576 research outputs found
Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data
It is well known that recognizers personalized to each user are much more
effective than user-independent recognizers. With the popularity of smartphones
today, although it is not difficult to collect a large set of audio data for
each user, it is difficult to transcribe it. However, it is now possible to
automatically discover acoustic tokens from unlabeled personal data in an
unsupervised way. We therefore propose a multi-task deep learning framework
called a phoneme-token deep neural network (PTDNN), jointly trained from
unsupervised acoustic tokens discovered from unlabeled data and very limited
transcribed data for personalized acoustic modeling. We term this scenario
"weakly supervised". The underlying intuition is that the high degree of
similarity between the HMM states of acoustic token models and phoneme models
may help them learn from each other in this multi-task learning framework.
Initial experiments performed over a personalized audio data set recorded from
Facebook posts demonstrated that very good improvements can be achieved in both
frame accuracy and word accuracy over popularly-considered baselines such as
fDLR, speaker code and lightly supervised adaptation. This approach complements
existing speaker adaptation approaches and can be used jointly with such
techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201
End-to-End Open Vocabulary Keyword Search With Multilingual Neural Representations
Conventional keyword search systems operate on automatic speech recognition
(ASR) outputs, which causes them to have a complex indexing and search
pipeline. This has led to interest in ASR-free approaches to simplify the
search procedure. We recently proposed a neural ASR-free keyword search model
which achieves competitive performance while maintaining an efficient and
simplified pipeline, where queries and documents are encoded with a pair of
recurrent neural network encoders and the encodings are combined with a
dot-product. In this article, we extend this work with multilingual pretraining
and detailed analysis of the model. Our experiments show that the proposed
multilingual training significantly improves the model performance and that
despite not matching a strong ASR-based conventional keyword search system for
short queries and queries comprising in-vocabulary words, the proposed model
outperforms the ASR-based system for long queries and queries that do not
appear in the training data.Comment: Accepted by IEEE/ACM Transactions on Audio, Speech and Language
Processing (TASLP), 202
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