A fault-tolerant one-way quantum computer

We describe a fault-tolerant one-way quantum computer on cluster states in three dimensions. The presented scheme uses methods of topological error correction resulting from a link between cluster states and surface codes. The error threshold is 1.4% for local depolarizing error and 0.11% for each source in an error model with preparation-, gate-, storage- and measurement errors.Comment: 26 page

A deep learning approach to assessing non-native pronunciation of English using phone distances

The way a non-native speaker pronounces the phones of a language is an important predictor of their proficiency. In grading spontaneous speech, the pairwise distances between generative statistical models trained on each phone have been shown to be powerful features. This paper presents a deep learning alternative to model-based phone distances in the form of a tunable Siamese network feature extractor to extract distance metrics directly from the audio frame sequence. Features are extracted at the phone instance level and combined to phone-level representations using an attention mechanism. Pair-wise distances between phone features are then projected through a feed-forward layer to predict score. The extraction stage is initialised on either a binary phone instance-pair classification task, or to mimic the model-based features, then the whole system is fine-tuned end-to-end, optimising the learning of the distance metric to the score prediction task. This method is therefore more adaptable and more sensitive to phone instance level phenomena. Its performance is compared agains

A deep learning approach to automatic characterisation of rhythm in non-native English speech

A speaker's rhythm contributes to the intelligibility of their speech and can be characteristic of their language and accent. For non-native learners of a language, the extent to which they match its natural rhythm is an important predictor of their proficiency. As a learner improves, their rhythm is expected to become less similar to their L1 and more to the L2. Metrics based on the variability of the durations of vocalic and consonantal intervals have been shown to be effective at detecting language and accent. In this paper, pairwise variability (PVI, CCI) and variance (varcoV, varcoC) metrics are first used to predict proficiency and L1 of non-native speakers taking an English spoken exam. A deep learning alternative to generalise these features is then presented, in the form of a tunable duration embedding, based on attention over an RNN over durations. The RNN allows relationships beyond pairwise to be captured, while attention allows sensitivity to the different relative importance of durations. The system is trained end-to-end for proficiency and L1 prediction and compared to the baseline. The values of both sets of features for different proficiency levels are then visualised and compared to native speech in the L1 and the L2.ALTA Institut

Complementary systems for Off-Topic spoken response detection

Increased demand to learn English for business and education has led to growing interest in automatic spoken language assessment and teaching systems. With this shift to automated approaches it is important that systems reliably assess all aspects of a candidate's responses. This paper examines one form of spoken language assessment; whether the response from the candidate is relevant to the prompt provided. This will be referred to as off-topic spoken response detection. Two forms of previously proposed approaches are examined in this work: the hierarchical attention-based topic model (HATM); and the similarity grid model (SGM). The work focuses on the scenario when the prompt, and associated responses, have not been seen in the training data, enabling the system to be applied to new test scripts without the need to collect data or retrain the model. To improve the performance of the systems for unseen prompts, data augmentation based on easy data augmentation (EDA) and translation based approaches are applied. Additionally for the HATM, a form of prompt dropout is described. The systems were evaluated on both seen and unseen prompts from Linguaskill Business and General English tests. For unseen data the performance of the HATM was improved using data augmentation, in contrast to the SGM where no gains were obtained. The two approaches were found to be complementary to one another, yielding a combined F(0.5) score of 0.814 for off-topic response detection where the prompts have not been seen in training.ALT

A hierarchical attention based model for off-topic spontaneous spoken response detection

Automatic spoken language assessment and training systems are becoming increasingly popular to handle the growing demand to learn languages. However, current systems often assess only fluency and pronunciation, with limited content-based features being used. This paper examines one particular aspect of content-assessment, off-topic response detection. This is important for deployed systems as it ensures that candidates understood the prompt, and are able to generate an appropriate answer. Previously proposed approaches typically require a set of prompt-response training pairs, which lim- its flexibility as example responses are required whenever a new test prompt is introduced. Recently, the attention based neural topic model (ATM) was presented, which can assess the relevance of prompt-response pairs regardless of whether the prompt was seen in training. This model uses a bidirectional Recurrent Neural Network (BiRNN) embedding of the prompt combined with an attention mechanism to attend over the hidden states of a BiRNN embedding of the response to compute a fixed-length embedding used to predict relevance. Unfortunately, performance on prompts not seen in the training data is lower than on seen prompts. Thus, this paper adds the following contributions: several im- provements to the ATM are examined; a hierarchical variant of the ATM (HATM) is proposed, which explicitly uses prompt similarity to further improve performance on unseen prompts by interpolating over prompts seen in training data given a prompt of interest via a second attention mechanism; an in-depth analysis of both models is conducted and main failure mode identified. On spontaneous spo- ken data, taken from BULATS tests, these systems are able to assess relevance to both seen and unseen prompt

Automatic detection of accent and lexical pronunciation errors in spontaneous non-native English speech

Detecting individual pronunciation errors and diagnosing pronunciation error tendencies in a language learner based on their speech are important components of computer-aided language learning (CALL). The tasks of error detection and error tendency diagnosis become particularly challenging when the speech in question is spontaneous and particularly given the challenges posed by the inconsistency of human annotation of pronunciation errors. This paper presents an approach to these tasks by distinguishing between lexical errors, wherein the speaker does not know how a particular word is pronounced, and accent errors, wherein the candidate's speech exhibits consistent patterns of phone substitution, deletion and insertion. Three annotated corpora of non-native English speech by speakers of multiple L1s are analysed, the consistency of human annotation investigated and a method presented for detecting individual accent and lexical errors and diagnosing accent error tendencies at the speaker level

Universal adversarial attacks on spoken language assessment systems

There is an increasing demand for automated spoken language assessment (SLA) systems, partly driven by the performance improvements that have come from deep learning based approaches. One aspect of deep learning systems is that they do not require expert derived features, operating directly on the original signal such as a speech recognition (ASR) transcript. This, however, increases their potential susceptibility to adversarial attacks as a form of candidate malpractice. In this paper the sensitivity of SLA systems to a universal black-box attack on the ASR text output is explored. The aim is to obtain a single, universal phrase to maximally increase any candidate's score. Four approaches to detect such adversarial attacks are also described. All the systems, and associated detection approaches, are evaluated on a free (spontaneous) speaking section from a Business English test. It is shown that on deep learning based SLA systems the average candidate score can be increased by almost one grade level using a single six word phrase appended to the end of the response hypothesis. Although these large gains can be obtained, they can be easily detected based on detection shifts from the scores of a “traditional” Gaussian Process based grader

LpL^p-generic cocycles have one-point Lyapunov spectrum

We show the sum of the first $k$ Lyapunov exponents of linear cocycles is an upper semicontinuous function in the $L^p$ topologies, for any $1 \le p \le \infty$ and $k$. This fact, together with a result from Arnold and Cong, implies that the Lyapunov exponents of the $L^p$-generic cocycle, $p<\infty$, are all equal.Comment: 8 pages. A gap in the previous version was correcte

Use of graphemic lexicons for spoken language assessment

Copyright © 2017 ISCA. Automatic systems for practice and exams are essential to support the growing worldwide demand for learning English as an additional language. Assessment of spontaneous spoken English is, however, currently limited in scope due to the difficulty of achieving sufficient automatic speech recognition (ASR) accuracy. "Off-the-shelf" English ASR systems cannot model the exceptionally wide variety of accents, pronunications and recording conditions found in non-native learner data. Limited training data for different first languages (L1s), across all proficiency levels, often with (at most) crowd-sourced transcriptions, limits the performance of ASR systems trained on non-native English learner speech. This paper investigates whether the effect of one source of error in the system, lexical modelling, can be mitigated by using graphemic lexicons in place of phonetic lexicons based on native speaker pronunications. Graphemicbased English ASR is typically worse than phonetic-based due to the irregularity of English spelling-to-pronunciation but here lower word error rates are consistently observed with the graphemic ASR. The effect of using graphemes on automatic assessment is assessed on different grader feature sets: audio and fluency derived features, including some phonetic level features; and phone/grapheme distance features which capture a measure of pronunciation ability