Bin Ma

Spoken language recognition refers to the automatic process through which we determine or verify the identity of the language spoken in a speech sample. We study a computational framework that allows such a decision to be made in a quantitative manner. In recent decades, we have made tremendous progress in spoken language recognition, which benefited from technological breakthroughs in related areas, such as signal processing, pattern recognition, cognitive science, and machine learning. In this paper, we attempt to provide an introductory tutorial on the fundamentals of the theory and the state-of-the-art solutions, from both phonological and computational aspects. We also give a comprehensive review of current trends and future research directions using the language recognition evaluation (LRE) formulated by the National Institute of Standards and Technology (NIST) as the case studies.

Extending the single optimized spaced seed of PatternHunter to multiple ones, PatternHunter II simultaneously remedies the lack of sensitivity of Blastn and the lack of speed of Smith-Waterman, for homology search. At Blastn speed, PatternHunter II approaches Smith-Waterman sensitivity, bringing homology search technology back to a full circle.

The RSR2015 database, designed to evaluate text-dependent speaker verification systems under different durations and lexical constraints has been collected and released by the Human Language Technology (HLT) department at Institute for Infocomm Research (I2R) in Singapore. English speakers were recorded with a balanced diversity of accents commonly found in Singapore. More than 151 h of speech data were recorded using mobile devices. The pool of speakers consists of 300 participants (143 female and 157 male speakers) between 17 and 42 years old making the RSR2015 database one of the largest publicly available database targeted for text-dependent speaker verification. We provide evaluation protocol for each of the three parts of the database, together with the results of two speaker verification system: the HiLAM system, based on a three layer acoustic architecture, and an i-vector/PLDA system. We thus provide a reference evaluation scheme and a reference performance on RSR2015 database to the research community. The HiLAM outperforms the state-of-the-art i-vector system in most of the scenarios.

The problem of finding a center string that is "close" to every given string arises in computational molecular biology and coding theory. This problem has two versions: the Closest String problem and the Closest Substring problem. Given a set of strings S = { s 1 , s 2 , ..., s n }, each of length m , the Closest String problem is to find the smallest d and a string s of length m which is within Hamming distance d to each s i ε S . This problem comes from coding theory when we are looking for a code not too far away from a given set of codes. Closest Substring problem, with an additional input integer L , asks for the smallest d and a string s , of length L , which is within Hamming distance d away from a substring, of length L , of each si. This problem is much more elusive than the Closest String problem. The Closest Substring problem is formulated from applications in finding conserved regions, identifying genetic drug targets and generating genetic probes in molecular biology. Whether there are efficient approximation algorithms for both problems are major open questions in this area. We present two polynomial-time approximation algorithms with approximation ratio 1 + ε for any small ε to settle both questions.