Quantitative analysis of the local speech rate and its application to speech synthesis

Ohno, Sumio; Fukumiya, Masamichi; Fujisaki, Hiroya

doi:10.1109/icslp.1996.607255

Cited by 7 publications

(3 citation statements)

References 5 publications

(3 reference statements)

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“…A case study on contrastive focus placement in Neapolitan Italian is used to show how this can be done in the practice, as well as to let the reader appreciate the quality of the results. Two features will be analyzed, f0 and relative speech rate, the latter expressed as a continuous function of time as proposed in [7]. The results show that known facts about the prosody of Neapolitan Italian emerged from the data, but also other interesting local or cross-feature relationships between contour traits appeared.…”

Section: Introductionmentioning

confidence: 99%

Joint analysis of f<inf>0</inf> and speech rate with Functional Data Analysis

Gubian

Boves

Cangemi

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this work we propose the use of Functional Data Analysis (FDA) as a powerful methodology to tackle problems where multiple continuous speech parameters have to be analyzed jointly. A production study on contrastive focus placement in Neapolitan Italian is used as illustration. Two features are analyzed, viz. f0 and relative speech rate, both expressed as continuous functions of time. The results show that known facts about the prosody of Neapolitan Italian emerge from the data, but also other interesting local or crossfeature relationships between contour traits appear. Thus, FDA results can be used as guidance in the exploration of speech feature contour shapes, an operation that used to be carried out manually in previous speech research. The capability of jointly analyzing multiple continuous features provides a valuable improvement not only for speech analysis but also for speech re-synthesis.

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Section: Introductionmentioning

confidence: 99%

Joint analysis of f<inf>0</inf> and speech rate with Functional Data Analysis

Gubian

Boves

Cangemi

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Therefore, we rely on DTW to align segments within each word. The allowable region of the dynamic path is set within the range of [1/3,3] [41]. We use the MFCCs as features for DTW, which are estimated for the synthetic signals and the target speech.…”

Section: B Time Alignment Processmentioning

confidence: 99%

Lexical Dependent Emotion Detection Using Synthetic Speech Reference

Lotfian

Busso

2019

IEEE Access

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This paper aims to create neutral reference models from synthetic speech to contrast the emotional content of a speech signal. Modeling emotional behaviors is a challenging task due to the variability in perceiving and describing emotions. Previous studies have indicated that relative assessments are more reliable than absolute assessments. These studies suggest that having a reference signal with known emotional content (e.g., neutral emotion) to compare a target sentence may produce more reliable metrics to identify emotional segments. Ideally, we would like to have an emotionally neutral sentence with the same lexical content as the target sentence where their contents are timely aligned. In this fictitious scenario, we would be able to identify localized emotional cues by contrasting frame-by-frame the acoustic features of the target and reference sentences. This paper explores the idea of building these reference sentences leveraging the advances in speech synthesis. This paper builds a synthetic speech signal that conveys the same lexical information and is timely aligned with the target sentence in the database. Since it is expected that a single synthetic speech will not capture the full range of variability observed in neutral speech, we build multiple synthetic sentences using various voices and text-to-speech approaches. This paper analyzes whether the synthesized signals provide valid template references to describe neutral speech using feature analysis and perceptual evaluation. Finally, we demonstrate how this framework can be used in emotion recognition, achieving improvements over classifiers trained with the state-of-the-art features in detecting low versus high levels of arousal and valence. INDEX TERMS Emotional speech analysis, emotional speech recognition, synthesis of speech, feature normalization.

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“…In order to understand paralinguistic information using a computer, it is one of important issues to detect portions of sentences in which the speaker intentionally decelerates the speech rate. There are several studies on local speech rate variation [5][6][7]. However, there are few studies on detection of local speech rate variation.…”

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confidence: 99%

Study on parameters of the variable threshold to detect local speech rate deceleration in Japanese spontaneous conversational speech

Takamaru

Hiroshige

Araki

et al. 2003

Acoust. Sci. & Tech.

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IntroductionIn human communication, speech conveys not only linguistic information but also emphasis, intention, attitude and so on. They are called paralinguistic information [1]. There are several researches on paralinguistic information [2,3]. Methods for modeling or detecting of paralinguistic information is useful for various application in man-machine communication such as speech synthesis with rich expressions and recognition of paralinguistic information in spontaneous speech. A speaker controls prosodic features such as fundamental frequency, power and temporal structures to express paralinguistic information. It is said that there are few speech rate variations in Japanese read speech. In spontaneous conversational speech, however, a speaker sometimes controls speech rate greatly to obtain a listener's attention. We previously found that speech rate of important words or portions of sentences is slowed to obtain the listener's attention [4]. In order to understand paralinguistic information using a computer, it is one of important issues to detect portions of sentences in which the speaker intentionally decelerates the speech rate. There are several studies on local speech rate variation [5][6][7]. However, there are few studies on detection of local speech rate variation.We try to detect a local slower portion from a time series of mora duration [4,8]. When the speech rate of one portion is slower than that of other portions, the mora duration is longer than the durations of other morae. However, it is known that variation in time series of mora duration is caused not only by intentionally controlled speech rate variation but also by other factors such as difference of phonemes, length of a phrase or a sentence and a position of a mora in a phrase or sentence [9]. We have proposed the variable threshold (VT) [8] for detecting a local slower portion decelerated by a speaker from observed mora duration. The VT is applied to time series of mora duration. A mora whose duration exceeds the VT is detected as a local slower portion. The outline of the VT is described in section 2. In this paper, we examine the properties of parameters in the VT that are used for determining range and speed of variation of the VT. Three sets of parameters are prepared. We assume that these sets of parameters correspond to the levels of a listener's attention to

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Quantitative analysis of the local speech rate and its application to speech synthesis

Cited by 7 publications

References 5 publications

Joint analysis of f<inf>0</inf> and speech rate with Functional Data Analysis

Joint analysis of f<inf>0</inf> and speech rate with Functional Data Analysis

Lexical Dependent Emotion Detection Using Synthetic Speech Reference

Study on parameters of the variable threshold to detect local speech rate deceleration in Japanese spontaneous conversational speech

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