Daniela Sosa - Academia.edu (original) (raw)
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Papers by Daniela Sosa
Infancias Imágenes, Jun 9, 2017
* Artículo de reflexión. El siguiente trabajo presenta los resultados de una reflexión teórico-li... more * Artículo de reflexión. El siguiente trabajo presenta los resultados de una reflexión teórico-literaria, fue realizado en Ciudad de México en julio del 2015 y culminado en la Universidad Distrital Francisco José de Caldas en marzo del 2016.
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
In this paper, we propose improving automatic speech recognition (ASR) accuracy for local points ... more In this paper, we propose improving automatic speech recognition (ASR) accuracy for local points of interest (POI) by leveraging a geo-specific language model (Geo-LM). Geographic regions are defined according to U.S. Census Bureau Combined Statistical Areas. Depending on the user's associated geographic region, for each user a class based Geo-LM is constructerd dynamically within a difference-LM based weighted finite state transducer (WFST) system. The benefits of this approach include: improved accuracy for local POI name recognition, flexibility in training, and efficient LM construction at runtime. Our experiments show that the proposed Geo-Lm achieves an average of over 18 % relative word error rate (WER) reduction on the tasks of local POI search, with no degradation to the general accuracy and very limited latency increase, compared to the baseline nationwide general LM. In addition to accuracy improvement, we also discuss optimization of runtime efficiency.
Infancias Imágenes, Jun 9, 2017
* Artículo de reflexión. El siguiente trabajo presenta los resultados de una reflexión teórico-li... more * Artículo de reflexión. El siguiente trabajo presenta los resultados de una reflexión teórico-literaria, fue realizado en Ciudad de México en julio del 2015 y culminado en la Universidad Distrital Francisco José de Caldas en marzo del 2016.
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
In this paper, we propose improving automatic speech recognition (ASR) accuracy for local points ... more In this paper, we propose improving automatic speech recognition (ASR) accuracy for local points of interest (POI) by leveraging a geo-specific language model (Geo-LM). Geographic regions are defined according to U.S. Census Bureau Combined Statistical Areas. Depending on the user's associated geographic region, for each user a class based Geo-LM is constructerd dynamically within a difference-LM based weighted finite state transducer (WFST) system. The benefits of this approach include: improved accuracy for local POI name recognition, flexibility in training, and efficient LM construction at runtime. Our experiments show that the proposed Geo-Lm achieves an average of over 18 % relative word error rate (WER) reduction on the tasks of local POI search, with no degradation to the general accuracy and very limited latency increase, compared to the baseline nationwide general LM. In addition to accuracy improvement, we also discuss optimization of runtime efficiency.