Search

Your search keyword '"Kacic, Z."' showing total 32 results
Start Over Author "Kacic, Z."
32 results on '"Kacic, Z."'

3. A study of x-vector based speaker recognition on short utterances

Author

Kacic, Z, Kanagasundaram, Ahilan, Sridharan, Sridha, Ganapathy, Sriram, Singh, Prachi, Fookes, Clinton, Kacic, Z, Kanagasundaram, Ahilan, Sridharan, Sridha, Ganapathy, Sriram, Singh, Prachi, and Fookes, Clinton

Abstract

The aim of this work is to gain insights into how the deep neural network (DNN) models should be trained for short utterance evaluation conditions in an x-vector based speaker verification system. The study suggests that the speaker embedding can be extracted with reduced dimensions for short utterance evaluation conditions. When the speaker embedding is extracted from deeper layer which has lower dimension, the x-vector system achieves 14% relative improvement over baseline approach on EER on NIST2010 5sec-5sec truncated conditions. We surmise that since short utterances have less phonetic information speaker discriminative x-vectors can be extracted from a deeper layer of the DNN which captures less phonetic information. Another interesting finding is that the x-vector system achieves 5% relative improvement on NIST2010 5sec-5sec evaluation condition when the back-end PLDA is trained using short utterance development data. The results confirms the intuitive expectation that duration of development utterances and the duration of evaluation utterances should be matched. Finally, for the duration mismatch condition, we propose a variance normalization approach for PLDA training that provides a 4% relative improvement on EER over baseline approach.

Published
2019

32. A rule-based emotion-dependent feature extraction method for emotion analysis from speech.

Author

Hozjan V and Kacic Z

Subjects
Adult, Female, Humans, Male, Principal Component Analysis, Psychoacoustics, Speech Acoustics, Speech Intelligibility, Speech Production Measurement, Emotions, Speech Perception, Verbal Behavior
Abstract

This paper presents a rule-based method to determine emotion-dependent features, which are defined from high-level features derived from the statistical measurements of prosodic parameters of speech. Emotion-dependent features are selected from high-level features using extraction rules. The ratio of emotional expression similarity between two speakers is defined by calculating the number and values of the emotion-dependent features that are present for the two speakers being compared. Emotional speech from Interface databases is used for evaluation of the proposed method, which was used to analyze emotional speech from five male and four female speakers in order to find any similarities and differences among individual speakers. The speakers are actors that have interpreted six emotions in four different languages. The results show that all the speakers share some universal signs regarding certain emotion-dependent features of emotional expression. Further analysis revealed that almost all speakers in the analysis used unique sets of emotion-dependent features and each speaker used unique values for the defined emotion-dependent features. The comparison among speakers shows that the expressed emotions can be analyzed according to two criteria. The first criterion is a defined set of emotion-dependent features and the second is an emotion-dependent feature value.

Published
2006
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results