Speaker-dependent model interpolation for statistical emotional speech synthesis

Hsu, Chih‐Yu; Chen, Chia-Ping

doi:10.1186/1687-4722-2012-21

Cited by 4 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The underlying methodology of previous studies is a lexicon-based method by which a lexicon is used to detect emotions in text based on speech recognition (Pajupuu et al 2012;Kato et al 2006). Using a single modal of speech recognition, the correct emotion recognition reaches 73% for happiness, 60% for angry, 55% for sadness, and the overall accuracy is 62% for all emotions (Hsu and Chen 2012). However, bimodal emotion recognition can reach an accuracy of 86.85%, an increase of 5% compared with using a single modal of emotion recognition (Song et al 2015;Chuang and Wu 2004;Kessous et al 2010).…”

Section: Methodsmentioning

confidence: 99%

Development and evaluation of an emotional lexicon system for young children

Wei

2019

Microsyst Technol

View full text Add to dashboard Cite

Traditionally, children's emotion has been assessed by teachers according to observation. We should be able to detect children's emotions using algorithmic techniques. To achieve this goal, it is necessary to develop and evaluate an emotional lexicon based on the standardized test entitled Emotional Competencies Scale for Young Children (ECSYC). The purpose of this study was to establish the criterion-related validity. The methodology of this study was to firstly develop 40 scenarios based on ECSYC. Secondly, we developed the five-level criteria. Thirdly, this study implemented observer training and calculated inter-rater consistency reliability. Fourthly, observers categorized 200 children's replies into five levels. Fifthly, this study ranked the sequence of frequency of each level and completed the emotional lexicon. The findings showed that the Spearman's rho coefficient reached up to .406*. (p = .026), which is significant, indicating that Young Children Emotional Lexicon (YCEL) and ECSYC were significantly correlated. The accuracies of the emotion detection recognizer using a bimodal emotion recognition approach achieved 46.7%, 60.85% and 78.73% for facial expression recognition, speech recognition, and a bimodal emotion recognition, respectively. Findings confirmed that the YCEL is feasible for speech recognition. The bimodal emotion recognition accuracies increased 32.03% and 17.88% compared with using a single modal of facial expression recognition and speech recognition, respectively. It is feasible to automatically detect children's emotional development and bring the norm up to date.

show abstract

Section: Methodsmentioning

confidence: 99%

Development and evaluation of an emotional lexicon system for young children

Wei

2019

Microsyst Technol

View full text Add to dashboard Cite

show abstract

“…Therefore, the digital human designers in future studies can use the digital human users in future studies to determine the effects of individually customised designs on user behaviours, functions, or structures. As a result, the digital human designers in future studies can also use the digital human users in future studies to fully automate the design process (Bekey, 1998;Bicego, 2005;Carruth et al, 2007;Choi et al, 2008;De Magistris et al, 2013;Duffy, 2007a, 2007b;Hardy et al, 1984;Hsu and Chen, 2012;Ishihara et al, 2005;Jung et al, 2009;Kao and Smith, 2011;Kuffner et al, 2003;Li and Zhang, 2007;Liu et al, 2010;Lu and Smith, 2006;Lu et al, 2010;Maldonado-Bascon, 2007;May et al, 2011;Miller et al, 2010;Minami et al, 1994;Noble et al, 2012;Oguri et al, 2000;Rohrer, 2007;Smith and Smith, submitted;Smith and Yen, 2010;Smith et al, 2012aSmith et al, , 2012bTaish et al, 2011;Tokuda et al, 1998;van den Broek, 2010;Zhang and Tan, 2013;Zimmer and Miteran, 2001).…”

Section: Future Studiesmentioning

confidence: 99%

Digital human designers

Smith¹,

Smith

2015

IJDH

View full text Add to dashboard Cite

This study creates digital human designers. Digital human designers are models of designers. Therefore, digital human designers use design methods to create designs. Digital human designers also use digital human users to create designs. Digital human users are models of users. Therefore, digital human designers use digital human users to determine the effects of designs on users. The digital human designers in this study created designs that matched user choices with 91.4% accuracies. As a result, the digital human designers in this study used design methods to fully automate the design process. The digital human designers in this study also used the digital human users in this study to determine the effects of designs on user structures. As a result, the digital human designers in this study also used the digital human users in this study to fully automate the design process.

show abstract

“…On the other hand, the level of expressive strength or speaker similarity cannot be guaranteed as the transplantation reach is very constrained. This is also the case for model interpolation techniques (Hsu et al, 2012), capable of achieving better expressiveness than traditional adaptation techniques at a cost in speaker similarity.…”

Section: Introductionmentioning

confidence: 99%

Emotion transplantation through adaptation in HMM-based speech synthesis

Lorenzo-Trueba¹,

Barra-Chicote²,

San-Segundo³

et al. 2015

Computer Speech & Language

View full text Add to dashboard Cite

This paper proposes an emotion transplantation method capable of modifying a synthetic speech model through the use of CSMAPLR adaptation in order to incorporate emotional information learned from a different speaker model while maintaining the identity of the original speaker as much as possible. The proposed method relies on learning both emotional and speaker identity information by means of their adaptation function from an average voice model, and combining them into a single cascade transform capable of imbuing the desired emotion into the target speaker. This method is then applied to the task of transplanting four emotions (anger, happiness, sadness and surprise) into 3 male speakers and 3 female speakers and evaluated in a number of perceptual tests. The results of the evaluations show how the perceived naturalness for emotional text significantly favors the use of the proposed transplanted emotional speech synthesis when compared to traditional neutral speech synthesis, evidenced by a big increase in the perceived emotional strength of the synthesized utterances at a slight cost in speech quality. A final evaluation with a robotic laboratory assistant application shows how by using emotional speech we can significantly increase the students' satisfaction with the dialog system, proving how the proposed emotion transplantation system provides benefits in real applications.

show abstract

Speaker-dependent model interpolation for statistical emotional speech synthesis

Cited by 4 publications

References 18 publications

Development and evaluation of an emotional lexicon system for young children

Development and evaluation of an emotional lexicon system for young children

Digital human designers

Emotion transplantation through adaptation in HMM-based speech synthesis

Contact Info

Product

Resources

About