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Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.

Perceptual audio coding is heavily and successfully applied for audio compression. However, perceptual audio coders may inject audible coding artifacts when encoding audio at low bitrates. Low-bitrate audio restoration is a challenging problem, which tries to recover a high-quality audio sample close to the uncompressed original from a low-quality encoded version. In this paper, we propose a novel data-driven method for audio restoration, where temporal and spectral dynamics are explicitly captured by a deep time-frequency-LSTM recurrent neural networks. Leveraging the captured temporal and spectral information can facilitate the task of learning a nonlinear mapping from the magnitude spectrogram of low-quality audio to that of high-quality audio. The proposed method substantially attenuates audible artifacts caused by codecs and is conceptually straightforward. Extensive experiments were carried out and the experimental results show that for low-bitrate audio at 96 kbps (mono), 64 kbps (mono), and 96 kbps (stereo), the proposed method can efficiently generate improved-quality audio that is competitive or even superior in perceptual quality to the audio produced by other state-of-the-art deep neural network methods and the LAME-MP3 codec.

Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology \"out of the lab\" to real-world, diverse data. In this contribution, we address the problem of finding \"disturbing\" scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP) on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis.

Identification of fatigue in people with Multiple Sclerosis (pwMS) is still mainly based on subjective assessments due to the lack of objective diagnostic tools. We aimed to identify vocal biomarkers to differentiate between pwMS with and without fatigue. This COMMITMENT trial was a prospective, observational study recruiting healthy controls (HCs,  = 20) and relapsing MS (RMS) patients (  = 50, EDSS <4.0) at University Hospital of Bern. The primary objective was to predict fatigue in pwMS by means of automated artificial intelligence (AI)-based speech analysis. An exploratory objective was to differentiate between pwMS and HCs. Participants had a mean age of 36.0 years, and 73% were female, with no significant group differences. Median EDSS in pwMS was 1.0 (range 0-3.0). Motor fatigue affected 50% and cognitive fatigue 40% of pwMS. Five acoustic features were associated with general fatigue, independently of depression and sleepiness. Further, five features were associated with motor-, and 12 with cognitive fatigue. The best-performing classification and regression models [Leave-One-Speaker-Out (LOSO) paradigm] achieved specificities of 0.68-0.94, whereas sensitivity remained lower (0.38-0.90). Speech biomarkers distinguished pwMS from HCs with a specificity of 0.90 but only a sensitivity of 0.3. Speech in pwMS may serve as a potential biomarker for MS-associated fatigue and might help to differentiate between pwMS and HC. Our findings suggest that AI-assisted speech analysis could complement existing fatigue assessments.

For many applications of emotion recognition, such as virtual agents, the system must select responses while the user is speaking. This requires reliable on-line recognition of the user’s affect. However most emotion recognition systems are based on turnwise processing. We present a novel approach to on-line emotion recognition from speech using Long Short-Term Memory Recurrent Neural Networks. Emotion is recognised frame-wise in a two-dimensional valence-activation continuum. In contrast to current state-of-the-art approaches, recognition is performed on low-level signal frames, similar to those used for speech recognition. No statistical functionals are applied to low-level feature contours. Framing at a higher level is therefore unnecessary and regression outputs can be produced in real-time for every low-level input frame. We also investigate the benefits of including linguistic features on the signal frame level obtained by a keyword spotter.

Besides reduction of energy consumption, which implies alternate actuation and light construction, the main research domain in automobile development in the near future is dominated by driver assistance and natural driver-car communication. The ability of a car to understand natural speech and provide a human-like driver assistance system can be expected to be a factor decisive for market success on par with automatic driving systems. Emotional factors and affective states are thereby crucial for enhanced safety and comfort. This paper gives an extensive literature overview on work related to influence of emotions on driving safety and comfort, automatic recognition, control of emotions, and improvement of in-car interfaces by affect sensitive technology. Various use-case scenarios are outlined as possible applications for emotion-oriented technology in the vehicle. The possible acceptance of such future technology by drivers is assessed in a Wizard-Of-Oz user study, and feasibility of automatically recognising various driver states is demonstrated by an example system for monitoring driver attentiveness. Thereby an accuracy of 91.3% is reported for classifying in real-time whether the driver is attentive or distracted.

Rhythmic information plays an important role in Music Information Retrieval. Example applications include automatically annotating large databases by genre, meter, ballroom dance style or tempo, fully automated D.J.-ing, and audio segmentation for further retrieval tasks such as automatic chord labeling. In this article, we therefore provide an introductory overview over basic and current principles of tempo detection. Subsequently, we show how to improve on these by inclusion of ballroom dance style recognition. We introduce a feature set of 82 rhythmic features for rhythm analysis on real audio. With this set, data-driven identification of the meter and ballroom dance style, employing support vector machines, is carried out in a first step. Next, this information is used to more robustly detect tempo. We evaluate the suggested method on a large public database containing 1.8 k titles of standard and Latin ballroom dance music. Following extensive test runs, a clear boost in performance can be reported.

We investigate the automatic recognition of emotions in the singing voice and study the worth and role of a variety of relevant acoustic parameters. The data set contains phrases and vocalises sung by eight renowned professional opera singers in ten different emotions and a neutral state. The states are mapped to ternary arousal and valence labels. We propose a small set of relevant acoustic features basing on our previous findings on the same data and compare it with a large-scale state-of-the-art feature set for paralinguistics recognition, the baseline feature set of the Interspeech 2013 Computational Paralinguistics ChallengE (ComParE). A feature importance analysis with respect to classification accuracy and correlation of features with the targets is provided in the paper. Results show that the classification performance with both feature sets is similar for arousal, while the ComParE set is superior for valence. Intra singer feature ranking criteria further improve the classification accuracy in a leave-one-singer-out cross validation significantly.

Robustly detecting keywords in human speech is an important precondition for cognitive systems, which aim at intelligently interacting with users. Conventional techniques for keyword spotting usually show good performance when evaluated on well articulated read speech. However, modeling natural, spontaneous, and emotionally colored speech is challenging for today’s speech recognition systems and thus requires novel approaches with enhanced robustness. In this article, we propose a new architecture for vocabulary independent keyword detection as needed for cognitive virtual agents such as the SEMAINE system. Our word spotting model is composed of a Dynamic Bayesian Network (DBN) and a bidirectional Long Short-Term Memory (BLSTM) recurrent neural net. The BLSTM network uses a self-learned amount of contextual information to provide a discrete phoneme prediction feature for the DBN, which is able to distinguish between keywords and arbitrary speech. We evaluate our Tandem BLSTM-DBN technique on both read speech and spontaneous emotional speech and show that our method significantly outperforms conventional Hidden Markov Model-based approaches for both application scenarios.

This paper proposes a real-time person activity detection framework operating in presence of multiple sources in reverberated environments. Such a framework is composed by two main parts: The speech enhancement front-end and the activity detector. The aim of the former is to automatically reduce the distortions introduced by room reverberation in the available distant speech signals and thus to achieve a significant improvement of speech quality for each speaker. The overall front-end is composed by three cooperating blocks, each one fulfilling a specific task: Speaker diarization, room impulse responses identification, and speech dereverberation. In particular, the speaker diarization algorithm is essential to pilot the operations performed in the other two stages in accordance with speakers’ activity in the room. The activity estimation algorithm is based on bidirectional Long Short-Term Memory networks which allow for context-sensitive activity classification from audio feature functionals extracted via the real-time speech feature extraction toolkit openSMILE. Extensive computer simulations have been performed by using a subset of the AMI database for activity evaluation in meetings: Obtained results confirm the effectiveness of the approach.