Prediction of ‘Nules Clementine’ mandarin susceptibility to rind breakdown disorder using Vis/NIR spectroscopy

The use of diffuse reflectance visible and near infrared (Vis/NIR) spectroscopy was explored as a non-destructive technique to predict ‘Nules Clementine’ mandarin fruit susceptibility to rind breakdown (RBD) disorder by detecting rind physico-chemical properties of 80 intact fruit harvested from different canopy positions. Vis/NIR spectra were obtained using a LabSpec® spectrophotometer. Reference physico-chemical data of the fruit were obtained after 8 weeks of storage at 8 °C using conventional methods and included RBD, hue angle, colour index, mass loss, rind dry matter, as well as carbohydrates (sucrose, glucose, fructose, total carbohydrates), and total phenolic acid concentrations. Principal component analysis (PCA) was applied to analyse spectral data to identify clusters in the PCA score plots and outliers. Partial least squares (PLS) regression was applied to spectral data after PCA to develop prediction models for each quality attribute. The spectra were subjected to a test set validation by dividing the data into calibration (n = 48) and test validation (n = 32) sets. An extra set of 40 fruit harvested from a different part of the orchard was used for external validation. PLS-discriminant analysis (PLS-DA) models were developed to sort fruit based on canopy position and RBD susceptibility. Fruit position within the canopy had a significant influence on rind biochemical properties. Outside fruit had higher rind carbohydrates, phenolic acids and dry matter content and lower RBD index than inside fruit. The data distribution in the PCA and PLS-DA models displayed four clusters that could easily be identified. These clusters allowed distinction between fruit from different preharvest treatments. NIR calibration and validation results demonstrated that colour index, dry matter, total carbohydrates and mass loss were predicted with significant accuracy, with residual predictive deviation (RPD) for prediction of 3.83, 3.58, 3.15 and 2.61, respectively. The good correlation between spectral information and carbohydrate content demonstrated the potential of Vis/NIR as a non-destructive tool to predict fruit susceptibility to RBD

How culture influences perspective taking: differences in correction, not integration

Individuals from East Asian (Chinese) backgrounds have been shown to exhibit greater sensitivity to a speaker’s perspective than Western (U.S.) participants when resolving referentially ambiguous expressions. We show that this cultural difference does not reflect better integration of social information during language processing, but rather is the result of differential correction: in the earliest moments of referential processing, Chinese participants showed equivalent egocentric interference to Westerners, but managed to suppress the interference earlier and more effectively. A time-series analysis of visual-world eye-tracking data found that the two cultural groups diverged extremely late in processing, between 600 and 1400 ms after the onset of egocentric interference. We suggest that the early moments of referential processing reflect the operation of a universal stratum of processing that provides rapid ambiguity resolution at the cost of accuracy and flexibility. Late components, in contrast, reflect the mapping of outputs from referential processes to decision-making and action planning systems, allowing for a flexibility in responding that is molded by culturally specific demands

Subspace Gaussian Mixture Models for Language Identification and Dysarthric Speech Intelligibility Assessment

En esta Tesis se ha investigado la aplicación de técnicas de modelado de subespacios de mezclas de Gaussianas en dos problemas relacionados con las tecnologías del habla, como son la identificación automática de idioma (LID, por sus siglas en inglés) y la evaluación automática de inteligibilidad en el habla de personas con disartria. Una de las técnicas más importantes estudiadas es el análisis factorial conjunto (JFA, por sus siglas en inglés). JFA es, en esencia, un modelo de mezclas de Gaussianas en el que la media de cada componente se expresa como una suma de factores de dimensión reducida, y donde cada factor representa una contribución diferente a la señal de audio. Esta factorización nos permite compensar nuestros modelos frente a contribuciones indeseadas presentes en la señal, como la información de canal. JFA se ha investigado como clasficador y como extractor de parámetros. En esta última aproximación se modela un solo factor que representa todas las contribuciones presentes en la señal. Los puntos en este subespacio se denominan i-Vectors. Así, un i-Vector es un vector de baja dimensión que representa una grabación de audio. Los i-Vectors han resultado ser muy útiles como vector de características para representar señales en diferentes problemas relacionados con el aprendizaje de máquinas. En relación al problema de LID, se han investigado dos sistemas diferentes de acuerdo al tipo de información extraída de la señal. En el primero, la señal se parametriza en vectores acústicos con información espectral a corto plazo. En este caso, observamos mejoras de hasta un 50% con el sistema basado en i-Vectors respecto al sistema que utilizaba JFA como clasificador. Se comprobó que el subespacio de canal del modelo JFA también contenía información del idioma, mientras que con los i-Vectors no se descarta ningún tipo de información, y además, son útiles para mitigar diferencias entre los datos de entrenamiento y de evaluación. En la fase de clasificación, los i-Vectors de cada idioma se modelaron con una distribución Gaussiana en la que la matriz de covarianza era común para todos. Este método es simple y rápido, y no requiere de ningún post-procesado de los i-Vectors. En el segundo sistema, se introdujo el uso de información prosódica y formántica en un sistema de LID basado en i-Vectors. La precisión de éste estaba por debajo de la del sistema acústico. Sin embargo, los dos sistemas son complementarios, y se obtuvo hasta un 20% de mejora con la fusión de los dos respecto al sistema acústico solo. Tras los buenos resultados obtenidos para LID, y dado que, teóricamente, los i-Vectors capturan toda la información presente en la señal, decidimos usarlos para la evaluar de manera automática la inteligibilidad en el habla de personas con disartria. Los logopedas están muy interesados en esta tecnología porque permitiría evaluar a sus pacientes de una manera objetiva y consistente. En este caso, los i-Vectors se obtuvieron a partir de información espectral a corto plazo de la señal, y la inteligibilidad se calculó a partir de los i-Vectors obtenidos para un conjunto de palabras dichas por el locutor evaluado. Comprobamos que los resultados eran mucho mejores si en el entrenamiento del sistema se incorporaban datos de la persona que iba a ser evaluada. No obstante, esta limitación podría aliviarse utilizando una mayor cantidad de datos para entrenar el sistema.In this Thesis, we investigated how to effciently apply subspace Gaussian mixture modeling techniques onto two speech technology problems, namely automatic spoken language identification (LID) and automatic intelligibility assessment of dysarthric speech. One of the most important of such techniques in this Thesis was joint factor analysis (JFA). JFA is essentially a Gaussian mixture model where the mean of the components is expressed as a sum of low-dimension factors that represent different contributions to the speech signal. This factorization makes it possible to compensate for undesired sources of variability, like the channel. JFA was investigated as final classiffer and as feature extractor. In the latter approach, a single subspace including all sources of variability is trained, and points in this subspace are known as i-Vectors. Thus, one i-Vector is defined as a low-dimension representation of a single utterance, and they are a very powerful feature for different machine learning problems. We have investigated two different LID systems according to the type of features extracted from speech. First, we extracted acoustic features representing short-time spectral information. In this case, we observed relative improvements with i-Vectors with respect to JFA of up to 50%. We realized that the channel subspace in a JFA model also contains language information whereas i-Vectors do not discard any language information, and moreover, they help to reduce mismatches between training and testing data. For classification, we modeled the i-Vectors of each language with a Gaussian distribution with covariance matrix shared among languages. This method is simple and fast, and it worked well without any post-processing. Second, we introduced the use of prosodic and formant information with the i-Vectors system. The performance was below the acoustic system but both were found to be complementary and we obtained up to a 20% relative improvement with the fusion with respect to the acoustic system alone. Given the success in LID and the fact that i-Vectors capture all the information that is present in the data, we decided to use i-Vectors for other tasks, specifically, the assessment of speech intelligibility in speakers with different types of dysarthria. Speech therapists are very interested in this technology because it would allow them to objectively and consistently rate the intelligibility of their patients. In this case, the input features were extracted from short-term spectral information, and the intelligibility was assessed from the i-Vectors calculated from a set of words uttered by the tested speaker. We found that the performance was clearly much better if we had available data for training of the person that would use the application. We think that this limitation could be relaxed if we had larger databases for training. However, the recording process is not easy for people with disabilities, and it is difficult to obtain large datasets of dysarthric speakers open to the research community. Finally, the same system architecture for intelligibility assessment based on i-Vectors was used for predicting the accuracy that an automatic speech recognizer (ASR) system would obtain with dysarthric speakers. The only difference between both was the ground truth label set used for training. Predicting the performance response of an ASR system would increase the confidence of speech therapists in these systems and would diminish health related costs. The results were not as satisfactory as in the previous case, probably because an ASR is a complex system whose accuracy can be very difficult to be predicted only with acoustic information. Nonetheless, we think that we opened a door to an interesting research direction for the two problems

Analyzing Prosody with Legendre Polynomial Coefficients

This investigation demonstrates the effectiveness of Legendre polynomial coefficients representing prosodic contours within the context of two different tasks: nativeness classification and sarcasm detection. By making use of accurate representations of prosodic contours to answer fundamental linguistic questions, we contribute significantly to the body of research focused on analyzing prosody in linguistics as well as modeling prosody for machine learning tasks. Using Legendre polynomial coefficient representations of prosodic contours, we answer prosodic questions about differences in prosody between native English speakers and non-native English speakers whose first language is Mandarin. We also learn more about prosodic qualities of sarcastic speech. We additionally perform machine learning classification for both tasks, (achieving an accuracy of 72.3% for nativeness classification, and achieving 81.57% for sarcasm detection). We recommend that linguists looking to analyze prosodic contours make use of Legendre polynomial coefficients modeling; the accuracy and quality of the resulting prosodic contour representations makes them highly interpretable for linguistic analysis

The non-invasive assessment of avocado maturity and quality

Horticultural products in today's modern market must have high quality standards. Consumer demand for consistent quality agricultural produce remains strong and continues to increase, this will lead to the development and subsequent increased availability of sophisticated techniques, sensors, and user-friendly non-invasive systems for measuring product quality indices. The inability to consistently guarantee internal fruit quality is a major factor not only for the Australian avocado industry but also the entire horticulture sector. Poor fruit quality is seen as a key factor affecting consumer confidence and impacts on supply chain efficiency and profitability. Removing fruit quality inconsistencies while providing the consumer with a consistent quality product is a vital commercial consideration of the Australian avocado industry for both domestic and export markets. Many fruit quality attributes affecting consumer acceptance are assessed using traditional methods that are generally subjective, labour intensive and costly. Commercially, avocado maturity is measured destructively by the determination of dry matter (DM) content, moisture content (MC) or oil content, all of which are highly correlated. Maturity is an important component in avocado fruit quality and a prime factor in palatability. A rapid, non-destructive measurement system that can accurately and simultaneously monitor external and internal attributes of every avocado fruit either in the field or in an in-line setting, is highly desirable for ensuring consistent product quality over an extended season, increasing industry marketability and profitability. The utility of near infrared (NIR) spectroscopy was investigated as a non-invasive assessment tool for estimating avocado maturity and thereby eating quality based on dry matter content of whole intact fruit primarily for the avocado variety 'Hass'. The technique was also assessed for detecting bruises and for predicting rot susceptibility as an indication of shelf-life for possible implementation in a commercial in-line application. The project also investigated the importance of the calibration model development process to incorporate seasonal and geographical variability to ensure model robustness. NIR spectroscopy has an obvious place in agriculture and environmental applications with its core strength in the analysis of biological materials, plus low cost of analysis, simplicity in sample preparation, no chemical reagent requirements, simultaneous analysis of multiple constituents, good repeatability and high throughput capability. The commercially available NIR spectroscopy systems assessed in this project highlighted the potential of NIR spectroscopy and its suitability for application in a commercial in-line setting for predicting avocado maturity and palatability of whole intact avocados, based on DM content. With horticultural products, the major challenge of implementing NIR spectroscopy is to ensure that the calibration model is robust, that is, that the calibration model holds across growing seasons and potentially across growing districts. The present project represents the first study to investigate the effect of seasonal variation on model robustness to be applied to avocado fruit. It found that seasonal variability has a significant effect on model predictive performance for DM in avocados. The robustness of the calibration model, which in general limits the commercial application for the technique, was found to increase across seasons when more seasonal variability was included in the calibration set. Across the seasons it achieved predictive performances in this case in the range of: validation coefficient of determination (Rᵥ²) of 0.76 – 0.89, root mean square error of prediction (RMSEP) of 1.43 - 1.97%, and standard deviation ratio's (SDR) of 2.0 to 3.1. Similarly, there are spectral differences between geographical regions and that specific regional models may have significantly reduced predictive performance when applied to samples containing biological variability from a different growing region. As with seasonal variability, this can be addressed by incorporating multiple geographical growing regions into the calibration model to account for the biological variability to improve model robustness as demonstrated in this study (i.e., Rᵥ² of 0.89, RMSEP of 1.51%, and SDR of 3.6). Furthermore, when models are constructed to include both season and geographical variability, model performance can be more robust when dealing with a broader range of future sample variability. This was demonstrated with calibration models constructed to incorporate 3 years of seasonal variability and encompassing 3 geographical regions, obtaining predictive performances ranging from Rᵥ ² 0.87 - 0.89; RMSEP of 1.42 - 1.64% and SDR of 2.7 - 3.1 across the various geographical regions. NIR spectroscopy shows great promise for the application in a commercial, in-line setting for the non-destructive evaluation of impact damage (bruising) and rot susceptibility of whole avocado fruit, although optimisation of the technology is required to address speed of throughput and environmental issues. The adoption of a rapid, non-invasive method to identify fruit that are less prone to rots and internal disorders would allow selection of fruit that could be sent to more distant markets with greater confidence that it will arrive in acceptable quality, thus ensuring maximum yield and higher returns for the producer and marketer. The ability of the NIR classification models to accurately predict rot development of hard green avocado fruit (stage 0 ripeness) into two classes, ≤10% and >10% of flesh affected, ranged from 65-84% over the three growing seasons. When the rot classes were defined as ≤30% and >30% the accuracy ranged from 69%-77%. In relation to impact damage (bruising), trials conducted over three growing seasons using an NIR spot assessment technique found hard green fruit at stage 2 ripeness, that were deliberately bruised could be correctly detected with 70-79% accuracy after 2-5 hours of impacting and with 83-89% accuracy after 24 hours. For eating ripe (stage 4) fruit, the accuracy was 60-100% after 2-5 hours of impacting and 66-100% after 24 hours across the three growing seasons. This indicates that in a commercial situation it would be an advantage to hold the fruit for 24 hours before undertaking NIR scanning

On the cognitive basis of contact-induced sound change: Vowel merger reversal in Shanghainese

This study investigated the source and status of a recent sound change in Shanghainese (Wu, Sinitic) that has been attributed to language contact with Mandarin. The change involves two vowels, /e/ and /ɛ/, reported to be merged three decades ago but produced distinctly in contemporary Shanghainese. Results of two production experiments showed that speaker age, language mode (monolingual Shanghainese vs. bilingual Shanghainese-Mandarin), and crosslinguistic phonological similarity all influenced the production of these vowels. These findings provide evidence for language contact as a linguistic means of merger reversal and are consistent with the view that contact phenomena originate from cross-language interaction within the bilingual mind