10 research outputs found
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
Multi-task Learning for Speaker Verification and Voice Trigger Detection
Automatic speech transcription and speaker recognition are usually treated as
separate tasks even though they are interdependent. In this study, we
investigate training a single network to perform both tasks jointly. We train
the network in a supervised multi-task learning setup, where the speech
transcription branch of the network is trained to minimise a phonetic
connectionist temporal classification (CTC) loss while the speaker recognition
branch of the network is trained to label the input sequence with the correct
label for the speaker. We present a large-scale empirical study where the model
is trained using several thousand hours of labelled training data for each
task. We evaluate the speech transcription branch of the network on a voice
trigger detection task while the speaker recognition branch is evaluated on a
speaker verification task. Results demonstrate that the network is able to
encode both phonetic \emph{and} speaker information in its learnt
representations while yielding accuracies at least as good as the baseline
models for each task, with the same number of parameters as the independent
models
Deep learning methods in speaker recognition: a review
This paper summarizes the applied deep learning practices in the field of
speaker recognition, both verification and identification. Speaker recognition
has been a widely used field topic of speech technology. Many research works
have been carried out and little progress has been achieved in the past 5-6
years. However, as deep learning techniques do advance in most machine learning
fields, the former state-of-the-art methods are getting replaced by them in
speaker recognition too. It seems that DL becomes the now state-of-the-art
solution for both speaker verification and identification. The standard
x-vectors, additional to i-vectors, are used as baseline in most of the novel
works. The increasing amount of gathered data opens up the territory to DL,
where they are the most effective