Wonders in the western isles, being a narrative of the commencement and progress of mission work in western Polynesia.

http://www.archive.org/details/wondersofwestern00murruoft

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.

Evaluation of Speaker Normalization Methods for Vowel Recognition Using Fuzzy ARTMAP and K-NN

A procedure that uses fuzzy ARTMAP and K-Nearest Neighbor (K-NN) categorizers to evaluate intrinsic and extrinsic speaker normalization methods is described. Each classifier is trained on preprocessed, or normalized, vowel tokens from about 30% of the speakers of the Peterson-Barney database, then tested on data from the remaining speakers. Intrinsic normalization methods included one nonscaled, four psychophysical scales (bark, bark with end-correction, mel, ERB), and three log scales, each tested on four different combinations of the fundamental (Fo) and the formants (F1 , F2, F3). For each scale and frequency combination, four extrinsic speaker adaptation schemes were tested: centroid subtraction across all frequencies (CS), centroid subtraction for each frequency (CSi), linear scale (LS), and linear transformation (LT). A total of 32 intrinsic and 128 extrinsic methods were thus compared. Fuzzy ARTMAP and K-NN showed similar trends, with K-NN performing somewhat better and fuzzy ARTMAP requiring about 1/10 as much memory. The optimal intrinsic normalization method was bark scale, or bark with end-correction, using the differences between all frequencies (Diff All). The order of performance for the extrinsic methods was LT, CSi, LS, and CS, with fuzzy AHTMAP performing best using bark scale with Diff All; and K-NN choosing psychophysical measures for all except CSi.

Speaker Normalization Methods for Vowel Cognition: Comparative Analysis Using Neural Network and Nearest Neighbor Classifiers

Intrinsic and extrinsic speaker normalization methods are systematically compared using a neural network (fuzzy ARTMAP) and L1 and L2 K-Nearest Neighbor (K-NN) categorizers trained and tested on disjoint sets of speakers of the Peterson-Barney vowel database. Intrinsic methods include one nonscaled, four psychophysical scales (bark, bark with endcorrection, mel, ERB), and three log scales, each tested on four combinations of F0 , F1, F2, F3. Extrinsic methods include four speaker adaptation schemes, each combined with the 32 intrinsic methods: centroid subtraction across all frequencies (CS), centroid subtraction for each frequency (CSi), linear scale (LS), and linear transformation (LT). ARTMAP and KNN show similar trends, with K-NN performing better, but requiring about ten times as much memory. The optimal intrinsic normalization method is bark scale, or bark with endcorrection, using the differences between all frequencies (Diff All). The order of performance for the extrinsic methods is LT, CSi, LS, and CS, with fuzzy ARTMAP performing best using bark scale with Diff All; and K-NN choosing psychophysical measures for all except CSi.

The impact of learning context on the acquisition of sociopragmatic variation patterns in non-native speaker teachers of English

The study is a cross-linguistic, cross-sectional investigation of the impact of learning contexts on the acquisition of sociopragmatic variation patterns and the subsequent enactment of compound identities. The informants are 20 non-native speaker teachers of English from a range of 10 European countries. They are all primarily mono-contextual foreign language learners/users of English: however, they differ with respect to the length of time accumulated in a target language environment. This allows for three groups to be established – those who have accumulated 60 days or less; those with between 90 days and one year and the final group, all of whom have accumulated in excess of one year. In order to foster the dismantling of the monolith of learning context, both learning contexts under consideration – i.e. the foreign language context and submersion context are broken down into micro-contexts which I refer to as loci of learning. For the purpose of this study, two loci are considered: the institutional and the conversational locus. In order to make a correlation between the impact of learning contexts and loci of learning on the acquisition of sociopragmatic variation patterns, a two-fold study is conducted. The first stage is the completion of a highly detailed language contact profile (LCP) questionnaire. This provides extensive biographical information regarding language learning history and is a powerful tool in illuminating the intensity of contact with the L2 that learners experience in both contexts as well as shedding light on the loci of learning to which learners are exposed in both contexts. Following the completion of the LCP, the informants take part in two role plays which require the enactment of differential identities when engaged in a speech event of asking for advice. The enactment of identities then undergoes a strategic and linguistic analysis in order to investigate if and how differences in the enactment of compound identities are indexed in language. Results indicate that learning context has a considerable impact not only on how identity is indexed in language, but also on the nature of identities enacted. Informants with very low levels of crosscontextuality index identity through strategic means – i.e. levels of directness and conventionality; however greater degrees of cross-contextuality give rise to the indexing of differential identities linguistically by means of speaker/hearer orientation and (non-) solidary moves. When it comes to the nature of identity enacted, it seems that more time spent in intense contact with native speakers in a range of loci of learning allows learners to enact their core identity; whereas low levels of contact with over-exposure to the institutional locus of learning fosters the enactment of generic identities.

Robust Speaker Recognition in Noisy Conditions

This paper investigates the problem of speaker identi-fication and verification in noisy conditions, assuming that speechsignals are corrupted by environmental noise, but knowledgeabout the noise characteristics is not available. This research ismotivated in part by the potential application of speaker recog-nition technologies on handheld devices or the Internet. Whilethe technologies promise an additional biometric layer of securityto protect the user, the practical implementation of such systemsfaces many challenges. One of these is environmental noise. Due tothe mobile nature of such systems, the noise sources can be highlytime-varying and potentially unknown. This raises the require-ment for noise robustness in the absence of information about thenoise. This paper describes a method that combines multicondi-tion model training and missing-feature theory to model noisewith unknown temporal-spectral characteristics. Multiconditiontraining is conducted using simulated noisy data with limitednoise variation, providing a “coarse” compensation for the noise,and missing-feature theory is applied to refine the compensationby ignoring noise variation outside the given training conditions,thereby reducing the training and testing mismatch. This paperis focused on several issues relating to the implementation of thenew model for real-world applications. These include the gener-ation of multicondition training data to model noisy speech, thecombination of different training data to optimize the recognitionperformance, and the reduction of the model’s complexity. Thenew algorithm was tested using two databases with simulated andrealistic noisy speech data. The first database is a redevelopmentof the TIMIT database by rerecording the data in the presence ofvarious noise types, used to test the model for speaker identifica-tion with a focus on the varieties of noise. The second database isa handheld-device database collected in realistic noisy conditions,used to further validate the model for real-world speaker verifica-tion. The new model is compared to baseline systems and is foundto achieve lower error rates.

Psychological and social profile of family caregivers upon commencement of palliative care provision

Context
Palliative care services are required to support patients who have advanced, life-threatening, noncurable disease, and their family caregivers. Comprehensive psychological and social support for bereaved family members also is expected. However, recent systematic reviews have demonstrated significant gaps in evidence-based approaches for such support. Furthermore, a comprehensive understanding of the psychological and social response to the family caregiver role is required for support to be optimized.

Objectives
We sought to examine the psychological and social profile of family caregivers on commencement of receiving palliative care services.

Methods
A self-report questionnaire was administered to primary family caregivers of patients within two weeks of admission to three palliative care services in Melbourne, Australia. The questionnaire incorporated six instruments that measured 11 family caregiver-related psychosocial factors; four instruments that measured caregiver psychological distress factors; 14 mental health lifetime risk factors; and a sociodemographic questionnaire.

Results
Three hundred and two family caregivers participated. Nearly half (44%) of the caregivers had a probable anxiety and/or depressive disorder, with 40% scoring more than the cutoff score for probable anxiety and 20% scoring more than the cutoff score for probable depression. Additionally, approximately 15% of caregivers met the criteria for pre-loss grief, and around 10% reported moderate to severe levels of demoralization. Caregivers who had a probable anxiety and/or depressive disorder also reported higher levels of pre-loss grief.

Conclusion
This study provides further evidence of the prevalence of poor psychosocial well-being in this population. The results reinforce the need to develop suitable strategies for psychological and social support for family caregivers.