1,737 research outputs found
Using Resources from a Closely-related Language to Develop ASR for a Very Under-resourced Language: A Case Study for Iban
International audienceThis paper presents our strategies for developing an automatic speech recognition system for Iban, an under-resourced language. We faced several challenges such as no pronunciation dictionary and lack of training material for building acoustic models. To overcome these problems, we proposed approaches which exploit resources from a closely-related language (Malay). We developed a semi-supervised method for building the pronunciation dictionary and applied cross-lingual strategies for improving acoustic models trained with very limited training data. Both approaches displayed very encouraging results, which show that data from a closely-related language, if available, can be exploited to build ASR for a new language. In the final part of the paper, we present a zero-shot ASR using Malay resources that can be used as an alternative method for transcribing Iban speech
Using Resources from a Closely-related Language to Develop ASR for a Very Under-resourced Language: A Case Study for Iban
This paper presents our strategies for developing an automatic speech recognition system for Iban, an under-resourced language. We faced several challenges such as no pronunciation dictionary and lack of training material for building acoustic models. To overcome these problems, we proposed approaches which exploit resources from a closely-related language (Malay). We developed a semi-supervised method for building the pronunciation dictionary and applied cross-lingual strategies for improving acoustic models trained with very limited training data. Both approaches displayed very encouraging results, which show that data from a closely-related language, if available, can be exploited to build ASR for a new language. In the final part of the paper, we present a zero-shot ASR using Malay resources that can be used as an alternative method for transcribing Iban speech
AutoLV: Automatic Lecture Video Generator
We propose an end-to-end lecture video generation system that can generate
realistic and complete lecture videos directly from annotated slides,
instructor's reference voice and instructor's reference portrait video. Our
system is primarily composed of a speech synthesis module with few-shot speaker
adaptation and an adversarial learning-based talking-head generation module. It
is capable of not only reducing instructors' workload but also changing the
language and accent which can help the students follow the lecture more easily
and enable a wider dissemination of lecture contents. Our experimental results
show that the proposed model outperforms other current approaches in terms of
authenticity, naturalness and accuracy. Here is a video demonstration of how
our system works, and the outcomes of the evaluation and comparison:
https://youtu.be/cY6TYkI0cog.Comment: 4 pages, 4 figures, ICIP 202
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