A vector space model for automatic indexing
Communications of the ACM
Modelling out-of-vocabulary words for robust speech recognition
Modelling out-of-vocabulary words for robust speech recognition
Extracting paraphrases from a parallel corpus
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Towards the automated social analysis of situated speech data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Augmenting mobile 3G using WiFi
Proceedings of the 8th international conference on Mobile systems, applications, and services
Language modeling for limited-data domains
Language modeling for limited-data domains
VoiceYourView: collecting real-time feedback on the design of public spaces
Proceedings of the 12th ACM international conference on Ubiquitous computing
Speech@home: an exploratory study
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Collecting highly parallel data for paraphrase evaluation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
SiFi: exploiting VoIP silence for WiFi energy savings insmart phones
Proceedings of the 13th international conference on Ubiquitous computing
Crowds in two seconds: enabling realtime crowd-powered interfaces
Proceedings of the 24th annual ACM symposium on User interface software and technology
Comparing Stochastic Approaches to Spoken Language Understanding in Multiple Languages
IEEE Transactions on Audio, Speech, and Language Processing
Empowering developers to estimate app energy consumption
Proceedings of the 18th annual international conference on Mobile computing and networking
BodyScope: a wearable acoustic sensor for activity recognition
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
StressSense: detecting stress in unconstrained acoustic environments using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
| Hi-index | 0.00 |
This paper presents the design and implementation of a programming system that enables third-party developers to add spoken natural language (SNL) interfaces to standalone mobile applications. The central challenge is to create statistical recognition models that are accurate and resource-efficient in the face of the variety of natural language, while requiring little specialized knowledge from developers. We show that given a few examples from the developer, it is possible to elicit comprehensive sets of paraphrases of the examples using internet crowds. The exhaustive nature of these paraphrases allows us to use relatively simple, automatically derived statistical models for speech and language understanding that perform well without per-application tuning. We have realized our design fully as an extension to the Visual Studio IDE. Based on a new benchmark dataset with 3500 spoken instances of 27 commands from 20 subjects and a small developer study, we establish the promise of our approach and the impact of various design choices.