A uniform phase representation for the harmonic model in speech synthesis applications

Feature-based vocoders, e.g., STRAIGHT, offer a way to manipulate the perceived characteristics of the speech signal in speech transformation and synthesis. For the harmonic model, which provide excellent perceived quality, features for the amplitude parameters already exist (e.g., Line Spectral Frequencies (LSF), Mel-Frequency Cepstral Coefficients (MFCC)). However, because of the wrapping of the phase parameters, phase features are more difficult to design. To randomize the phase of the harmonic model during synthesis, a voicing feature is commonly used, which distinguishes voiced and unvoiced segments. However, voice production allows smooth transitions between voiced/unvoiced states which makes voicing segmentation sometimes tricky to estimate. In this article, two-phase features are suggested to represent the phase of the harmonic model in a uniform way, without voicing decision. The synthesis quality of the resulting vocoder has been evaluated, using subjective listening tests, in the context of resynthesis, pitch scaling, and Hidden Markov Model (HMM)-based synthesis. The experiments show that the suggested signal model is comparable to STRAIGHT or even better in some scenarios. They also reveal some limitations of the harmonic framework itself in the case of high fundamental frequencies.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

Bipolar disorder comorbid with alcohol use disorder: Focus on neurocognitive correlates

Bipolar disorder (BD) and alcohol use disorders (AUDs) are usually comorbid, and both have been associated with significant neurocognitive impairment. Patients with the BD-AUD comorbidity (dual diagnosis) may have more severe neurocognitive deficits than those with a single diagnosis, but there is paucity of research in this area. To explore this hypothesis more thoroughly, we carried out a systematic literature review through January 2015. Eight studies have examined the effect of AUDs on the neurocognitive functioning of BD patients. Most studies found that BD patients with current or past history of comorbid AUDs show more severe impairments, especially in verbal memory and executive cognition, than their non-dual counterparts. Greater neurocognitive dysfunction is another facet of this severe comorbid presentation. Implications for clinical practice and research are discussed. Specifically, the application of holistic approaches, such as clinical staging and systems biology, may open new avenues of discoveries related to the BD-AUD comorbidity.