1,566 research outputs found
How speaker tongue and name source language affect the automatic recognition of spoken names
In this paper the automatic recognition of person names and geographical names uttered by native and non-native speakers is examined in an experimental set-up. The major aim was to raise our understanding of how well and under which circumstances previously proposed methods of multilingual pronunciation modeling and multilingual acoustic modeling contribute to a better name recognition in a cross-lingual context. To come to a meaningful interpretation of results we have categorized each language according to the amount of exposure a native speaker is expected to have had to this language. After having interpreted our results we have also tried to find an answer to the question of how much further improvement one might be able to attain with a more advanced pronunciation modeling technique which we plan to develop
Combined Acoustic and Pronunciation Modelling for Non-Native Speech Recognition
In this paper, we present several adaptation methods for non-native speech
recognition. We have tested pronunciation modelling, MLLR and MAP non-native
pronunciation adaptation and HMM models retraining on the HIWIRE foreign
accented English speech database. The ``phonetic confusion'' scheme we have
developed consists in associating to each spoken phone several sequences of
confused phones. In our experiments, we have used different combinations of
acoustic models representing the canonical and the foreign pronunciations:
spoken and native models, models adapted to the non-native accent with MAP and
MLLR. The joint use of pronunciation modelling and acoustic adaptation led to
further improvements in recognition accuracy. The best combination of the above
mentioned techniques resulted in a relative word error reduction ranging from
46% to 71%
Acoustic Modelling for Under-Resourced Languages
Automatic speech recognition systems have so far been developed only for very few languages out of the 4,000-7,000 existing ones.
In this thesis we examine methods to rapidly create acoustic models in new, possibly under-resourced languages, in a time and cost effective manner. For this we examine the use of multilingual models, the application of articulatory features across languages, and the automatic discovery of word-like units in unwritten languages
Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information
This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech
Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition
With the rapid development of speech assistants, adapting server-intended
automatic speech recognition (ASR) solutions to a direct device has become
crucial. Researchers and industry prefer to use end-to-end ASR systems for
on-device speech recognition tasks. This is because end-to-end systems can be
made resource-efficient while maintaining a higher quality compared to hybrid
systems. However, building end-to-end models requires a significant amount of
speech data. Another challenging task associated with speech assistants is
personalization, which mainly lies in handling out-of-vocabulary (OOV) words.
In this work, we consider building an effective end-to-end ASR system in
low-resource setups with a high OOV rate, embodied in Babel Turkish and Babel
Georgian tasks. To address the aforementioned problems, we propose a method of
dynamic acoustic unit augmentation based on the BPE-dropout technique. It
non-deterministically tokenizes utterances to extend the token's contexts and
to regularize their distribution for the model's recognition of unseen words.
It also reduces the need for optimal subword vocabulary size search. The
technique provides a steady improvement in regular and personalized
(OOV-oriented) speech recognition tasks (at least 6% relative WER and 25%
relative F-score) at no additional computational cost. Owing to the use of
BPE-dropout, our monolingual Turkish Conformer established a competitive result
with 22.2% character error rate (CER) and 38.9% word error rate (WER), which is
close to the best published multilingual system.Comment: 16 pages, 7 figure
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