Comparing non-verbal vocalisations in conversational speech corpora

Conversations do not only consist of spoken words but they also consist of non-verbal vocalisations. Since there is no standard to define and to classify (possible) non-speech sounds the annotations for these vocalisations differ very much for various corpora of conversational speech. There seems to be agreement in the six inspected corpora that hesitation sounds and feedback vocalisations are considered as words (without a standard orthography). The most frequent non-verbal vocalisation are laughter on the one hand and, if considered a vocal sound, breathing noises on the other

Auditory communication in domestic dogs: vocal signalling in the extended social environment of a companion animal

Domestic dogs produce a range of vocalisations, including barks, growls, and whimpers, which are shared with other canid species. The source–filter model of vocal production can be used as a theoretical and applied framework to explain how and why the acoustic properties of some vocalisations are constrained by physical characteristics of the caller, whereas others are more dynamic, influenced by transient states such as arousal or motivation. This chapter thus reviews how and why particular call types are produced to transmit specific types of information, and how such information may be perceived by receivers. As domestication is thought to have caused a divergence in the vocal behaviour of dogs as compared to the ancestral wolf, evidence of both dog–human and human–dog communication is considered. Overall, it is clear that domestic dogs have the potential to acoustically broadcast a range of information, which is available to conspecific and human receivers. Moreover, dogs are highly attentive to human speech and are able to extract speaker identity, emotional state, and even some types of semantic information

Influences of maternal care on chicken welfare

This review was funded by a BBSRC Future Leader Fellowship to Joanne Edgar.In domestic chickens, the provision of maternal care strongly influences the behavioural development of chicks. Mother hens play an important role in directing their chicks' behaviour and are able to buffer their chicks' response to stressors. Chicks imprint upon their mother, who is key in directing the chicks' behaviour and in allowing them to develop food preferences. Chicks reared by a mother hen are less fearful and show higher levels of behavioural synchronisation than chicks reared artificially. In a commercial setting, more fearful chicks with unsynchronised behaviour are more likely to develop behavioural problems, such as feather pecking. As well as being an inherent welfare problem, fear can also lead to panic responses, smothering, and fractured bones. Despite the beneficial effects of brooding, it is not commercially viable to allow natural brooding on farms and so chicks are hatched in large incubators and reared artificially, without a mother hen. In this review we cover the literature demonstrating the important features of maternal care in domestic chickens, the behavioural consequences of deprivation and the welfare implications on commercial farms. We finish by suggesting ways to use research in natural maternal care to improve commercial chick rearing practice.Publisher PDFPeer reviewe

Using geographic profiling to locate elusive nocturnal animals: A case study with spectral tarsiers

© 2015 The Zoological Society of London. Estimates of biodiversity, population size, population density and habitat use have important implications for management of both species and habitats, yet are based on census data that can be extremely difficult to collect. Traditional assessment techniques are often limited by time and money and by the difficulties of working in certain habitats, and species become more difficult to find as population size decreases. Particular difficulties arise when studying elusive species with cryptic behaviours. Here, we show how geographic profiling (GP) - a statistical tool originally developed in criminology to prioritize large lists of suspects in cases of serial crime - can be used to address these problems. We ask whether GP can be used to locate sleeping sites of spectral tarsiers Tarsius tarsier in Sulawesi, Southeast Asia, using as input the positions at which tarsier vocalizations were recorded in the field. This novel application of GP is potentially of value as tarsiers are cryptic and nocturnal and can easily be overlooked in habitat assessments (e.g. in dense rainforest). Our results show that GP provides a useful tool for locating sleeping sites of this species, and indeed analysis of a preliminary dataset during field work strongly suggested the presence of a sleeping tree at a previously unknown location; two sleeping trees were subsequently found within 5m of the predicted site. We believe that GP can be successfully applied to locating the nests, dens or roosts of elusive animals such as tarsiers, potentially improving estimates of population size with important implications for management of both species and habitats.We thank Operation Wallacea for supporting S.C.F. in thisproject and for providing logistical support for the fieldwork,and Aidan Kelsey for invaluable assistance in the field. Wethank the Indonesian Institute of Sciences (LIPI) andKementerian Riset dan Teknologi Republik Indonesia(RISTEK) for providing permission to undertake the work(RISTEK permit no. 211/SIP/FRP/SM/VI/2013, and BalaiKonservasi Sumber Daya Alam (BKSDA) for theirassistance

Vocalisations of the New Zealand morepork (Ninox novaeseelandiae) on Ponui Island : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Zoology at Massey University, Palmerston North, New Zealand

Vocalisations provide an effective way to overcome the challenge of studying the behaviour of cryptic or nocturnal species. Knowledge of vocalisations can be applied to management strategies such as population census, monitoring, and territory mapping. The New Zealand Morepork (Ninox novaeseelandiae) is a nocturnal raptor and, to date, there has been little research into their vocalisations even though this offers a key method for monitoring morepork populations. Although not at risk, population monitoring of morepork will help detect population size changes in this avian predator which may prey on native endangered fauna and may suffer secondary poisoning. This study investigated the vocal ecology of morepork on Ponui Island, Hauraki Gulf, New Zealand from April 2013 to April 2014. The initial goal was to develop a monitoring method for morepork. However, due to a lack of detailed basic knowledge of their vocalisations, the primary objective shifted to filling that knowledge gap and providing baseline data for future research. The aims of this study were thus to characterise all of the calls given by the morepork on the island; to investigate spectral and temporal parameters of three main calls; to plot the amount of calling across a night and a year; and to study the responses of morepork to playback calls. Eight morepork were caught using mist-nets and subsequently tracked by radio-telemetry. Vocalisations were recorded using manual and automatic digital sound recorders and calls were analysed with manual and automated sound analysis software. I described eleven distinct calls, referred to as more-pork, trill, rororo, more-more-pork, weow, low trill, copulation squeal, single hoot, distress squeak, chicketting and juvenile begging trill and I further analysed the spectral and temporal characteristics of three main calls, more-pork, trill and rororo. I found variation between individual morepork in acoustic parameters of these call types. I found no evidence of sexual variation in the fundamental frequency, fundamental duration nor inter-syllable duration of the three call types. However, sample sizes were small (2 males to 7 females) and a larger sample size would be needed to confirm these results. The average number of all morepork call types showed temporal variation both nightly and monthly. A low amount of calling in winter months compared to summer appeared to coincide with the morepork breeding cycle. The highest numbers of call were heard from November to January, with the numbers of calls during this period being significantly higher than in all other months. The number of calls per hour showed two peaks: one around the middle of the night and the other during the last hour of darkness. The number of calls heard in the first two hours after sunset were significantly lower than during the rest of the night. Playbacks were effective in eliciting responses from morepork, but the proportion of responses to playback was lower than to natural calls. Response rates did not seem to be affected by season. Session time and order of playback had an effect on proportional responses as well as playback call-type whereby rororo elicited the most responses and trill elicited the fewest. This project broadened our knowledge of morepork vocal ecology and therefore contributes to our knowledge of raptor vocal communication. The study also presents information and recommendations that will be useful to future research and also in management of morepork. In particular, this project provides background information needed to help develop protocols for acoustic monitoring of morepork. The techniques used in this study and the general results can be used or applied to studies of other nocturnal species

End-to-End Audiovisual Fusion with LSTMs

Several end-to-end deep learning approaches have been recently presented which simultaneously extract visual features from the input images and perform visual speech classification. However, research on jointly extracting audio and visual features and performing classification is very limited. In this work, we present an end-to-end audiovisual model based on Bidirectional Long Short-Term Memory (BLSTM) networks. To the best of our knowledge, this is the first audiovisual fusion model which simultaneously learns to extract features directly from the pixels and spectrograms and perform classification of speech and nonlinguistic vocalisations. The model consists of multiple identical streams, one for each modality, which extract features directly from mouth regions and spectrograms. The temporal dynamics in each stream/modality are modeled by a BLSTM and the fusion of multiple streams/modalities takes place via another BLSTM. An absolute improvement of 1.9% in the mean F1 of 4 nonlingusitic vocalisations over audio-only classification is reported on the AVIC database. At the same time, the proposed end-to-end audiovisual fusion system improves the state-of-the-art performance on the AVIC database leading to a 9.7% absolute increase in the mean F1 measure. We also perform audiovisual speech recognition experiments on the OuluVS2 database using different views of the mouth, frontal to profile. The proposed audiovisual system significantly outperforms the audio-only model for all views when the acoustic noise is high.Comment: Accepted to AVSP 2017. arXiv admin note: substantial text overlap with arXiv:1709.00443 and text overlap with arXiv:1701.0584