Multiple Manifold Clustering Using Curvature Constrained Path.

The problem of multiple surface clustering is a challenging task, particularly when the surfaces intersect. Available methods such as Isomap fail to capture the true shape of the surface near by the intersection and result in incorrect clustering. The Isomap algorithm uses shortest path between points. The main draw back of the shortest path algorithm is due to the lack of curvature constrained where causes to have a path between points on different surfaces. In this paper we tackle this problem by imposing a curvature constraint to the shortest path algorithm used in Isomap. The algorithm chooses several landmark nodes at random and then checks whether there is a curvature constrained path between each landmark node and every other node in the neighborhood graph. We build a binary feature vector for each point where each entry represents the connectivity of that point to a particular landmark. Then the binary feature vectors could be used as a input of conventional clustering algorithm such as hierarchical clustering. We apply our method to simulated and some real datasets and show, it performs comparably to the best methods such as K-manifold and spectral multi-manifold clustering.

Fully Automated Assessment of the Severity of Parkinson's Disease from Speech.

For several decades now, there has been sporadic interest in automatically characterizing the speech impairment due to Parkinson's disease (PD). Most early studies were confined to quantifying a few speech features that were easy to compute. More recent studies have adopted a machine learning approach where a large number of potential features are extracted and the models are learned automatically from the data. In the same vein, here we characterize the disease using a relatively large cohort of 168 subjects, collected from multiple (three) clinics. We elicited speech using three tasks - the sustained phonation task, the diadochokinetic task and a reading task, all within a time budget of 4 minutes, prompted by a portable device. From these recordings, we extracted 1582 features for each subject using openSMILE, a standard feature extraction tool. We compared the effectiveness of three strategies for learning a regularized regression and find that ridge regression performs better than lasso and support vector regression for our task. We refine the feature extraction to capture pitch-related cues, including jitter and shimmer, more accurately using a time-varying harmonic model of speech. Our results show that the severity of the disease can be inferred from speech with a mean absolute error of about 5.5, explaining 61% of the variance and consistently well-above chance across all clinics. Of the three speech elicitation tasks, we find that the reading task is significantly better at capturing cues than diadochokinetic or sustained phonation task. In all, we have demonstrated that the data collection and inference can be fully automated, and the results show that speech-based assessment has promising practical application in PD. The techniques reported here are more widely applicable to other paralinguistic tasks in clinical domain.

Detecting vocalizations of individual monkeys in social groups.

Vocalization is an important clue in recognizing monkeys' behaviors. Previous studies have shown that the frequencies, the types and the lengths of vocalizations reveal significant information about social interactions in a group of monkeys. In this work, we describe a corpus of monkey vocalizations, recorded from Oregon National Primate Research Center with the goal of developing automatic methods for recognizing social behaviors of individuals in groups. The constraints of the problem necessitated using tiny low-power recorders, mounted on their collars. The recordings from each monkeys' recorder nonetheless contains vocalizations from not only the monkey wearing the recorder but also its spatial neighbors. The devices recorded vocalizations for two consecutive days, 12 hours each day, from each monkey in the group. Like in sensor networks, low power recorders are unreliable and have sample loss over long durations. Furthermore, the recordings contain high-levels of background noise, including clanging of metal collars against cages and conversations of caretakers. These practical issues poses an interesting challenge in processing the recordings. In this paper, we investigate our automated approaches to process the data efficiently, detect the vocalizations and align the recordings from the same sessions.

INFERRING SOCIAL CONTEXTS FROM AUDIO RECORDINGS USING DEEP NEURAL NETWORKS.

In this paper, we investigate the problem of detecting social contexts from the audio recordings of everyday life such as in life-logs. Unlike the standard corpora of telephone speech or broadcast news, these recordings have a wide variety of background noise. By nature, in such applications, it is difficult to collect and label all the representative noise for learning models in a fully supervised manner. The amount of labeled data that can be expected is relatively small compared to the available recordings. This lends itself naturally to unsupervised feature extraction using sparse auto-encoders, followed by supervised learning of a classifier for social contexts. We investigate different strategies for training these models and report results on a real-world application.

Robust and Accurate Features for Detecting and Diagnosing Autism Spectrum Disorders.

In this paper, we report experiments on the Interspeech 2013 Autism Challenge, which comprises of two subtasks - detecting children with ASD and classifying them into four subtypes. We apply our recently developed algorithm to extract speech features that overcomes certain weaknesses of other currently available algorithms [1, 2]. From the input speech signal, we estimate the parameters of a harmonic model of the voiced speech for each frame including the fundamental frequency (f 0). From the fundamental frequencies and the reconstructed noise-free signal, we compute other derived features such as Harmonic-to-Noise Ratio (HNR), shimmer, and jitter. In previous work, we found that these features detect voiced segments and speech more accurately than other algorithms and that they are useful in rating the severity of a subject's Parkinson's disease [3]. Here, we employ these features, along with standard features such as energy, cepstral, and spectral features. With these features, we detect ASD using a regression and identify the sub-type using a classifier. We find that our features improve the performance, measured in terms of unweighted average recall (UAR), of detecting autism spectrum disorder by 2.3% and classifying the disorder into four categories by 2.8% over the baseline results.

ROBUST DETECTION OF VOICED SEGMENTS IN SAMPLES OF EVERYDAY CONVERSATIONS USING UNSUPERVISED HMMS.

We investigate methods for detecting voiced segments in everyday conversations from ambient recordings. Such recordings contain high diversity of background noise, making it difficult or infeasible to collect representative labelled samples for estimating noise-specific HMM models. The popular utility get-f0 and its derivatives compute normalized cross-correlation for detecting voiced segments, which unfortunately is sensitive to different types of noise. Exploiting the fact that voiced speech is not just periodic but also rich in harmonic, we model voiced segments by adopting harmonic models, which have recently gained considerable attention. In previous work, the parameters of the model were estimated independently for each frame using maximum likelihood criterion. However, since the distribution of harmonic coefficients depend on articulators of speakers, we estimate the model parameters more robustly using a maximum a posteriori criterion. We use the likelihood of voicing, computed from the harmonic model, as an observation probability of an HMM and detect speech using this unsupervised HMM. The one caveat of the harmonic model is that they fail to distinguish speech from other stationary harmonic noise. We rectify this weakness by taking advantage of the non-stationary property of speech. We evaluate our models empirically on a task of detecting speech on a large corpora of everyday speech and demonstrate that these models perform significantly better than standard voice detection algorithm employed in popular tools.

Interspeech Pathology Challenge: Investigations into Speaker and Sentence Specific Effects.

In this paper, we report our experiments on Interspeech 2012 Speaker Trait Pathology challenge task [2]. Specifically, we investigate two factors that impact the acoustic properties of the utterances collected in this task. Although the task treats utterances as independent data points, multiple utterances are recorded from individual speakers. Furthermore, the utterances correspond to readings of 17 given written sentences. In one experiment, we attempt to reduce variation due to speaker through dimensionality reduction. While these experiments showed promising results on development set, the performance did not translate to the evaluation test. In another, we learn classifiers conditioned on the sentences to capture sentence-specific signatures. This approach showed improved performance over the baseline on development set and the improvement translated to marginal gains on evaluation set. These experiments demonstrates the need to pay attention to the independence assumptions while collecting and defining clinical tasks.