NLU Framework for Voice Enabling Non-Native Applications on Smart Devices
Authors: Soujanya Lanka, Deepika Pathania, Pooja Kushalappa, Pradeep Varakantham
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To aid this demonstration, we have implemented the framework as a service in Android OS. When the user issues a voice command, the natural language query is obtained by this service (using one of local, cloud based or hybrid speech recognizers). The service then executes our NLU framework to identify the relevant application and particular action details. In this demonstration, we will showcase this NLU framework implemented as an Android service on a set of applications that will be installed on the fly. Specifically, we will show how the voice queries are understood and necessary services are launched on android smart wearables and phones. |
| Researcher Affiliation | Industry | Soujanya Lanka, Deepika Pathania, Pooja Kushalappa, Pradeep Varakantham i.am+ LLC. 809 North Cahunga Blvd, Los Angeles 90036 {soujanya, deepika.pathania, pooja, pradeep}@iamplus.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Figure 1 shows a diagram of the NLU framework workflow. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions a "set of applications that will be installed on the fly" and "grammar files" created by the application developer, implying custom or internal data. It does not provide concrete access information (link, DOI, repository, or formal citation) for any publicly available or open dataset used for training. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) needed to reproduce data partitioning for training, validation, or testing. |
| Hardware Specification | No | The paper mentions "smart devices (wearables, phones, speakers, televisions)" and "Android smart wearables and phones" as platforms where the framework can be employed or demonstrated, but it does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments or development. |
| Software Dependencies | No | The paper mentions "Android OS", "CMU SPHINX 4", "JSGF grammar (Java Speech Grammar)", and "Aneed A", but it does not provide specific version numbers for any of these or other key software components that would be needed to replicate the experiment environment. |
| Experiment Setup | No | The paper describes the NLU framework and its workflow but does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |