29,925 research outputs found
Accent processing in dementia
Accented speech conveys important nonverbal information about the speaker as well as presenting the brain with the problem of decoding a non-canonical auditory signal. The processing of non-native accents has seldom been studied in neurodegenerative disease and its brain basis remains poorly understood. Here we investigated the processing of non-native international and regional accents of English in cohorts of patients with Alzheimer's disease (AD; n=20) and progressive nonfluent aphasia (PNFA; n=6) in relation to healthy older control subjects (n=35). A novel battery was designed to assess accent comprehension and recognition and all subjects had a general neuropsychological assessment. Neuroanatomical associations of accent processing performance were assessed using voxel-based morphometry on MR brain images within the larger AD group. Compared with healthy controls, both the AD and PNFA groups showed deficits of non-native accent recognition and the PNFA group showed reduced comprehension of words spoken in international accents compared with a Southern English accent. At individual subject level deficits were observed more consistently in the PNFA group, and the disease groups showed different patterns of accent comprehension impairment (generally more marked for sentences in AD and for single words in PNFA). Within the AD group, grey matter associations of accent comprehension and recognition were identified in the anterior superior temporal lobe. The findings suggest that accent processing deficits may constitute signatures of neurodegenerative disease with potentially broader implications for understanding how these diseases affect vocal communication under challenging listening conditions
Leveraging native language information for improved accented speech recognition
Recognition of accented speech is a long-standing challenge for automatic
speech recognition (ASR) systems, given the increasing worldwide population of
bi-lingual speakers with English as their second language. If we consider
foreign-accented speech as an interpolation of the native language (L1) and
English (L2), using a model that can simultaneously address both languages
would perform better at the acoustic level for accented speech. In this study,
we explore how an end-to-end recurrent neural network (RNN) trained system with
English and native languages (Spanish and Indian languages) could leverage data
of native languages to improve performance for accented English speech. To this
end, we examine pre-training with native languages, as well as multi-task
learning (MTL) in which the main task is trained with native English and the
secondary task is trained with Spanish or Indian Languages. We show that the
proposed MTL model performs better than the pre-training approach and
outperforms a baseline model trained simply with English data. We suggest a new
setting for MTL in which the secondary task is trained with both English and
the native language, using the same output set. This proposed scenario yields
better performance with +11.95% and +17.55% character error rate gains over
baseline for Hispanic and Indian accents, respectively.Comment: Accepted at Interspeech 201
Combined Acoustic and Pronunciation Modelling for Non-Native Speech Recognition
In this paper, we present several adaptation methods for non-native speech
recognition. We have tested pronunciation modelling, MLLR and MAP non-native
pronunciation adaptation and HMM models retraining on the HIWIRE foreign
accented English speech database. The ``phonetic confusion'' scheme we have
developed consists in associating to each spoken phone several sequences of
confused phones. In our experiments, we have used different combinations of
acoustic models representing the canonical and the foreign pronunciations:
spoken and native models, models adapted to the non-native accent with MAP and
MLLR. The joint use of pronunciation modelling and acoustic adaptation led to
further improvements in recognition accuracy. The best combination of the above
mentioned techniques resulted in a relative word error reduction ranging from
46% to 71%
The Role of Speaker Identification in Taiwanese Attitudes Towards Varieties of English
No abstract available
The perception of English-accented polish – a pilot study
•Does familiarity with a specific foreign language facilitate the recognition and identification of that accent in foreign-accented Polish
Four not six: revealing culturally common facial expressions of emotion
As a highly social species, humans generate complex facial expressions to communicate a diverse range of emotions. Since Darwin’s work, identifying amongst these complex patterns which are common across cultures and which are culture-specific has remained a central question in psychology, anthropology, philosophy, and more recently machine vision and social robotics. Classic approaches to addressing this question typically tested the cross-cultural recognition of theoretically motivated facial expressions representing six emotions, and reported universality. Yet, variable recognition accuracy across cultures suggests a narrower cross-cultural communication, supported by sets of simpler expressive patterns embedded in more complex facial expressions. We explore this hypothesis by modelling the facial expressions of over 60 emotions across two cultures, and segregating out the latent expressive patterns. Using a multi-disciplinary approach, we first map the conceptual organization of a broad spectrum of emotion words by building semantic networks in two cultures. For each emotion word in each culture, we then model and validate its corresponding dynamic facial expression, producing over 60 culturally valid facial expression models. We then apply to the pooled models a multivariate data reduction technique, revealing four latent and culturally common facial expression patterns that each communicates specific combinations of valence, arousal and dominance. We then reveal the face movements that accentuate each latent expressive pattern to create complex facial expressions. Our data questions the widely held view that six facial expression patterns are universal, instead suggesting four latent expressive patterns with direct implications for emotion communication, social psychology, cognitive neuroscience, and social robotics
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