大车前（Plantago major L. "Giant Turkish."）体外优化再生体系的建立及Na+/H+逆向转运蛋白基因SeNHX1 和TtNHX1 的功能鉴定

大车前（Plantago major L. "Giant Turkish."）不仅有很高的药用价值，在生态学研究方面也是重要模式植物。大车前的组织培养工作，目前报道很少。对其组织培养体系的建立，为筛选大车前耐盐突变体和基因转化建立高效的体外再生系统和实验平台体系。通过愈伤组织诱导和直接不定芽再生途径， 建立了大车前（Plantago major L. "Giant Turkish."）的快速高效再生系统。叶片外植体在含有1.0 mg/L NAA的MS培养基中培养3周后，形成愈伤组织，愈伤组织在含4.0 mg/L 6-BA的MS培养基中成功再生，得到完整植株。种子外植体在含0.2 mg/L IAA和1.0 mg/L TDZ的MS培养基中培养4周后产生大量的丛生芽，对9株再生植株进行RAPD检测表明，部分植株在DNA水平上发生了变异。 植物抵御盐胁迫的一个重要机制是在液泡中积累Na+，从而使细胞质内Na+保持在较低水平，并且降低细胞渗透势。Na+运输到液泡是由液泡Na+/H+逆向转运蛋白完成的。本实验室已从盐生植物盐角草（Salicornia europaea）和番杏（Tetragonia tetragonioides）中分别克隆得到SeNHX1和TtNHX1基因。本文研究了SeNHX1和TtNHX1基因在酵母突变体里的作用。TtNHX1和SeNHX1蛋白在缺陷型酵母菌株里的表达能够提高这些菌株对NaCl、LiCl和潮霉素的抗性，提高到与野生型相当的抗性水平。说明TtNHX1和SeNHX1有着与酵母ScNHX1相似的细胞定位和作用机制，是ScNHX1的功能类似蛋白。

Phone-level pronunciation scoring and assessment for interactive language learning

This paper investigates a method of automatic pronunciation scoring for use in computer-assisted language learning (CALL) systems. The method utilizes a likelihood-based `Goodness of Pronunciation' (GOP) measure which is extended to include individual thresholds for each phone based on both averaged native confidence scores and on rejection statistics provided by human judges. Further improvements are obtained by incorporating models of the subject's native language and by augmenting the recognition networks to include expected pronunciation errors. The various GOP measures are assessed using a specially recorded database of non-native speakers which has been annotated to mark phone-level pronunciation errors. Since pronunciation assessment is highly subjective, a set of four performance measures has been designed, each of them measuring different aspects of how well computer-derived phone-level scores agree with human scores. These performance measures are used to cross-validate the reference annotations and to assess the basic GOP algorithm and its refinements. The experimental results suggest that a likelihood-based pronunciation scoring metric can achieve usable performance, especially after applying the various enhancements.

KLAIR: A virtual infant for spoken language acquisition research

Recent research into the acquisition of spoken language has stressed the importance of learning through embodied linguistic interaction with caregivers rather than through passive observation. However the necessity of interaction makes experimental work into the simulation of infant speech acquisition difficult because of the technical complexity of building real-time embodied systems. In this paper we present KLAIR: a software toolkit for building simulations of spoken language acquisition through interactions with a virtual infant. The main part of KLAIR is a sensori-motor server that supplies a client machine learning application with a virtual infant on screen that can see, hear and speak. By encapsulating the real-time complexities of audio and video processing within a server that will run on a modern PC, we hope that KLAIR will encourage and facilitate more experimental research into spoken language acquisition through interaction. Copyright © 2009 ISCA.

Cross-Lingual spoken language understanding from unaligned data using discriminative classification models and machine translation

This paper investigates several approaches to bootstrapping a new spoken language understanding (SLU) component in a target language given a large dataset of semantically-annotated utterances in some other source language. The aim is to reduce the cost associated with porting a spoken dialogue system from one language to another by minimising the amount of data required in the target language. Since word-level semantic annotations are costly, Semantic Tuple Classifiers (STCs) are used in conjunction with statistical machine translation models both of which are trained from unaligned data to further reduce development time. The paper presents experiments in which a French SLU component in the tourist information domain is bootstrapped from English data. Results show that training STCs on automatically translated data produced the best performance for predicting the utterance's dialogue act type, however individual slot/value pairs are best predicted by training STCs on the source language and using them to decode translated utterances. © 2010 ISCA.