We propose strategies for a state-of-the-art keyword search (KWS) system developed by the SINGA team in the context of the 2014 NIST Open Keyword Search Evaluation (OpenKWS14) using conversational Tamil provided by the IARPA Babel program. To tackle low-resource challenges and the rich morphological nature of Tamil, we present highlights of our current KWS system, including: (1) Submodular optimization data selection to maximize acoustic diversity through Gaussian component indexed N-grams; (2) Keywordaware language modeling; (3) Subword modeling of morphemes and homophones.

The artificial intelligence renaissance: deep learning and the road to human-Level machine intelligence kar-han tan 1 and boon pang lim 2In this paper we look at recent advances in artificial intelligence. Decades in the making, a confluence of several factors in the past few years has culminated in a string of breakthroughs in many longstanding research challenges. A number of problems that were considered too challenging just a few years ago can now be solved convincingly by deep neural networks. Although deep learning appears to be reducing the algorithmic problem solving to a matter of data collection and labeling, we believe that many insights learned from 'pre-Deep Learning' works still apply and will be more valuable than ever in guiding the design of novel neural network architectures.

We propose strategies for a state-of-the-art Vietnamese keyword search (KWS) system developed at the Institute for Infocomm Research (I 2 R). The KWS system exploits acoustic features characterizing creaky voice quality peculiar to lexical tones in Vietnamese, a minimal-resource transliteration framework to alleviate out-ofvocabulary issues from foreign loan words, and a proposed system combination scheme FusionX. We show that the proposed creaky voice quality features complement pitch-related features, reaching fusion gains of 17.7% relative (6.9% absolute). To the best of our knowledge, the proposed transliteration framework is the first reported rule-based system for Vietnamese; it outperforms statisticalapproach baselines up to 14.93 -36.73% relative on foreign loan word search tasks. Using FusionX to combine 3 sub-systems, the actual term-weighted value (ATWV) reaches 0.4742, exceeding the ATWV=0.3 benchmark for IARPA Babel participants in the NIST OpenKWS13 Evaluation.

Many keyword search (KWS) systems make "hit/false alarm (FA)" decisions based on the lattice-based posterior probability, which is incomparable across keywords. Therefore, score normalization is essential for a KWS system. In this paper, we investigate the integration of two novel features, ranking-score and relative-to-max, into a discriminative score normalization method. These features are extracted by considering all competing hypotheses of a putative detection. A metric-based normalization method is also applied as a post-processing step to further optimize the term-weighted value (TWV) evaluation metric. We report empirical improvements over standard baselines using the Vietnamese data from IARPA's Babel program in the NIST OpenKWS13 Evaluation setup.