Udhyakumar Nallasamy

Named entity transliteration for cross-language information retrieval using compressed word format mapping algorithm

Transliteration of named entities in user queries is a vital step in any Cross-Language Information Retrieval (CLIR) system. Several methods for transliteration have been proposed till date based on the nature of the languages considered. In this paper, we present a transliteration algorithm for mapping English named entities to their proper Tamil equivalents. Our algorithm employs a grapheme-based model, in which transliteration equivalents are identified by mapping the source language names to their equivalents in a target language database, instead of generating them. The basic principle is to compress the source word into its minimal form and align it across an indexed list of target language words to arrive at the top n-equivalents based on the edit distance. We compare the performance of our approach with a statistical generation approach using Microsoft Research India (MSRI) transliteration corpus. Our approach has proved very effective in terms of accuracy and time.

Neighbour selection and adaptation for rapid speaker-dependent ASR

Speaker dependent (SD) ASR systems have significantly lower word error rates (WER) compared to speaker independent (SI) systems. However, SD systems require sufficient training data from the target speaker, which is impractical to collect in a short time. We present a technique for training SD models using just few minutes of speakers data. We compensate for the lack of adequate speaker-specific data by selecting neighbours from a database of existing speakers who are acoustically close to the target speaker. These neighbours provide ample training data, which is used to adapt the SI model to obtain an initial SD model for the new speaker with significantly lower WER. We evaluate various neighbour selection algorithms on a large-scale medical transcription task and report significant reduction in WER using only 5 mins of speaker-specific data. We conduct a detailed analysis of various factors such as gender and accent in the neighbour selection. Finally, we study neighbour selection and adaptation in the context of discriminative objective functions.