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In this paper, we study the role of long-time analytic phase of speech
signals in spoken language recognition (SLR) and employ a set
of features termed as instantaneous frequency cepstral coefficients
(IFCC). We extract IFCC from long-time analytic phase, in an effort
to capture long range acoustic features from speech signals. These
features are used in combination with the traditional shifted delta
cepstral coefficients (SDCC) for SLR. As the SDCC are extracted
from spectral magnitude and IFCC are from analytic phase, they

Predicting continuous emotion in terms of affective attrib-utes has mainly been focused on hard labels, which ignored the ambiguity of recognizing certain emotions. This ambigu-ity may result in high inter-rater variability and in turn caus-es varying prediction uncertainty with time. Based on the assumption that temporal dependencies occur in the evolu-tion of emotion uncertainty, this paper proposes a dynamic multi-rater Gaussian Mixture Regression (GMR), aiming to obtain the emotion uncertainty prediction reflected by multi-raters by taking into account their temporal dependencies.

Aphasia is a type of acquired language impairment resulting from brain injury. Speech assessment is an important part of the comprehensive assessment process for aphasic patients. It is based on the acoustical and linguistic analysis of patients’ speech elicited through pre-defined story-telling tasks. This type of narrative spontaneous speech embodies multi-fold atypical characteristics related to the underlying language impairment.

Bidirectional long short term memory (BLSTM) recurrent neural networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and deep neural networks (DNNs) in automatic language identification (LID), particularly when testing with very short utterances (∼3s). Mismatches conditions between training and test data, e.g. speaker, channel, duration and environmental noise, are a major source of performance degradation for LID.