Respiratory Belts and Whistles: A Preliminary Study of Breathing Acoustics for Turn-Taking

Włodarczak, Marcin; Heldner, Mattias

doi:10.21437/interspeech.2016-344

Cited by 12 publications

(10 citation statements)

References 21 publications

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“…The study was based on the same material used in [10]. The description of the recording setup and data pre-processing is repeated below for completeness.…”

Section: Methodsmentioning

confidence: 99%

Respiratory Turn-Taking Cues

Włodarczak

Heldner

2016

Interspeech 2016

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This paper investigates to what extent breathing can be used as a cue to turn-taking behaviour. The paper improves on existing accounts by considering all possible transitions between speaker states (silent, speaking, backchanneling) and by not relying on global speaker models. Instead, all features (including breathing range and resting expiratory level) are estimated in an incremental fashion using the left-hand context. We identify several inhalatory features relevant to turn-management, and assess the fit of models with these features as predictors of turntaking behaviour.

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“…The study was based on the same material used in [10]. The description of the recording setup and data pre-processing is repeated below for completeness.…”

Section: Methodsmentioning

confidence: 99%

Respiratory Turn-Taking Cues

Włodarczak

Heldner

2016

Interspeech 2016

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“…RST is used in the diagnosis of the sleepless syndrome, and its purpose is to know if the upper body is inflamed by examining the signals of a patient's respiration. The two RST have been used previously in examining the differences in speech rhythm ( [7], [8]), the correlation between pitch and intensity ( [9]), gender differences ( [10]), the F0 declination ( [11]), and turn-taking in dialogue ( [12]), among other uses.…”

Section: Introductionmentioning

confidence: 99%

Respiratory Control, Pauses, and Tonal Control in L1’s and L2’s Text Reading – A Pilot Study on Swedish and Japanese –

Isei-Jaakkola¹,

Nagano-Madsen²,

Ochi³

2018

Speech Prosody 2018

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This paper reports the results of a pilot study, which examines the respiratory control exerted by chest and abdominal-muscles during the reading of a long text in the mother tongue (L1) and a targeted foreign language that is being learned (L2), with reference to syntax and prosody in Japanese and Swedish. Three datasets of read speech were obtained from Swedish speakers (SwL1), Swedish learners of Japanese (SwL2), and Japanese speakers (JL1). The results showed that the subjects used respiratory control differently while reading L1 texts and L2 texts, respectively. Both SwL1 and JL1 used chest and abdominal-muscles almost simultaneously, and the peaks of their muscular movements co-occurred at the onset of major syntactic units such as sentences and clauses. SwL2 used more chest muscles than abdominal-muscles, with muscular movements being more frequent, irregular, and small. There was no significant difference between JL1 and Swedish L1 and L2 in terms of the tonal control (pitch range). Some pitch peaks and pauses that appeared at the major syntactic boundaries coincided with the peaks of the muscular movements, but other pitch peaks and pauses did not. These results led to the hypothesis that the acquisition of intonation precedes that of respiratory control in L2 learning.

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“…In most of the works that consider respiration, data is captured with respiration sensors such as belt-like strips placed on the chest (e.g., [26,58]), or with other dedicated devices. An example of such a device is the CO2100C module by Biopac 1 that measures the quantity of C O 2 in the exhaled air.…”

Section: Methods For Measuring the Respirationmentioning

confidence: 99%

“…Wlodarczak and Heldner [58] studied the communicative functions of respiratory sounds. They found that acoustic intensity of inhalation is the feature that allows one to detect the forthcoming turn-takings.…”

Section: Measuring Respiration From the Audio Signalmentioning

confidence: 99%

The role of respiration audio in multimodal analysis of movement qualities

Lussu

Niewiadomski

Volpe

et al. 2019

J Multimodal User Interfaces

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In this paper, we explore how the audio respiration signal can contribute to multimodal analysis of movement qualities. Within this aim, we propose two novel techniques which use the audio respiration signal captured by a standard microphone placed near to mouth and supervised machine learning algorithms. The first approach consists of the classification of a set of acoustic features extracted from exhalations of a person performing fluid or fragmented movements. In the second approach, the intrapersonal synchronization between the respiration and kinetic energy of body movements is used to distinguish the same qualities. First, the value of synchronization between modalities is computed using the Event Synchronization algorithm. Next, a set of features, computed from the value of synchronization, is used as an input to machine learning algorithms. Both approaches were applied to the multimodal corpus composed of short performances by three professionals performing fluid and fragmented movements. The total duration of the corpus is about 17 min. The highest F-score (0.87) for the first approach was obtained for the binary classification task using Support Vector Machines (SVM-LP). The best result for the same task using the second approach was obtained using Naive Bayes algorithm (F-score of 0.72). The results confirm that it is possible to infer information about the movement qualities from respiration audio.

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Respiratory Belts and Whistles: A Preliminary Study of Breathing Acoustics for Turn-Taking

Cited by 12 publications

References 21 publications

Respiratory Turn-Taking Cues

Respiratory Turn-Taking Cues

Respiratory Control, Pauses, and Tonal Control in L1’s and L2’s Text Reading – A Pilot Study on Swedish and Japanese –

The role of respiration audio in multimodal analysis of movement qualities

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