We present a language independent, unsupervised method for building word embeddings using morphological expansion of text. Our model handles the problem of data sparsity and yields improved word embeddings by relying on training word embeddings on artificially generated sentences. We evaluate our method using small sized training sets on eleven test sets for the word similarity task across seve...

acoustic signal which simply represents the sequence of the actually pronounced phones. This might in turn reduce recognition accuracy. One solution to this problem is to use "phonological rules" ([2], for both French and English, and [3]) which account for various pronunciation variations in the language. However, it is hard to obtain reliable sets of such rules. Because of the compactness of ...

In this work we study the set of leader codewords of a non-binary linear code. This set has some nice properties related to the monotonicity of the weight compatible order on the generalized support of a vector in Fnq . This allows us to describe a test set, a trial set and the set zero neighbours in terms of the leader codewords.

In this study, a new modeling method based on trapezoidal fuzzy number FAHP has been proposed to solve the problem of risk element transmission in enterprise project evaluation chain. We firstly put forward enterprise project chain risk element transmission and constructs enterprise project chain risk element transmission tetrahedron model and then proposed the theory of enterprise project eval...

Selecting appropriate words to compose a sentence is one common problem faced by non-native Chinese learners. In this paper, we propose (bidirectional) LSTM sequence labeling models and explore various features to detect word usage errors in Chinese sentences. By combining CWINDOW word embedding features and POS information, the best bidirectional LSTM model achieves accuracy 0.5138 and MRR 0.6...

This paper describes a maxent-based preposition sense disambiguation system entry to the preposition sense disambiguation task of the SemEval 2007. This system uses a wide variety of semantic and syntactic features to perform the disambiguation task and achieves a precision of 69.3% over the test data.

This paper summarises the contributions of the teams at the University of Helsinki, Uppsala University and the University of Turku to the news translation tasks for translating from and to Finnish. Our models address the problem of treating morphology and data coverage in various ways. We introduce a new efficient tool for word alignment and discuss factorisations, gappy language models and rei...

This paper examines the recognition of non-native speech in jupiter, a speaker-independent, spontaneous-speech conversational system. Because the non-native speech in this domain is limited and varied, speakerand accent-specific methods are impractical. We therefore chose to model all of the non-native data with a single model. In particular, this paper describes an attempt to better model non-...

Monosyllabic word recognition was studied in 140 subjects between the ages of 20 and 90 years. The subjects were tested under a condition of fixed audibility that was achieved by presenting bandpass-filtered Northwestern University Auditory Test No. 6 (NU-6) word lists at a constant signal-to-noise ratio and limiting threshold losses at the speech frequencies to 25 dB HL. The results indicated ...

It is indispensable to establish an objective test methodology for noise-reduced speech. This paper proposes a new methodology which estimates word intelligibility of the noise-reduced speech from PESQ MOS (subjective MOS estimated by the PESQ). To evaluate the effectiveness of the proposed methodology, a word intelligibility test of the noise-reduced speech was performed by using four noise re...