Checking in with LDC Data Scholarship Recipients
The LDC Data Scholarship program provides college and university students with access to LDC data at no-cost. Students are asked to complete an application which consists of a proposal describing their intended use of the data, as well as a letter of support from their thesis adviser. LDC introduced the Data Scholarship program during the Fall 2010 semester. Since that time, more than thirty individual students and student research groups have been awarded no-cost copies of LDC data for their research endeavors. Here is an update on the work of a few of the student recipients:
Leili Javadpour - Louisiana State University (USA), Engineering Science. Leili was awarded a copy of BBN Pronoun Coreference and Entity Type Corpus (LDC2005T33) and Message Understanding Conference (MUC) 7 (LDC2001T02) for her work in pronominal anaphora resolution. Leili's research involves a learning approach for pronominal anaphora resolution in unstructured text. She evaluated her approach on the BBN Pronoun Coreference and Entity Type Corpus and obtained encouraging results of 89%. In this approach machine learning is applied to a set of new features selected from other computational linguistic research. Leili's future plans involve evaluating the approach on Message Understanding Conference (MUC) 7 as well as on other genres of annotated text such as stories and conversation transcripts.
Olga Nickolaevna Ladoshko - National Technical University of Ukraine “KPI” (Ukraine), graduate student, Acoustics and Acoustoelectronics. Olga was awarded copies of NTIMT (LDC93S2) and STC-TIMIT 1.0 (LDC2008S03) for her research in automatic speech recognition for Ukrainian. Olga used NTIMIT in the first phase of her research; one problem she investigated was the influence of telephone communication channels on the reliability of phoneme recognition in different types of parametrization and configuration speech recognition systems on the basis of HTK tools. The second phase involves using NTIMIT to test the algorithm for determining voice in non-stationary noise. Her future work with STC-TIMIT 1.0 will include an experiment to develop an improved speech recognition algorithm, allowing for increased accuracy under noisy conditions.
Genevieve Sapijaszko - University of Central Florida (USA), Phd Candidate, Electrical and Computer Engineering. Genevieve was awarded a copy TIMIT Acoustic-Phonetic Continuous Speech Corpus (LDC93S1) and YOHO Speaker Verification (LDC94S16) for her work in digital signal processing. Her experiment used VQ and Euclidean distance to recognize a speaker's identity through extracting the features of the speech signal by the following methods: RCC, MFCC, MFCC + ΔMFCC, LPC, LPCC, PLPCC and RASTA PLPCC. Based on the results, in a noise free environment MFCC, (at an average of 94%), is the best feature extraction method when used in conjunction with the VQ model. The addition of the ΔMFCC showed no significant improvement to the recognition rate. When comparing three phrases of differing length, the longer two phrases had very similar recognition rates but the shorter phrase at 0.5 seconds had a noticeable lower recognition rate across methods. When comparing recognition time, MFCC was also faster than other methods. Genevieve and her research team concluded that MFCC in a noise free environment was the best method in terms of recognition rate and recognition rate time.
John Steinberg - Temple University (USA), MS candidate, Electrical and Computer Engineering. John was awarded a copy of CALLHOME Mandarin Chinese Lexicon (LDC96L15) and CALLHOME Mandarin Chinese Transcripts (LDC96T16) for his work in speech recognition. John used the CALLHOME Mandarin Lexicon and Transcripts to investigate the integration of Bayesian nonparametric techniques into speech recognition systems. These techniques are able to detect the underlying structure of the data and theoretically generate better acoustic models than typical parametric approaches such as HMM. His work investigated using one such model, Dirichlet process mixtures, in conjunction with three variational Bayesian inference algorithms for acoustic modeling. The scope of his work was limited to a phoneme classification problem since John's goal was to determine the viability of these algorithms for acoustic modeling. One goal of his research group is to develop a speech recognition system that is robust to variations in the acoustic channel. The group is also interested in building acoustic models that generalize well across languages. For these reasons, both CALLHOME English and CALLHOME Mandarin data were used to help determine if these new Bayesian nonparametric models were prone to any language specific artifacts. These two languages, though phonetically very different, did not yield significantly different performances. Furthermore, one variational inference algorithm- accelerated variational Dirichlet process mixtures (AVDPM) - was found to perform well on extremely large data sets.