SAMVAAD: speech applications made viable for access-anywhere devices

Nitendra Rajput, Amit A. Nanavati, Mohit Kumar, Pankaj Kankar, Ravinder Dahiya · 2006

The proliferation of pervasive devices has stimulated the development of applications that support ubiquitous access via multiple modalities. Since the processing capabilities of pervasive devices differ vastly, device-specific application adaptation becomes essential. We address the problem of speech application adaptation by dialog call-flow reorganisation for pervasive devices with different memory constraints. Given an atomic dialog call-flow A and device memory size m, we present optimal deterministic algorithms, RESEQUENCE and BALANCE-TREE, which minimise the number of questions in the reorganised output call-flow A/sub m/. Algorithms MASQ and MATREE produce C/sub m/, minimally distant from input call-flow A/sub m/ while accommodating the memory constraint m. These two minimisation criteria are capable of capturing various usability requirements important in dialog call-flow design. The following observation forms the cornerstone of all the algorithms in this paper: Two grammars g/sub 1/ and g/sub 2/ comprising of |g/sub 1/| and |g/sub 2/| elements respectively can be merged into a single grammar g = g/sub 1/ /spl times/ g/sub 2/ having |g/sub 1/|/spl middot/|g/sub 2/| elements for the sequential case, and g = g/sub 1/ + g/sub 2/ having |g/sub 1/|+|g/sub 2/| elements for the tree case. Device-speciific considerations lead us to introduce the concept of an-characterisation of a call-flow, defined as the set of pairs {(m/sub i/,q/sub i/)| /spl isin/ N}, where q/sub i/ is the minimum number of questions required for memory size m/sub i/. Each call-flow has a unique, device-independent signature in its-characterisation - a measure of its adaptability. We present SAMVAAD, a system that implements these algorithms on call-flows authored in VXML containing SRGS grammars. The system was tested on an IBM voice browser using a sample airline reservation system call-flow reorganised for memories ranging from 64 MB to 210 KB. We ran an experiment with 14 users to obtain feedback on the usability of the adapted call-flows.

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