Vowel-Based Non-uniform Prosody Modification for Emotion Conversion

Vydana, Hari Krishna; Kadiri, Sudarsana Reddy; Vuppala, Anil Kumar

doi:10.1007/s00034-015-0134-1

Cited by 20 publications

(5 citation statements)

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“…These features were used to build emotion detection [206] and emotion recognition systems [19], [20], [205]. The effectiveness of excitation during the production of emotional speech was examined in [207]- [209] using prosody modification to convert neutral speech to emotional speech.…”

Section: B Study Of Vocal Emotionsmentioning

confidence: 99%

Extraction and Utilization of Excitation Information of Speech: A Review

2021

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| Speech production can be regarded as a process where a time-varying vocal tract system (filter) is excited by a time-varying excitation. In addition to its linguistic message, the speech signal also carries information about, for example, the gender and age of the speaker. Moreover, the speech signal includes acoustical cues about several speaker traits, such as the emotional state and the state of health of the speaker. In order to understand the production of these acoustical cues by the human speech production mechanism and utilize this information in speech technology, it is necessary to extract features describing both the excitation and the filter of the human speech production mechanism. While the methods to estimate and parameterize the vocal tract system are well established, the excitation appears less studied. This article provides a review of signal processing approaches used for the extraction of excitation information from speech. This article highlights the importance of excitation information in the analysis and classification of phonation type and vocal emotions, in the analysis of nonverbal laughter sounds, and in studying pathological voices. Furthermore, recent developments of deep learning techniques in the context of extraction and utilization of the excitation information are discussed.

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Section: B Study Of Vocal Emotionsmentioning

confidence: 99%

Extraction and Utilization of Excitation Information of Speech: A Review

2021

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“…Prosody features, such as pitch contour, jitter [21], [30], and speaking rate [47] have been analyzed in literature. Prosody modification methods mainly focus on either static (sentencewise) or dynamic (region-wise) prosody transformation [22], [24], [47], [48]. In most of these, epoch locations, which are the instants of glottal closure, have been taken as anchor points for prosody manipulation.…”

Section: Related Workmentioning

confidence: 99%

“…MCD is used to evaluate the spectral conversion efficiency in emotion conversion [45], [55], [56]. It is calculated as in (24)…”

Section: ) MCDmentioning

confidence: 99%

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Hybrid Framework for Speaker-Independent Emotion Conversion Using i-Vector PLDA and Neural Network

et al. 2019

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Expressive speech can be synthesized using acoustic feature modeling by mapping the spectral and fundamental frequency parameters between neutral speech and target emotions based on context. Speaker and text-independent emotion conversion are challenging modeling problems in this paradigm. In this paper, spectral mapping using an i-vector-based framework of fixed dimensions is proposed for the speaker-independent emotion conversion, considering the entire problem in the utterance domain, rather than the existing approaches using frame-level processing. The high dimensionality of i-vectors and reduced utterances for i-vector training necessitate the use of Probabilistic Linear Discriminant Analysis (PLDA) to derive the emotion dependent latent vector. The i-vector setup does not require parallel data or alignment procedures at any stage of training. F 0 training is conducted on a multilayer feed-forward neural network using limited aligned seed parallel data. The framework is tested on three different languages (datasets) viz. German (EmoDB), Telugu (IITKGP), and English (SAVEE). The proposed approach delivered superior performance compared to the baseline under both clean and noisy data conditions considered for analysis. Under clean data conditions, the proposed model was found to perform better than the baseline with a Mel Cepstral Distortion as low as 3.8 (fear), an F 0-RMSE of 26.31 (happiness), and a Perceptual Evaluation of Speech Quality (PESQ) of 3.64 (anger) across datasets. Subjective testing provided a maximum CMOS of 4.10 (anger), 4.44 (fear), and 3.43 (happiness). INDEX TERMS CV-GMM, speech emotion, feed-forward ANN, i-vector, MFCC, PLDA. I. INTRODUCTION Emotions form a prominent para-linguistic element of human communication, consisting of speech, facial expressions, gestures, body language, etc. Among these, speech is the most readily accessible information source containing datapoints such as the message conveyed, speaker identity, gender, emotion, and speaker state of health. Emotions animate our speech and is essential for effective dialogue delivery in human-machine interaction and socio-cultural relationships. Furthermore, expressive speech synthesis finds applications The associate editor coordinating the review of this manuscript and approving it for publication was Kathiravan Srinivasan. in story-telling, speaking aids for the disabled [1]-[6], video games and speech-to-speech translators [7] to name a few. An expression synthesis system is normally added as a post-processing stage in text-to-speech synthesis (TTS) systems. There is often a need for a TTS synthesizer tested across multiple languages. This case is particularly relevant in multilingual countries such as India, where 22 official languages [8] exist along-with several other unofficial languages. Effective training of prosodic and spectral parameters from multiple languages is particularly useful in designing affective speech-to-speech translators in lowresource languages. Human-like dialogue delivery often encounters spontaneou...

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“…Recently, the importance of features extracted around GCIs has been observed in several studies in emotional speech analysis and detection [17][18][19]. The importance of frequency of vibration of vocal folds (interval between GCIs) was studied in emotion detection and emotion conversion/synthesis in [18,20]. In addition, some speech synthesis systems (e.g., [21][22][23]) use voice source modeling and GCI detection in generation of the synthesizer's excitation signal.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Glottal Closure Instants Detection Algorithms for Emotional Speech

Kadiri

Alku

Yegnanarayana

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In production of voiced speech, epochs or glottal closure instants (GCIs) refer to the instants of significant excitation of the vocal tract. Extraction of GCIs is used as a pre-processing stage in many areas of speech technology, such as in prosody modification, speech synthesis and voice source analysis. In the past decades, several GCI detection algorithms have been developed and most of them provide excellent results for speech signals produced using modal (normal) type of phonation. There are, however, no studies comparing multiple state-of-the-art GCI detection methods in emotional speech. In this paper, we compare six GCI detection algorithms using emotional speech and known evaluation metrics. We use the Berlin EMO-DB acted emotional speech database which contains seven emotions and simultaneous electroglottography (EGG) recordings as ground truth. The results show that all six GCI detection algorithms give best performance in processing speech of neutral emotion and that the performance degrade particularly in emotions of high arousal (anger and joy). To improve the performance of GCI detection in emotional speech, the study underlines the importance of local average pitch period estimates.

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Vowel-Based Non-uniform Prosody Modification for Emotion Conversion

Cited by 20 publications

References 13 publications

Extraction and Utilization of Excitation Information of Speech: A Review

Extraction and Utilization of Excitation Information of Speech: A Review

Hybrid Framework for Speaker-Independent Emotion Conversion Using i-Vector PLDA and Neural Network

Comparison of Glottal Closure Instants Detection Algorithms for Emotional Speech

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