211 research outputs found
Open-vocabulary keyword spotting in any language through multilingual contrastive speech-phoneme pretraining
In this paper, we introduce a massively multilingual speech corpora with
fine-grained phonemic transcriptions, encompassing more than 115 languages from
diverse language families. Based on this multilingual dataset, we propose
CLAP-IPA, a multilingual phoneme-speech contrastive embedding model capable of
open-vocabulary matching between speech signals and phonemically transcribed
keywords or arbitrary phrases. The proposed model has been tested on two
fieldwork speech corpora in 97 unseen languages, exhibiting strong
generalizability across languages. Comparison with a text-based model shows
that using phonemes as modeling units enables much better crosslinguistic
generalization than orthographic texts.Comment: Preprint; Work in Progres
Hierarchical Neural Network Architecture In Keyword Spotting
Keyword Spotting (KWS) provides the start signal of ASR problem, and thus it
is essential to ensure a high recall rate. However, its real-time property
requires low computation complexity. This contradiction inspires people to find
a suitable model which is small enough to perform well in multi environments.
To deal with this contradiction, we implement the Hierarchical Neural
Network(HNN), which is proved to be effective in many speech recognition
problems. HNN outperforms traditional DNN and CNN even though its model size
and computation complexity are slightly less. Also, its simple topology
structure makes easy to deploy on any device.Comment: To be submitted in part to IEEE ICASSP 201
Role of sentiment classification in sentiment analysis: a survey
Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results
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