoustic noise in the speaking environment. The application of nonacoustic and nontraditional sensors, which are less sensitive to acoustic noise than the standard microphone, is being investigated as a means to address this problem. Sensors being investigated include the general electromagnetic motion sensor (GEMS) and the physiological microphone (P-mic). As an initial effort in this direction, a multisensor MELPe coder (MELP coder with the addition of a noise preprocessor) using parameter substitution has been developed, where pitch and voicing parameters are obtained from GEMS and P-Mic sensors, respectively, and the remaining parameters are obtained as usual from a standard acoustic microphone. This parameter substitution technique is shown to produce significant and promising DRT (diagnostic rhyme test) intelligibility improvements over the standard 2400 bps MELPe coder in several high-noise military environments. Further work is in progress aimed at utilizing the nontraditional sensors for additional intelligibility improvements and for more effective lower-rate coding in noise.

Various psycho-acoustical experiments have concluded that visual features strongly affect the perception of speech. This contribution is most pronounced in noisy environments where the intelligibility of audio-only speech is quickly degraded. The paper explores the effectiveness of using extracted visual features, such as lip height and width, for improving speech intelligibility in noisy environments. The intelligibility content of these extracted visual features is investigated through an intelligibility test on an animated rendition of the video generated from the extracted visual features, as well as on the original video. These experiments demonstrate that the extracted video features do contain important aspects of intelligibility that may be utilized in augmenting speech enhancement and coding applications. Alternatively, these extracted visual features can be transmitted in a bandwidth effective way to augment speech coders.