103 research outputs found
Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech
Polyphone disambiguation aims to capture accurate pronunciation knowledge
from natural text sequences for reliable Text-to-speech (TTS) systems. However,
previous approaches require substantial annotated training data and additional
efforts from language experts, making it difficult to extend high-quality
neural TTS systems to out-of-domain daily conversations and countless languages
worldwide. This paper tackles the polyphone disambiguation problem from a
concise and novel perspective: we propose Dict-TTS, a semantic-aware generative
text-to-speech model with an online website dictionary (the existing prior
information in the natural language). Specifically, we design a
semantics-to-pronunciation attention (S2PA) module to match the semantic
patterns between the input text sequence and the prior semantics in the
dictionary and obtain the corresponding pronunciations; The S2PA module can be
easily trained with the end-to-end TTS model without any annotated phoneme
labels. Experimental results in three languages show that our model outperforms
several strong baseline models in terms of pronunciation accuracy and improves
the prosody modeling of TTS systems. Further extensive analyses demonstrate
that each design in Dict-TTS is effective. The code is available at
\url{https://github.com/Zain-Jiang/Dict-TTS}.Comment: Accepted by NeurIPS 202
A Review of Accent-Based Automatic Speech Recognition Models for E-Learning Environment
The adoption of electronics learning (e-learning) as a method of disseminating knowledge in the global educational system is growing at a rapid rate, and has created a shift in the knowledge acquisition methods from the conventional classrooms and tutors to the distributed e-learning technique that enables access to various learning resources much more conveniently and flexibly. However, notwithstanding the adaptive advantages of learner-centric contents of e-learning programmes, the distributed e-learning environment has unconsciously adopted few international languages as the languages of communication among the participants despite the various accents (mother language influence) among these participants. Adjusting to and accommodating these various accents has brought about the introduction of accents-based automatic speech recognition into the e-learning to resolve the effects of the accent differences. This paper reviews over 50 research papers to determine the development so far made in the design and implementation of accents-based automatic recognition models for the purpose of e-learning between year 2001 and 2021. The analysis of the review shows that 50% of the models reviewed adopted English language, 46.50% adopted the major Chinese and Indian languages and 3.50% adopted Swedish language as the mode of communication. It is therefore discovered that majority of the ASR models are centred on the European, American and Asian accents, while unconsciously excluding the various accents peculiarities associated with the less technologically resourced continents
Multilingual Adaptation of RNN Based ASR Systems
In this work, we focus on multilingual systems based on recurrent neural
networks (RNNs), trained using the Connectionist Temporal Classification (CTC)
loss function. Using a multilingual set of acoustic units poses difficulties.
To address this issue, we proposed Language Feature Vectors (LFVs) to train
language adaptive multilingual systems. Language adaptation, in contrast to
speaker adaptation, needs to be applied not only on the feature level, but also
to deeper layers of the network. In this work, we therefore extended our
previous approach by introducing a novel technique which we call "modulation".
Based on this method, we modulated the hidden layers of RNNs using LFVs. We
evaluated this approach in both full and low resource conditions, as well as
for grapheme and phone based systems. Lower error rates throughout the
different conditions could be achieved by the use of the modulation.Comment: 5 pages, 1 figure, to appear in 2018 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2018
Non-native children speech recognition through transfer learning
This work deals with non-native children's speech and investigates both
multi-task and transfer learning approaches to adapt a multi-language Deep
Neural Network (DNN) to speakers, specifically children, learning a foreign
language. The application scenario is characterized by young students learning
English and German and reading sentences in these second-languages, as well as
in their mother language. The paper analyzes and discusses techniques for
training effective DNN-based acoustic models starting from children native
speech and performing adaptation with limited non-native audio material. A
multi-lingual model is adopted as baseline, where a common phonetic lexicon,
defined in terms of the units of the International Phonetic Alphabet (IPA), is
shared across the three languages at hand (Italian, German and English); DNN
adaptation methods based on transfer learning are evaluated on significant
non-native evaluation sets. Results show that the resulting non-native models
allow a significant improvement with respect to a mono-lingual system adapted
to speakers of the target language
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model
Multilingual models for Automatic Speech Recognition (ASR) are attractive as
they have been shown to benefit from more training data, and better lend
themselves to adaptation to under-resourced languages. However, initialisation
from monolingual context-dependent models leads to an explosion of
context-dependent states. Connectionist Temporal Classification (CTC) is a
potential solution to this as it performs well with monophone labels.
We investigate multilingual CTC in the context of adaptation and
regularisation techniques that have been shown to be beneficial in more
conventional contexts. The multilingual model is trained to model a universal
International Phonetic Alphabet (IPA)-based phone set using the CTC loss
function. Learning Hidden Unit Contribution (LHUC) is investigated to perform
language adaptive training. In addition, dropout during cross-lingual
adaptation is also studied and tested in order to mitigate the overfitting
problem.
Experiments show that the performance of the universal phoneme-based CTC
system can be improved by applying LHUC and it is extensible to new phonemes
during cross-lingual adaptation. Updating all the parameters shows consistent
improvement on limited data. Applying dropout during adaptation can further
improve the system and achieve competitive performance with Deep Neural Network
/ Hidden Markov Model (DNN/HMM) systems on limited data
Modularity and Neural Integration in Large-Vocabulary Continuous Speech Recognition
This Thesis tackles the problems of modularity in Large-Vocabulary Continuous Speech Recognition with use of Neural Network
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