Technology Pipeline for Large Scale Cross-Lingual Dubbing of Lecture Videos into Multiple Indian Languages

Cross-lingual dubbing of lecture videos requires the transcription of the original audio, correction and removal of disfluencies, domain term discovery, text-to-text translation into the target language, chunking of text using target language rhythm, text-to-speech synthesis followed by isochronous lipsyncing to the original video. This task becomes challenging when the source and target languages belong to different language families, resulting in differences in generated audio duration. This is further compounded by the original speaker's rhythm, especially for extempore speech. This paper describes the challenges in regenerating English lecture videos in Indian languages semi-automatically. A prototype is developed for dubbing lectures into 9 Indian languages. A mean-opinion-score (MOS) is obtained for two languages, Hindi and Tamil, on two different courses. The output video is compared with the original video in terms of MOS (1-5) and lip synchronisation with scores of 4.09 and 3.74, respectively. The human effort also reduces by 75%

On Solving the System

Mathematically a system is said to be solved if its future states can be predicted from the information provided by the present and past state history. In this paper we present a way of solving artificial life systems using the principles of state-machines. We present the view of manipulating the artificial systems considering them as being embedded in external program entities. Further, we discuss the technique of using algorithmic transformations to understand the behavioral complexity of virtual organisms. Finally, we relate the complexity of virtual systems with the algorithmic complexity and establish that open-ended evolution requires programs with ever increasing algorithmic complexity

Is Question Answering an Acquired Skill?

We present a question answering (QA) system which learns how to detect and rank answer passages by analyzing questions and their answers (QA pairs) provided as training data. We built our system in only a few person-months using o#- the-shelf components: a part-of-speech tagger, a shallow parser, a lexical network, and a few well-known supervised learning algorithms. In contrast, many of the top TREC QA systems are large group efforts, using customized ontologies, question classifiers, and highly tuned ranking functions. Our ease of deployment arises from using generic, trainable algorithms that exploit simple feature extractors on QA pairs. With TREC QA data, our system achieves mean reciprocal rank (MRR) that compares favorably with the best scores in recent years, and generalizes from one corpus to another. Our key technique is to recover, from the question, fragments of what might have been posed as a structured query, had a suitable schema been available. One fragment comprises selectors: tokens that are likely to appear (almost) unchanged in an answer passage. The other fragment contains question tokens which give clues about the answer type, and are expected to be replaced in the answer passage by tokens which specialize or instantiate the desired answer type. Selectors are like constants in where-clauses in relational queries, and answer types are like column names. We present new algorithms for locating selectors and answer type clues and using them in scoring passages with respect to a question

Natural Language Generation from Semantic Net like Structures with application to Hindi

In this paper we discuss a Natural Language Generator System from Universal Networking Language (UNL)- which is a semantic net like knowledge representation scheme- to Hindi for assertive sentences. UNL is a interlingua proposed by United Nations University, Tokyo, Japan for transfer and exchange of information over the internet. The UNL representation of the sentence is converted into a structure called nodenet. The nodenet is traversed by resolving the relation labels to re ect the syntactic structure of the Hindi equivalent of the UNL representation. By performing the Morphology on the intermediate sentence, desired target Language sentence can be generated. The approach is a general one which can be applied to the SOV class of sentences and is therfore of capable of handling a large number of Indian Languages.